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pilotadmin 7eb7f61483 Merge pull request 'feat: company capability profile foundation' (#4) from feat/company-intelligence-2a into main
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2026-06-26 15:13:21 +02:00
Benjamin Admin 8c893ca783 feat(company): Company Intelligence 2A — Company Capability Profile foundation
HEAD of the spine Company->Capability->Product->Regulation->Obligation->Procedure
->Evidence. New compliance/company/ package: CompanyContext container + a four-state
trust model (declared/inferred/confirmed/unknown).

Hard rule (structural): a certification yields at most an INFERRED candidate and is
never auto-treated as CONFIRMED/"erfuellt". A certification produces evidence-of-
capability; only real ExistingEvidence promotes a capability to CONFIRMED.

Ownership: Reasoning owns the container + trust-state; the Certification->Capability
mapping is Execution's domain, consumed via an injected contract. No mapping data in
product code (tests inject mocks). No endpoint/UI/RAG/new regs/controls; no meta-model
classes (freeze v1.0 untouched). 8 tests; mypy --strict clean.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 14:59:42 +02:00
pilotadmin d1383227b2 Merge pull request 'feat: regulatory change intelligence foundation' (#3) from feat/regulatory-change-intelligence into main
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2026-06-26 14:01:48 +02:00
Benjamin Admin a5687bbc65 feat(rci): Regulatory Change Intelligence foundation (delta over the stored map)
RCI/Delta as a read-/reasoning layer ON TOP of the product-first pipeline. Answers
"what changes relative to my existing Regulatory Map?" — NOT "what does the new
law say in general". No UI, no ingestion (newsletter/mailbox), no RAG, no new
regulations/controls, no legal evaluation outside the stored map.

- 4 core objects (compliance/rci/schemas.py): ComplianceBaseline (snapshot of
  profile + map + registry obligations + required/present evidence), RegulatoryChange
  (simulated/provided INPUT), ObligationDelta (delta_type NEW|CHANGED|REMOVED|
  ALREADY_COVERED|NEEDS_REVIEW|NOT_APPLICABLE), ChangeImpactSummary. delta_type is a
  THIRD vocabulary, disjoint from ClaimCoverage (Welt 1) and ComplianceStatus (Welt 2).
- create_baseline() snapshots the existing pipeline once; assess_change() computes
  deltas deterministically against the snapshot (no re-evaluation).
- 12 tests = the 5 acceptance questions (affects product? new/changed? already
  covered by evidence? needs human review? not relevant?) + repeal/uncertain-reg/
  missing-evidence/boundary. Existing pipeline tests stay green; mypy clean; LOC ok.
- App/reasoning types only — no compliance-meta-model classes (freeze v1.0 untouched).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 13:45:23 +02:00
pilotadmin da466b3821 Merge pull request 'feat(ai-sdk): IACE hazard-engine quality + offline proposer (Session 4)' (#2) from feat/iace-gt-warewashing into main
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2026-06-26 11:48:09 +02:00
pilotadmin eca8ec43c5 Merge pull request 'feat(reasoning): product-first regulatory pipeline — Profile → Navigator → Scope → Map → Interpretation' (#1) from feat/regulatory-reasoning-engine into main 2026-06-26 11:47:18 +02:00
Benjamin Admin 37c9b8e773 docs: Domaene-2 Wake-up-Trigger + erster Folgeauftrag Feature Coverage Report
User-Praezisierung: Domaene 2 ruht NICHT unbestimmt. Wake-up-Trigger (EINER reicht):
Feature Graph>=200 Features · Span-Anker verfuegbar · neue Regulierung ingestiert · Runtime
kennt neue Evidence-Typen. Erster Folgeauftrag (gated auf Feature Library v1):
FEATURE COVERAGE REPORT = Wissenslueckenanalyse pro Feature (Feature->cap.*->Obligation->
Procedure->Evidence -> Coverage %; zeigt fehlende Capability/Procedure/Evidence je Feature).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 11:24:06 +02:00
Benjamin Admin 50ae9e94d1 feat(interpretation-in-map): judge a customer interpretation within the map (step 5)
Thin adapter — it judges the customer's reading WITHIN the already-built
RegulatoryMap, it does not assess abstract legal questions and it is not RCI.

- Reuses the existing assess_interpretation (no new legal reasoning); the 6
  verdicts (plausible/too_narrow/too_broad/partially_correct/unsupported/uncertain)
  pass through unchanged.
- Restricts affected_regulations/affected_obligations to those present in the map
  (intersection); links to the map's uncertain regulations.
- Touched unsupported domains (wastewater/chemicals/...) are reported as
  future_corpus_domains (future_corpus_needed) — never pseudo-evaluated.
- Customer-readable explanation ("Ihre Interpretation ist wahrscheinlich zu eng. …
  Betroffen in Ihrer Map: CRA.").
- POST /reasoning/interpretation-in-map (renders the map, then interprets).
- 7 tests; 63 green (existing reasoning MVP stays green), mypy clean, LOC ok.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:58:00 +02:00
Benjamin Admin 429ac957c1 docs: Feature Knowledge Graph + Sequenz (Domaene 3 Rename + Feature Library; Domaene 2 STOPP #59)
User-Entscheidung: Domaene 3 = „Feature Knowledge Graph" (Kunden kaufen Features, nicht
Capabilities — Advisor beginnt bei „Fernwartung", nicht „cap.transport_encryption"). Besitzt
zusaetzlich Feature Library (~200-400 Features) != Product Profile. Volle Pipeline
Feature Library -> Product Profile -> Capabilities -> Obligations -> Procedures -> Controls -> Evidence.
SEQUENZ: (1) cap.*-Vertrag JETZT an Domaene 3 uebergeben (Multiplikator); (2) Domaene 3 Vollgas
(Feature->cap.*); (3) Domaene 2 STOPP bei #59 (Capability Registry STABIL, nur Bugfixes, bis
Domaene 3 den realen Bedarf zeigt); (4) Domaene 1 Re-Ingest/Spans/Citation.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:45:46 +02:00
Benjamin Admin 9312ad18ef feat(regulatory-map): customer-readable read-model over the scope (step 4)
The Map Renderer explains the engine's state, it does not extend it. Pure
composition of resolve_product_scope (scope verdict) + derive_obligations
(registry-linked obligations + overlaps) into one RegulatoryMap.

- product_summary, trigger_facts, applicable/uncertain/excluded regulations,
  unsupported_domains, overlaps (shared_obligations), shared_evidence, and a
  customer-readable executive_summary.
- No own legal decisions: applicable/uncertain mirror the scope verdict exactly.
- Obligations shown ONLY when registry-linkable (registry_anchor) — MaschinenVO/
  EMV obligations are proposed, so they render empty + a note, never as linked.
  Overlaps/shared_evidence likewise filtered to registry-linked members.
- Uncertain regulations link to the navigator question that would resolve them
  (RED -> has_radio_module, DataAct -> generates_usage_data).
- Environmental appears only as unsupported_domain; executive_summary has NO
  percentage (counts + "no further regulations identified" instead).
- POST /reasoning/regulatory-map (thin handler). Response types are presentation-
  level, not meta-model classes (freeze v1.0 untouched).
- 9 tests; 56 green (existing reasoning MVP stays green), mypy clean, LOC ok.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:36:06 +02:00
Benjamin Admin 2063615d37 feat: Capability Registry v1 API-Vertrag (#59) + Ownership-Modell finalisiert
#59 (geschaerft, User): capabilities.json -> capability_registry_v1 (contract_version 1.0):
stabile `cap.*`-IDs (NIE umbenennen) + 5 Vertragsfelder (description/guidance_basis/
realizes_obligations/required_procedures/evidence_patterns), PRODUKTNEUTRAL (keine Features).
= stabiler API-Vertrag fuer die Product->Compliance-Schnittstelle (Feature->Capability,
Session 3 mappt read-only dagegen).
session_ownership_model_v1.md RESOLVED: Legal-Owner = Re-Ingest-Session (vergibt KEINE
obligation_id, nur citation_span->legal_basis) · 4. Session -> Quality & Validation (nur
Tests, KEINE Daten) · Compliance 2 Branches DAUERHAFT (A=Build, B=Runtime). 4-Bibliotheken-
Zielbild (Legal/Product/Capability/Evidence).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:35:49 +02:00
Benjamin Admin 4d225f73a8 feat(ai-sdk): coverage blind-spot proposer (P2 slice 6, type 4)
Completes the proposer's four types.

- FindCoverageGaps (proposer_coverage.go): deterministic — which EN ISO 12100
  hazard groups A-G did the engine leave with zero hazards for this machine? An
  empty group is a structural blind-spot signal (the machine may truly lack it,
  or a pattern/GT case is missing). Useful with no model at all.
- ProposeMissingHazards + BuildCoveragePrompt: optional LLM expansion of each gap
  into specific expected-but-missing hazards a safety assessor would name
  (propose-only, reuses LLMCompleter, degrades to nil on any error).
- Wired into iace-audit propose -> audit-reports/coverage.{md,json}.

On the dishwasher: D. Pneumatik (truly absent — nothing invented), E. Laerm
(borderline), F. Ergonomie (a genuine gap: manual loading the engine did not
produce). P3 (pin an accepted proposal into a GT case) remains as a human-in-the-
loop follow-up.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin c13aa9183a feat(ai-sdk): vocab->tag proposer (P2 slice 5, type 3)
Extends Method C: for each unknown narrative token that pattern text names, suggest
the keyword_dictionary tag = the RequiredComponentTags shared by the naming
patterns (ranked by frequency, kept only when shared by >=40% of them, top 3).
Surfaces real dictionary gaps like "zwischenkreis" -> stored_energy and
"updates" -> has_software, which close coverage without hand-editing the dict.

Two precision fixes to Method C while here:
- patternsMentioning now matches WHOLE WORDS, not substrings — substring matching
  flagged fragments like "stehen" inside "entstehen" and produced nonsensical
  tag suggestions.
- a token is only proposed with a tag if one is shared by >=40% of its naming
  patterns, so diffuse common verbs (spread across categories) drop out.

Wired into iace-audit propose -> audit-reports/vocab.{md,json}. Residual
common-verb noise is left to the human/LLM filter rather than a hand-grown
stopword list. Type 4 (coverage blind spots) + P3 (pin accepted proposals into a
GT case) remain for slice 6.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 662aec209a feat(ai-sdk): foreign-framing proposer (P2 slice 4, type 2)
Surfaces fired patterns whose zone names terms the machine's narrative never
mentions — foreign framing that leaks through terms not yet in domainGateTerms
(once a term is a gate term, the ghost-pattern invariant already fences it out).

- FindFramingCandidates (proposer_framing.go): per fired pattern, zone terms with
  no narrative echo (minus a generic hazard-location stoplist). Echo matching is
  bidirectional to survive German compounding (narrative "Steuerung" echoes zone
  "Steuerungssystem"). Heuristic verdict foreign (fully orphan) / plausible
  (partial). Over-surfaces by design — human/LLM is the precision filter.
- Wired into iace-audit propose -> audit-reports/framing.{md,json}, threshold via
  IACE_FRAMING_MIN_ORPHAN (default 0.6).

Honest finding: genuine wrong-MACHINE framing (Walzen, Transportbaender) no longer
fires thanks to the machine-type gate; the residual is mostly cyber/control
patterns with generic-industrial zone vocabulary, candidates for re-framing.
Proposal types 3-4 (vocab->tag, coverage blind spots) remain for slice 5.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 8440ddfecb feat(ai-sdk): runnable iace-audit propose CLI + live LLM wiring (P2 slice 3)
Makes the offline proposer runnable end-to-end.

- BuildProposerInput (proposer_input.go): non-test engine->hazards path. The
  PatternMatch->Hazard converter is lifted out of the GT test files into
  production scope so both the tests and the CLI share one pipeline.
- iace-audit propose <narrative.json> [<ground-truth.json>]: detect candidates ->
  GT-screen survivors (when a ground truth is given) -> judge (HeuristicJudge by
  default, LLMJudge over ollama when IACE_PROPOSE_LLM=1) -> write the human-review
  queue to audit-reports/proposals.{md,json}. Propose-only.

Smoke run on a dishwasher narrative: 32 fired -> 3 candidates -> queue with a
confident duplicate, a confident distinct, and one punted to the LLM judge; GT
wall recall-safe. Live qwen is opt-in via env; the heuristic default keeps the
tool runnable (and CI deterministic) without a model. Proposal types 2-4
(foreign-framing gates, vocab->tag, coverage blind spots) remain for slice 4.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 0ce4794767 feat(ai-sdk): pluggable LLM judgment over recall-safe dedup candidates (P2 slice 2)
Adds the semantic judgement layer on top of the slice-1 detector + GT wall.
DEV-TIME, propose-only — nothing mutates the library or runtime.

- CandidateJudge interface with two implementations: HeuristicJudge
  (deterministic default/fallback, used in tests) and LLMJudge (offline, over the
  shared llm.ProviderRegistry via the LLMCompleter adapter). LLMJudge degrades to
  "uncertain" on any transport/parse error — it can never break a run.
- BuildJudgePrompt: the ISO 12100 same-vs-distinct prompt, unit-tested
  deterministically even though the call is not.
- RenderProposalQueue: markdown human-review queue with a suggested action per
  candidate (supersede / keep both / needs review).

On real warewashing output the heuristic punts to "uncertain — needs the LLM
judge" for exactly the two recall-safe near-dupes (HP807/HP033 update,
HP101/HP096 winding-vs-friction), making the LLM's role explicit. All 3 GTs
unaffected (read-only). Live qwen wiring + a CLI/file queue are slice 3.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 8674b2cd9a feat(ai-sdk): offline dedup-candidate proposer + deterministic GT wall (P2 slice 1)
First thin slice of the offline library-improvement proposer. DEV-TIME ONLY,
propose-only — it never mutates the pattern library or the runtime.

- FindDedupCandidates (proposer_dedup.go): structural near-duplicate detection
  over the fired patterns (category + measure/zone/scenario overlap). Bakes in
  the P1 lesson: only same-category pairs compare, and pairs with different
  operational states are never proposed (normal-operation vs maintenance are
  legitimately distinct, e.g. HP011 vs HP077).
- ScreenSupersession (proposer_screen.go): the wall. A proposal is safe only if
  (1) dropping the hazard does not reduce GT recall AND (2) keep/drop do not
  credit DIFFERENT GT entries. Check 2 catches distinct hazards that merely share
  measures (HP2201 hot surface GT 1.3 vs HP2202 hot ware GT 1.4) which recall
  alone would wave through.

On real warewashing output: 3 candidates -> 1 BLOCKED (distinct GT), 2
RECALL-SAFE for human/LLM review (the update + winding/friction near-dupes).
Nothing auto-applied. All 3 GTs unaffected (read-only). The LLM judgement and a
CLI/file queue are slice 2.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 80862e7073 fix(ai-sdk): supersede foreign-framed stored-energy duplicate for warewashing
HP013 (stored electrical energy) fires for dishwashers via the broad stored_energy
tag but its zone is framed for Batteriefaecher/USV-Anlagen, which a dishwasher does
not have. The precise residual-voltage pattern HP144 (Frequenzumrichter/Zwischenkreis,
Priority 90) already fires and covers the same hazard. Add HP013 to the
warewashing-scoped supersession set so the duplicate is dropped only when
dom_warewashing is present.

Warewashing recall stays 100% (25/25), precision 92.6% -> 96.2%. Kistenhub/Bremse
keep HP013 (no dom_warewashing); 26 Bremse pins + benchmark unaffected.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin a8c61eb320 fix(ai-sdk): warewashing-scoped supersession of generic thermal duplicates
The generic hot-surface patterns HP016 (high_temperature) and HP018 (actuator
burn) fire for dishwashers via broad tags and duplicate the precise warewashing
pattern HP2201 (Boiler/Tank/Spuelkammer). Suppress HP016/HP018 only when
dom_warewashing is present, so the specific pattern wins and the duplicate is
dropped. Scoped to the domain tag -> Kistenhub/Bremse and every non-warewashing
machine keep the generic patterns unchanged.

Warewashing recall stays 100% (25/25), precision 90% -> 92.6% (2 dupes removed).
Bremse 26 pins and Kistenhub benchmark unaffected.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 8f89fbf8a7 feat(ai-sdk): order the hazard log by ISO 12100 hazard group
ListHazards returned hazards in pattern-firing order, which reads as a jumble.
Sort by EN ISO 12100 hazard group (A. Mechanisch, B. Elektrisch, C. Thermisch,
D. Pneumatik/Hydraulik, E. Laerm, F. Ergonomie, G. Stoffe, H. Software/Steuerung,
I. Cyber, J. KI), stable within a group. Matches the frontend CATEGORY_LABELS.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 33790bb5e7 fix(ai-sdk): pneumatic restenergy hazard requires actual pneumatics
HP1717 was gated on the generic stored_energy tag (carried by a frequency
converter's DC link) + pneumatic_pressure (emitted by "Boiler unter Druck"),
so it leaked into the dishwasher despite the absence of any pneumatics. Require
pneumatic_part instead. The Bremse pin is a static pattern->measure check
(unaffected); full suite incl. Bremse coverage and Kistenhub 97.1% unchanged.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 7287e989a6 fix(ai-sdk): battery hazards require a battery, not generic stored_energy
HP753 (lithium thermal runaway), HP754 (battery off-gassing) and HP755 (HV
battery shock) were gated on stored_energy, which a frequency converter (C034,
DC-link capacitors) legitimately carries — so they leaked into any machine with
a VFD (surfaced by the dishwasher after the Frequenzumrichter narrative). Now
require the "battery" tag; add lithium/batteriespeicher synonyms so real
battery-storage machines still emit it.

GT #3 100% recall unchanged, battery themes gone from the dishwasher log;
Kistenhub 97.1% and Bremse pinned mappings unchanged.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00
Benjamin Admin 63fe2d496e docs: session_ownership_model_v1.md — Arbeitsteilung nach Modell-Besitz + 3 Vertraege
User-Antwort auf „wie verteilen wir die Arbeit": nach BESITZ der Datenmodelle, NICHT nach
Regulierung. 3 Domaenen (Legal Knowledge / Compliance Execution / Product Knowledge), jede
besitzt EIN Modell (andere read-only). 3 Vertraege: Legal->Compliance citation_span->legal_basis ·
Product->Compliance Feature->Capability (WICHTIGSTE Schnittstelle) · Compliance->Legal
obligation_id->legal_basis. Product Knowledge Graph = naechster Meilenstein (Reasoning-Session
umfokussieren, besitzt schon CanonicalProductRegulatoryProfile+Navigator). NIS2 verschoben.
Offene Fragen: Legal-KG-Owner, IACE-4.-Session, Compliance-2-Branch-Split.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:23:07 +02:00
Benjamin Admin 4e8eb2dc0e feat(product-scope): gate Navigator facts, then reuse discover_scope (step 3)
Connects the Navigator's fact-gate to the existing reasoning discover_scope —
the Scope Engine decides only once the minimum (P0) facts are released.

- resolve_product_scope(canonical): if not ready_for_scope -> NEEDS_FACTS
  (missing_facts + suggested_questions, discover_scope NOT run); else project
  canonical->reasoning profile and run the EXISTING discover_scope exactly once
  -> RESOLVED with applicable/excluded/uncertain regulations.
- Environmental triggers surface ONLY as unsupported_domains (future_corpus_needed),
  never as a legal evaluation — transparency, no false completeness.
- POST /reasoning/product-scope (thin handler) returns case NEEDS_FACTS or RESOLVED.
- No new scope rules, no new regulations, no environmental-law evaluation, no UI,
  no Go, no RAG, no percent-compliance. Response types are application-level, not
  meta-model classes (freeze v1.0 untouched).
- 6 tests incl. discover_scope spy (0 calls when gated, exactly 1 when ready),
  category separation, environmental-as-unsupported-only. 47 tests green (existing
  reasoning MVP tests stay green), mypy clean, LOC ok.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:21:27 +02:00
Benjamin Admin 78aeedafae feat(navigator): Product Regulatory Navigator as a thin missing-facts layer
Step 2 of the convergence sequence. The Navigator sits over the
CanonicalProductRegulatoryProfile (prefilled from company-profile / ProductWizard)
and reports ONLY which facts are still missing + prioritized questions to collect
them. It decides which facts are needed, NEVER what applies — that stays with the
Scope Engine (step 3). No regulation logic, no UI, no Go, no RAG.

- NavigatorQuestion (interaction type, NOT a compliance-meta-model class — freeze
  v1.0 untouched): question_id, target_field, label, why_needed,
  regulatory_domains_unblocked (static metadata), answer_type, options, priority.
- QUESTION_CATALOG: 12 questions over canonical gaps — P0 (markets, role,
  lifecycle, machine/component), P1 (radio, usage-data, security-function,
  environmental wastewater/air/chemicals triggers), P2 (structured BOM).
- engine: navigate() -> missing_facts + suggested_questions (priority-sorted) +
  completeness_summary (ready_for_scope = no P0 missing); apply_answers() ->
  updated profile. Pure field-presence; no scope import.
- 8 tests: <=10 questions for a filled company-profile, known facts not re-asked,
  environmental = trigger questions only (no law evaluation), apply round-trip,
  P0 ordering, ready_for_scope. 41 tests green, mypy clean, LOC ok.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:05:27 +02:00
Benjamin Admin 2e6eee6ba1 Merge origin/main (8609b696) in machinery-multi-reg-run 2026-06-26 10:05:24 +02:00
Benjamin Admin f23ae32077 feat: MaschVO als erster Multi-Regulation-Run + Reuse-Metrik (Freeze haelt: 0 neue Klassen)
User-Reframe: nicht „naechste Regulierung", sondern erster MULTI-REGULATION-Reuse-Test.
- obligations/cra_machinery.json: 31 MaschVO-Obligations (25 LM = Anhang-III-Essential-Reqs
  rechtlich legit + 6 BP). Pipeline 2229->1096 micro->120 review-units->Opus. out_of_scope
  41 RU (AI-Act/DSGVO/Common-Criteria/Banking/...).
- obligations/machinery_reuse_metrics.json: ERSTE Reuse-KPI. **NEUE OBJEKTKLASSEN = 0**
  (Architektur-Freeze haelt gegen physische-Safety-Regulierung — empirisch). 39% Reuse / 61%
  net-new; Capability-Reuse 2 (Cyber-Safety-Bruecke: access_control_safety_functions->access,
  protection_against_corruption->integrity/tamper), Procedure-Reuse 6, Evidence-Reuse 2,
  CORE-Spezialisierung 2 (risk_assessment->update_risk_assessment, conformity->sbom_tech_doc).
- join_keys 95->126 (machinery 31). precluster.py: machinery-Scope.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:05:00 +02:00
Benjamin Admin 739a477d3f feat(profile): CanonicalProductRegulatoryProfile convergence layer (types + mappers + tests)
ONE canonical product profile so the Go gap engine and the Python reasoning
engine stop diverging ("SPS mit Remote Access" means the same everywhere).
gap.ProductProfile LEADS; the reasoning ProductProfile becomes an adapter/DTO.
Types + mappers only — no regulation logic, no Go changes, no UI, no new questions.

- CanonicalProductRegulatoryProfile mirrors gap.ProductProfile + the Navigator
  gaps the audit found: economic-operator role, radio_module, generates_usage_data,
  lifecycle_phase, structured BOM (ProductComponent), safety-vs-security split,
  machine-vs-component + a forward-looking EnvironmentalImpact domain (wastewater/
  air/chemicals triggers — fields only, no rules yet).
- Mappers: from_product_wizard (lossless), from_company_profile (prefill incl.
  the machineBuilder block), to_gap_profile (emits the unchanged gap JSON shape),
  to_reasoning_profile (projects into the reasoning ProductProfile; AI stays
  delegated to ai-act/ucca). Only profile->reasoning is coupled; reasoning stays
  hermetic.
- 10 tests = the 10 acceptance criteria incl. ProductWizard round-trip lossless,
  markets no longer forced ['EU'], and canonical->reasoning->discover_scope
  proving one semantic profile drives the engine. 33 tests green, mypy clean.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 09:52:46 +02:00
Benjamin Admin 8609b696c9 fix(ucca): CM-7 repo_scan is required evidence for attack_surface_minimization
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evidence_required lists only required:true rows; repo_scan was required:false so
attack_surface_minimization surfaced config_export alone. An attack-surface scan
IS required to evidence a minimized attack surface. Adds a test pinning the curated
evidence_required set per NIST obligation (the table test only checked control count).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 09:42:12 +02:00
Benjamin Admin 207fc9cb56 Merge remote-tracking branch 'origin/main' into feat/advisor-status 2026-06-26 09:35:46 +02:00
Benjamin Admin fdaf547b06 feat(ucca): re-point NIST primary_implementation to CORE obligations (#6)
Registry materialized the generic CORE security objectives (#5b, Modell C), so
the two broad NIST controls now point at their canonical parents instead of the
domain-scoped matches:
  SI-7 -> software_integrity_protection  (CORE, Annex I (2)(f))
  CM-7 -> attack_surface_minimization    (CORE, Annex I (2)(j))
Non-breaking: the domain-scoped obligations stay valid and specialize the CORE.

SI-7 evidence = sbom + config_export (SBOM evidences component/supply-chain
integrity; config = signing/secure-boot). Export proposed_obligation_id + handler
test (2 CORE cases) updated. go test green.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 09:35:46 +02:00
Benjamin Admin fa536f9714 docs: compliance_meta_model_v1.md — FROZEN v1.0 + Architektur-Freeze
User-Entscheidung: Metamodell als v1.0 einfrieren (nur META-SEMANTIK: 6 Klassen + Kanten-
Vokabular + Attribute; NICHT Registry/Capabilities/Procedures). Architektur-Freeze in Kraft:
neue Regulierung = DATEN nicht Architektur; 0 neue Objektklassen erwartet; reopen nur bei
nachgewiesenem Scheitern (Hazard/Threat = einzige bekannte künftige Öffnungs-Ursache, nur fuer
FMEA). Reuse-Metrik-KPI definiert (Wissens-Akkumulations-Beweis). Validiert gegen 5
Regulierungsarten (DSGVO/CRA/MaschVO/Data-Act/NIS2). Erster Live-Durchlauf: MaschVO.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 09:29:44 +02:00
Benjamin Admin cba066f49b Merge origin/main (f85fff43) in meta-model-validation 2026-06-26 01:09:16 +02:00
Benjamin Admin 75f7bd8de4 docs: meta_model_validation_v1.md (Phase 6) — Modell ist regulierungsunabhaengig
User-Stresstest VOR der naechsten Regulierung: passt MaschVO/Data-Act/AI-Act/NIS2 ins
6-Klassen-Modell (Obligation/Capability/Procedure/Control/Evidence + Guidance) OHNE neue
Objektklasse? Ergebnis 4x NEIN -> Compliance Meta Model steht. 2 Verfeinerungen
(realized_by Capability OPTIONAL; Risiko-Niveau/Frist/Hazard-Schwere/Risiko-Tier = Attribute,
keine Klassen). 1 Watch-Point: Hazard/Threat (erst noetig bei quantitativem FMEA-Risiko als
First-Class-Knoten, nicht fuer Compliance-Abbildung). Kein Code, keine Regulierung ingestiert.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 01:08:57 +02:00
Benjamin Admin f85fff4398 chore(ucca): re-sync data/obligations join-keys copy (93 -> 95)
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Registry grew to 95 (Capability materialization #5b added CORE obligations).
Keep the ai-sdk build-context copy current so obligation-status reflects the
live registry contract.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 01:02:02 +02:00
Benjamin Admin 3bcffaf52c Merge remote-tracking branch 'origin/main' into feat/advisor-status 2026-06-26 01:01:16 +02:00
Benjamin Admin 3a19affb67 ci(compliance): re-trigger scoped ai-sdk build + doc synced join-keys copy
Prior gitea push's build-ai-sdk failed on a transient registry push (arm64 built
clean on macmini; amd64 cross-compile is green) and last-build/main got poisoned
to that SHA, so a plain re-run scopes to nothing. A real touch in ai-compliance-sdk/
re-scopes the build. Also documents the synced-copy contract for
data/obligations/obligation_join_keys.json.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 01:00:53 +02:00
Benjamin Admin 2b985ad526 Merge origin/main (9aef5ecf) in capability-materialization 2026-06-26 00:54:43 +02:00
Benjamin Admin 4e761c1363 feat: #5b materialize capability layer (Modell C) — capabilities.json + cra_core.json
User-Entscheidung Modell C + objective_tags-Safeguard (Tags, keine Klasse). Deterministisch
via materialize_capabilities.py:
- obligations/capabilities.json: 5 Capabilities (multi_factor_authentication/session_management/
  transport_encryption/code_signing/security_monitoring_alerting), realized_by (n:m) +
  guidance_basis KANONISCH hochgezogen. access_control gedroppt (OVERLAP).
- obligations/cra_core.json: 2 CORE-Sicherheitsziele (attack_surface_minimization (2)(j)/CM-7 +
  software_integrity_protection (2)(f)/SI-7) -> fuellt den #4-NIST-Gap.
- DOMAIN specializes->CORE (remote_access_attack_surface_min, component_remote_interface_security,
  signed_update_integrity, firmware_software_authentication) + objective_tags.
- Merge: vuln_remediation_patching -> deprecated_alias von provide_security_updates.
- remote_access_data_export_protection bleibt BEST_PRACTICE (pending Data-Act-Scope).
- join_keys 93->95 (core 2). Bidirektional validiert.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 00:54:23 +02:00
Benjamin Admin 6673c8052b fix(reasoning): drop "vollständig" from ClaimCoverage wording [F1 final]
"vollständig" still implied fulfillment. potentially_addresses now reads
"… adressiert N Pflichten direkt und M teilweise; K werden durch die Aussage
nicht berührt. … Dies ist keine Konformitätsaussage." Enum value kept
(potentially_addresses chosen over addresses_claimed for product clarity).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 00:49:20 +02:00
Benjamin Admin 5e5002c883 refactor(reasoning): enforce ClaimCoverage (Welt 1) vs ComplianceStatus (Welt 2) boundary [F1]
Architecture-validation finding: the implementation mode produced compliance-
flavored output ("teilweise erfüllt", "covered") from a mere customer claim,
blurring the line to the Execution layer. This is a design decision, not a text
fix — the reasoning layer judges only the customer's STATEMENT, never conformity.

- CoverageStatus -> ClaimCoverage; values are claim-relative + carry "potential":
  potentially_addresses / partially_addresses / does_not_address /
  insufficient_information.
- ImplementationAssessment -> ClaimObligationMapping (coverage_status ->
  claim_coverage); ImplementationResponse -> ImplementationReasoningResponse
  (assessments -> mappings, + explicit `disclaimer`); request renamed; engine
  entry assess_implementation -> reason_implementation_claim.
- Endpoint /reasoning/implementation-assessment -> /reasoning/implementation-reasoning.
- Summary/explanations reworded: "adressiert wahrscheinlich N Pflichten … für
  eine Bewertung der tatsächlichen Umsetzung sind Nachweise erforderlich (keine
  Konformitätsaussage)". No "erfüllt"/"abgedeckt" leaks.
- New guard test asserts no compliance verdict leaks (no "erfüllt"; disclaimer
  separates ClaimCoverage from ComplianceStatus). 23 tests green, mypy clean.

Discovery (scope/obligations) was already structurally claim-free and unaffected.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 00:37:57 +02:00
Benjamin Admin 9aef5ecf6c Merge remote-tracking branch 'origin/main' into feat/advisor-status
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2026-06-26 00:26:56 +02:00
Benjamin Admin f6c5f4e0a9 fix(ucca): SI-2 evidence = config_export + test_report
Aligns provide_security_updates -> SI-2 evidence to the curated acceptance set:
config_export (secure-update mechanism config) + test_report (patch verification).
For "provide updates" the patch-verification test is more on-point than a vuln
scan; repo_scan stays on CM-7 for attack-surface.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 00:26:29 +02:00
Benjamin Admin c72fd3eb5a Merge origin/main (Compliance endpoint+graph-loader 2341bda6) in capability-model 2026-06-26 00:24:37 +02:00
Benjamin Admin b0435f9885 docs: capability_model_v1.md (#5a) — Objektarten + Beziehungstypen, NICHT materialisiert
Schema-Papier statt capabilities.json (User-Entscheidung). Befund: die 8 SHARED_CAPABILITY-
Cluster zerfallen in Typ-1 (technische Capabilities: mfa/tls/code_signing/session/anomaly)
und Typ-2 (Sicherheitsziele: attack_surface_min/software_integrity = die #4-Gaps). Empfehlung
Modell C: Capability = EINZIGE neue Klasse; Sicherheitsziele = CORE Legal Obligations
(CORE/DOMAIN existiert bereits). Kanten-Graph (realized_by/specializes/...). guidance_basis
gehört konzeptionell an die Capability. 4 Entscheidungen offen (User). #5b Materialisierung
GEGATED auf Modell-Annahme — keine Daten verschoben.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 00:24:09 +02:00
Benjamin Admin 2341bda621 feat(ucca): adopt NIST obligation_ids (Registry Handoff #4, 10/10)
Registry filled proposed_obligation_id for the 3 NIST primary_implementation
controls: SI-7->signed_update_integrity, SI-2->provide_security_updates,
CM-7->remote_access_attack_surface_min. Adopted onto cra_nist.jsonl so the join
is now EXACT (obligation_id) instead of the coarse citation_unit fallback.
obligation-status now surfaces SI-2 under provide_security_updates; test extended.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 19:37:14 +02:00
Benjamin Admin 4634cc09d0 Merge remote-tracking branch 'origin/main' into feat/advisor-status 2026-06-25 19:31:20 +02:00
Benjamin Admin 1607c89459 feat(reasoning): Regulatory Reasoning Engine MVP (scope/obligations/implementation/interpretation)
Deterministic reasoning layer ON TOP of the Legal Knowledge Graph (obligation
registry) and the Compliance Execution Graph (control mapping/evidence). Answers
which regulations apply to a concrete product, which obligations follow, whether
the customer's implementation covers them, and whether a customer interpretation
is too narrow/broad/plausible.

- ProductProfile with tri-state facts (Optional[bool]=None => uncertain, never
  false security); safe predicate evaluator (no eval).
- 6 regulation triggers (CRA/MaschinenVO/RED/EMV/DataAct/NIS2) with missing-fact
  prompts; 24 obligation scope rules.
- CRA obligation_ids RE-USED verbatim from the registry (93 ids) — never re-minted
  (control_uuid trap); Machine/Data-Act flagged proposed=True.
- required_evidence constrained to the framework-agnostic shared evidence catalog;
  capabilities echo the planned Obligation->Capability layer.
- Overlap groups (CRA<->MaschinenVO cyber-safety) + evidence-for-multiple (USP).
- 4 endpoints POST /reasoning/{scope,obligations,implementation-assessment,
  interpretation-assessment}; thin handlers, registered in api/__init__.py.
- 22 tests (5 machine-builder scenarios + 10 acceptance questions). No DB
  migration, no RAG, no new controls.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 19:30:53 +02:00
Benjamin Admin d4df1e01df feat(compliance): GET /sdk/v1/compliance/obligation-status (file-backed graph)
Vertical slice over the Compliance Execution Graph: obligation_id -> accepted
controls -> required evidence -> status. NEVER auto-asserts fulfillment - with
no evidence collection wired (MVP), a mapped obligation is "not_assessed" and
every required evidence is "missing". Fail-closed: no id -> 400; unknown id ->
unknown_obligation; mapped-but-no-control -> unmapped; graph not loaded -> 503.

- ComplianceGraphHandlers (separate from the DB-backed ObligationsHandlers):
  loads Registry join keys + accepted control mappings + evidence once at start.
- LoadComplianceGraph: candidate-path resolution across dev/container/test.
- Data plumbing: Dockerfile now COPYs data/{control_mappings,evidence_requirements,
  obligations}; data/obligations/obligation_join_keys.json is a SYNCED COPY of the
  repo-root Registry contract (re-sync on Registry growth).
- Table-driven handler test (mapped/unmapped/unknown/400 + no-fulfillment-claim).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 19:29:37 +02:00
Benjamin Admin ed31fdc0df fill: NIST primary_implementation -> obligation_id (Handoff #4, jetzt 10/10)
SI-2 -> provide_security_updates (stark, (2)(c)/Art.13) · SI-7 -> signed_update_integrity
(update-scoped) · CM-7 -> remote_access_attack_surface_min (remote-scoped). Validiert gegen
Registries (join_keys 93). GAP-BEFUND (Cross-Domain-Review): generische Parent-Obligations
software_integrity_protection + attack_surface_minimization fehlen (SI-7/CM-7 sind breiter
als die domaenen-scoped Treffer) -> Kandidaten fuer neue Obligations (User-Entscheidung).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 19:15:49 +02:00
Benjamin Admin 5412bf0ba3 Merge origin/main (NIST-Export e46e74dd) in cross-domain-discovery 2026-06-25 19:13:21 +02:00
Benjamin Admin 8a9d5e7c4d Merge remote-tracking branch 'origin/main' into feat/advisor-status 2026-06-25 19:12:41 +02:00
Benjamin Admin 01956ee690 feat: cross-domain relationship discovery — Capability-Schicht-Entwurf (CRA P1)
Stufe 1+2 der Ontologie-Entdeckung (User-Schaerfung #54): nicht Aehnlichkeit sondern
STRUKTURELLE Beziehung. 93 Obligations -> BGE-M3 -> 101 cross-family Paare -> Opus
klassifiziert in 8 Kategorien (genau eine je Paar).
- scripts/obligation_discovery/cross_domain_pairs.py (Stufe 1, key-frei)
- scripts/obligation_discovery/classify_relationships.py (Stufe 2, Opus)
- obligations/cross_domain_relationships.json: 16 SHARED_CAPABILITY -> 8 Capabilities
  (mfa/session/transport-tls/code_signing/anomaly_detection), 23 SUPPORTED_BY
  (Hubs: vuln_identification_inventory<-SBOM-Familie 5x, vuln_remediation_patching 5x),
  1 SAME_OBLIGATION (vuln_remediation_patching == provide_security_updates, MERGE-Kandidat),
  42 OVERLAP_ONLY sauber verworfen. Erstentwurf der Capability-Schicht (Phase 4).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 19:12:17 +02:00
Benjamin Admin e46e74ddbb feat(bridge): export 3 CRA->NIST controls (primary_implementation) for obligation_id
Adds SI-7/SI-2/CM-7 to controls_for_obligation_mapping.json (7 OWASP -> 10),
mapping_type=primary_implementation (the single canonical control per obligation).
proposed_obligation_id left empty for the Registry to assign. Notes aligned to the
updates family (join_keys 93): SI-2 -> provide_security_updates (strong),
SI-7 -> signed_update_integrity (partial; SI-7 broader), CM-7 ->
remote_access_attack_surface_min (partial; CM-7 broader).

Origin-only (data/tooling; backend does not load obligations/* at runtime) -> no Orca.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 18:57:17 +02:00
Benjamin Admin 63d65af41b feat(ucca): persist 3 CRA->NIST mappings (primary_implementation) + evidence
CRA Annex I Part I (2)(e)/(2)(l)/(2)(i) had no clean OWASP target (rejected:
"Mapping ueber NIST/BSI erforderlich"). Their NIST home, curated + accepted:
  (2)(e) Integritaet     -> SI-7 (Software/Firmware/Information Integrity)
  (2)(l) Sichere Updates -> SI-2 (Flaw Remediation)
  (2)(i) Angriffsflaeche -> CM-7 (Least Functionality)

New mapping_type=primary_implementation = the single canonical control per
obligation (stronger than implements/supports); related controls (SC-3(3),
RA-5, AC-6, SI-16, ...) follow later as supports.

Evidence is framework-AGNOSTIC: SI-7/SI-2/CM-7 reuse the shared evidence_type
catalog (config_export/test_report/repo_scan) - same types carry CRA, NIST,
ISO 27001, IEC 62443, BSI. (framework,control) is only the link, not the type.

obligation_id left empty: the Obligation Registry assigns it (exported via
controls_for_obligation_mapping.json), then we adopt. go test ./internal/ucca green.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 18:52:35 +02:00
Benjamin Admin 8937f105ea feat(bridge): security-updates obligation cut (CRA Annex I (2)(c)/Art 13) — 9 obligations
- obligations/cra_updates.json: 9 (6 LEGAL_MINIMUM + 3 BEST_PRACTICE), Beziehungen.
  Pipeline 670->318 micro->15 review-units -> Opus-Synthese. Synthese gut kalibriert ->
  light review (KEINE Hart-Re-Tier, vs Auth/Remote-Access). out_of_scope M4/M7.
  5 capability_candidate-Marker (signed/trusted/automatic/rollback/testing) fuer
  Phase-4-Capability-Pruefung. Anker approximativ (curation.anchor_quality).
- obligation_join_keys.json: 84 -> 93 (updates 9). Alle 6 CRA-P1-Domaenen abgedeckt.
- precluster.py: updates-Scope.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 18:51:09 +02:00
Benjamin Admin 1584b8fb2f feat(bridge): remote-access obligation cut (CRA Annex I) — 18 obligations
- obligations/cra_remote_access.json: 18 (5 LEGAL_MINIMUM outcomes + 13 BEST_PRACTICE),
  15 Beziehungen. Two-stage clustering 445->209 micro->27 review-units -> Opus-Synthese.
  Synthese vergab 14 LM -> key-free re-tier nach Auth-Regel (Mechanismen MFA/Session/VPN/
  insecure-protocol/OT/Wartungs-Governance/temp/data-export/component -> BEST_PRACTICE +
  supports-Kante zur Eltern-LM). out_of_scope M5/M11 = physische Maschinen-Fernsteuerung
  (MaschinenVO 2023/1230). Anker approximativ (siehe curation.anchor_quality).
- obligation_join_keys.json: 66 -> 84 (remote_access 18).
- precluster.py: remote_access-Scope.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-25 18:37:10 +02:00
109 changed files with 20843 additions and 88 deletions
+8
View File
@@ -33,6 +33,14 @@ COPY migrations/ ./migrations/
# Copy policy files (YAML rules)
COPY policies/ ./policies/
# Copy Compliance Execution Graph data (file-backed: Registry join-key copy + accepted control
# mappings + evidence requirements) consumed by GET /sdk/v1/compliance/obligation-status.
# data/obligations/obligation_join_keys.json is a synced copy of the repo-root Registry contract
# (the Obligation Registry owns the canonical file) — re-sync it when the Registry grows.
COPY data/control_mappings/ ./data/control_mappings/
COPY data/evidence_requirements/ ./data/evidence_requirements/
COPY data/obligations/ ./data/obligations/
# Create non-root user
RUN adduser -D -u 1000 appuser
USER appuser
+3 -1
View File
@@ -34,6 +34,8 @@ func main() {
cmdEcho(os.Args[2:])
case "hierarchy":
cmdHierarchy(os.Args[2:])
case "propose":
cmdPropose(os.Args[2:])
default:
usage()
os.Exit(2)
@@ -41,7 +43,7 @@ func main() {
}
func usage() {
fmt.Fprintln(os.Stderr, "Usage: iace-audit <reachability|consistency|vocabulary|echo|hierarchy> [args]")
fmt.Fprintln(os.Stderr, "Usage: iace-audit <reachability|consistency|vocabulary|echo|hierarchy|propose> [args]")
}
func cmdReachability(_ []string) {
+188
View File
@@ -0,0 +1,188 @@
package main
import (
"context"
"encoding/json"
"fmt"
"os"
"strconv"
"strings"
"github.com/breakpilot/ai-compliance-sdk/internal/iace"
"github.com/breakpilot/ai-compliance-sdk/internal/iace/audit"
"github.com/breakpilot/ai-compliance-sdk/internal/llm"
)
type narrativeInput struct {
MachineType string `json:"machine_type"`
Narrative string `json:"narrative"`
MachineTypes []string `json:"machine_types,omitempty"`
}
// cmdPropose — Method P: offline dedup-candidate proposer.
//
// iace-audit propose <narrative.json> [<ground-truth.json>]
//
// Detect near-duplicate patterns, screen survivors against a ground truth (if
// given), judge them (heuristic by default, LLM when enabled), and write the
// human-review queue to audit-reports/proposals.{md,json}. Propose-only — it
// writes a report and never mutates the pattern library.
//
// Env:
//
// IACE_PROPOSE_THRESHOLD candidate score threshold (default 0.30)
// IACE_PROPOSE_LLM=1 use the offline LLM judge instead of the heuristic
// OLLAMA_URL ollama base URL (default http://localhost:11434)
// SELF_HOSTED_LLM_MODEL model name (default qwen2.5:32b-instruct)
func cmdPropose(args []string) {
if len(args) < 1 {
fmt.Fprintln(os.Stderr, "propose: usage: iace-audit propose <narrative.json> [<ground-truth.json>]")
os.Exit(2)
}
var in narrativeInput
must(readJSONFile(args[0], &in))
if in.Narrative == "" {
fmt.Fprintln(os.Stderr, "propose: narrative is empty")
os.Exit(2)
}
var gt *iace.GroundTruth
if len(args) >= 2 {
var g iace.GroundTruth
must(readJSONFile(args[1], &g))
gt = &g
}
threshold := envFloat("IACE_PROPOSE_THRESHOLD", 0.30)
hazards, mits, fired := iace.BuildProposerInput(in.Narrative, in.MachineType, in.MachineTypes)
candidates := iace.FindDedupCandidates(fired, threshold)
byID := make(map[string]iace.PatternMatch, len(fired))
for _, pm := range fired {
byID[pm.PatternID] = pm
}
judge := selectJudge(in.MachineType)
ctx := context.Background()
var proposals []iace.JudgedProposal
blocked := 0
for _, c := range candidates {
var sr iace.ScreenResult
if gt != nil {
sr = iace.ScreenSupersession(gt, hazards, mits, c.KeepHazardName, c.DropName)
if sr.RecallAfter < sr.RecallBefore || sr.DistinctGT {
blocked++
continue
}
}
v, conf, rat := judge.Judge(ctx, c, byID[c.KeepPattern], byID[c.DropPattern])
proposals = append(proposals, iace.JudgedProposal{
Candidate: c, Screen: sr, Verdict: v, Confidence: conf, Rationale: rat, Judge: judge.Name(),
})
}
writeText("audit-reports/proposals.md", iace.RenderProposalQueue(in.MachineType, proposals))
writeJSON("audit-reports/proposals.json", proposals)
// Type 2: foreign-framing candidates (zone terms with no narrative echo).
framing := iace.FindFramingCandidates(fired, in.Narrative, envFloat("IACE_FRAMING_MIN_ORPHAN", 0.6))
writeText("audit-reports/framing.md", iace.RenderFramingQueue(in.MachineType, framing))
writeJSON("audit-reports/framing.json", framing)
// Type 3: vocab->tag proposals (unknown narrative tokens that pattern text
// names as a whole word, with a dominant shared required tag).
vocab := audit.RunVocabulary(map[string]any{"narrative": in.Narrative})
var vgaps []audit.DictionarySuggestion
for _, s := range vocab.SuggestedDictionaryEntries {
if len(s.SuggestedTags) > 0 {
vgaps = append(vgaps, s)
}
}
writeText("audit-reports/vocab.md", renderVocabQueue(in.MachineType, vgaps))
writeJSON("audit-reports/vocab.json", vgaps)
// Type 4: coverage blind-spots (empty ISO 12100 groups A-G) + LLM expansion.
gaps := iace.FindCoverageGaps(hazards)
var missing []iace.MissingHazard
if lj, ok := judge.(iace.LLMJudge); ok {
missing = iace.ProposeMissingHazards(ctx, lj.Completer, in.MachineType, in.Narrative, hazards, gaps)
}
writeText("audit-reports/coverage.md", iace.RenderCoverageQueue(in.MachineType, gaps, missing))
writeJSON("audit-reports/coverage.json", gaps)
printSummary("Method P — Dedup Proposer ("+judge.Name()+")", map[string]int{
"fired_patterns": len(fired),
"candidates": len(candidates),
"in_queue": len(proposals),
"gt_blocked": blocked,
"framing_flags": len(framing),
"vocab_gaps": len(vgaps),
"coverage_gaps": len(gaps),
})
if gt == nil {
fmt.Fprintln(os.Stderr, "note: no ground truth provided — GT wall NOT applied (candidates not recall-screened)")
}
}
func selectJudge(machineClass string) iace.CandidateJudge {
if os.Getenv("IACE_PROPOSE_LLM") != "1" {
return iace.HeuristicJudge{}
}
base := envStr("OLLAMA_URL", "http://localhost:11434")
model := envStr("SELF_HOSTED_LLM_MODEL", "qwen2.5:32b-instruct")
reg := llm.NewProviderRegistry("ollama", "")
reg.Register(llm.NewOllamaAdapter(base, model))
fmt.Printf("using LLM judge (ollama %s, model %s)\n", base, model)
return iace.LLMJudge{Completer: iace.NewRegistryCompleter(reg, model), MachineClass: machineClass}
}
func readJSONFile(path string, v any) error {
raw, err := os.ReadFile(path)
if err != nil {
return err
}
return json.Unmarshal(raw, v)
}
func writeText(path, content string) {
_ = os.MkdirAll("audit-reports", 0o755)
if err := os.WriteFile(path, []byte(content), 0o644); err != nil {
fmt.Fprintln(os.Stderr, "warn: could not write", path, err)
return
}
fmt.Println("→ wrote", path)
}
func envStr(key, def string) string {
if v := os.Getenv(key); v != "" {
return v
}
return def
}
func envFloat(key string, def float64) float64 {
if v := os.Getenv(key); v != "" {
if f, err := strconv.ParseFloat(v, 64); err == nil {
return f
}
}
return def
}
func renderVocabQueue(machine string, entries []audit.DictionarySuggestion) string {
var b strings.Builder
fmt.Fprintf(&b, "# Vocab→tag review queue — %s\n\n", machine)
fmt.Fprintf(&b, "%d unknown token(s) appear in pattern text but map to no dictionary tag. Propose-only — a human (or the LLM) confirms the tag, then adds a keyword_dictionary entry and pins a GT case.\n\n", len(entries))
for i, s := range entries {
tag := "<tag>"
if len(s.SuggestedTags) > 0 {
tag = s.SuggestedTags[0]
}
fmt.Fprintf(&b, "## %d. \"%s\" → suggested tag(s): %s\n", i+1, s.Token, strings.Join(s.SuggestedTags, ", "))
fmt.Fprintf(&b, "- named by %d pattern(s): %s\n", len(s.PatternIDs), strings.Join(s.PatternIDs, ", "))
fmt.Fprintf(&b, "- suggested action: add keyword_dictionary entry {%q → %s} so narratives mentioning it trigger those patterns; human confirms\n\n", s.Token, tag)
}
return b.String()
}
@@ -0,0 +1,8 @@
// Control-Mapping: CRA Annex I -> NIST SP 800-53 Rev. 5. Eine Zeile = ein Mapping (Schema: ControlMapping).
// Reviewt 2026-06-25 (benjamin): 3 accepted, mapping_type=primary_implementation (kanonische Primaer-Control je Anforderung).
// Heimat der OWASP-Rejects (2)(e)/(2)(l)/(2)(i): dort war OWASP nicht der Zielstandard ("Mapping ueber NIST/BSI erforderlich").
// related-Controls (SC-3(3), RA-5, AC-6, SI-16, ...) folgen separat als mapping_type=supports — hier nur der kanonische Einstieg.
// obligation_id (Registry-Handoff #4 adoptiert, #6 auf CORE re-pointet 2026-06-26): SI-7->software_integrity_protection (CORE (2)(f)), SI-2->provide_security_updates, CM-7->attack_surface_minimization (CORE (2)(j)). Join exakt. Die domaenen-scoped IDs (signed_update_integrity, remote_access_attack_surface_min) bleiben gueltige Obligations und zeigen per specializes->CORE auf diese Ziele.
{"source_norm": "CRA Annex I Part I (2)(e) — Integritaet", "source_role": "operational_requirement", "target_framework": "NIST SP 800-53", "target_control": "SI-7", "mapping_type": "primary_implementation", "mapping_status": "accepted", "provenance": "human_curated", "rationale": "NIST SI-7 = Software, Firmware, and Information Integrity — kanonische Integritaetskontrolle (Signaturpruefung, Manipulationserkennung).", "reviewed_by": "benjamin", "review_date": "2026-06-25", "review_reason": "Primaere Implementierung der CRA-Integritaetsanforderung; OWASP war hier kein passender Treffer. Related (spaeter, supports): SA-10, CM-14.", "version": "2026-06-25", "obligation_id": "software_integrity_protection"}
{"source_norm": "CRA Annex I Part I (2)(l) — Sichere Updates", "source_role": "operational_requirement", "target_framework": "NIST SP 800-53", "target_control": "SI-2", "mapping_type": "primary_implementation", "mapping_status": "accepted", "provenance": "human_curated", "rationale": "NIST SI-2 = Flaw Remediation — kanonische Update-/Patch-Kontrolle.", "reviewed_by": "benjamin", "review_date": "2026-06-25", "review_reason": "Primaere Implementierung der CRA-Update-Anforderung. Related (spaeter, supports): RA-5, CM-3, SA-11.", "version": "2026-06-25", "obligation_id": "provide_security_updates"}
{"source_norm": "CRA Annex I Part I (2)(i) — Angriffsflaeche minimieren", "source_role": "operational_requirement", "target_framework": "NIST SP 800-53", "target_control": "CM-7", "mapping_type": "primary_implementation", "mapping_status": "accepted", "provenance": "human_curated", "rationale": "NIST CM-7 = Least Functionality — Deaktivierung nicht benoetigter Ports/Dienste/Funktionen.", "reviewed_by": "benjamin", "review_date": "2026-06-25", "review_reason": "CM-7 als Primaer-Control fuer Angriffsflaeche (nicht SC-3(3)). Related (spaeter, supports): SC-3(3), AC-6, SI-16.", "version": "2026-06-25", "obligation_id": "attack_surface_minimization"}
@@ -0,0 +1,10 @@
// Evidence-Requirements je NIST-SP-800-53-Control (Schema: EvidenceRequirement). Eine Zeile = eine geforderte Evidenz.
// WICHTIG: evidence_type ist FRAMEWORK-AGNOSTISCH (geteilter Katalog config_export/test_report/repo_scan/sbom/...) —
// dieselben Typen tragen CRA, NIST, ISO 27001, IEC 62443, BSI. (framework, control) ist nur der Verweis, nicht der Typ.
// Stand 2026-06-25, Basis: die 3 accepted CRA->NIST primary_implementation-Mappings (SI-7 Integritaet, SI-2 Updates, CM-7 Angriffsflaeche).
{"framework": "NIST SP 800-53", "control": "SI-7", "evidence_type": "sbom", "evidence_source": "ci", "freshness_requirement": "per_release", "required": true, "rationale": "SBOM weist die Integritaet/Herkunft der Software-Bestandteile nach (bekannte, unmanipulierte Komponenten).", "version": "2026-06-25"}
{"framework": "NIST SP 800-53", "control": "SI-7", "evidence_type": "config_export", "evidence_source": "github", "freshness_requirement": "per_release", "required": true, "rationale": "Secure-Boot-/Code-Signing-Konfiguration als Nachweis der Integritaetspruefung.", "version": "2026-06-25"}
{"framework": "NIST SP 800-53", "control": "SI-2", "evidence_type": "config_export", "evidence_source": "github", "freshness_requirement": "per_release", "required": true, "rationale": "Konfiguration des sicheren Update-/Patch-Mechanismus (signierte/automatische Updates) als technischer Nachweis.", "version": "2026-06-25"}
{"framework": "NIST SP 800-53", "control": "SI-2", "evidence_type": "test_report", "evidence_source": "ci", "freshness_requirement": "per_release", "required": true, "rationale": "Update-/Patch-Verifikationstest (CI) belegt, dass Sicherheitsupdates greifen.", "version": "2026-06-25"}
{"framework": "NIST SP 800-53", "control": "CM-7", "evidence_type": "config_export", "evidence_source": "github", "freshness_requirement": "per_release", "required": true, "rationale": "Konfiguration deaktivierter Ports/Dienste/Funktionen als Nachweis minimierter Angriffsflaeche.", "version": "2026-06-25"}
{"framework": "NIST SP 800-53", "control": "CM-7", "evidence_type": "repo_scan", "evidence_source": "scanner", "freshness_requirement": "per_release", "required": true, "rationale": "Angriffsflaechen-Scan (offene Ports/Dienste) als Nachweis tatsaechlich minimierter Angriffsflaeche.", "version": "2026-06-25"}
@@ -0,0 +1,846 @@
{
"schema_version": "obligation_join_keys_v1",
"contract": "obligation_id ist der stabile Join-Key. Legal Knowledge Graph haengt citation_spans an obligation_id; Compliance Execution Graph mappt control_mapping.source_norm -> obligation_id. Interim-Bruecke = citation_units. obligation_id NIE neu vergeben (re-link).",
"count": 95,
"obligation_ids": [
{
"obligation_id": "sbom_creation",
"regulation": "CRA",
"family": "sbom",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part II (1)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "sbom_dependency_coverage",
"regulation": "CRA",
"family": "sbom",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Art. 3(36) i.V.m. Annex I Part II (1)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "sbom_format_standard",
"regulation": "CRA",
"family": "sbom",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part II (1)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "sbom_maintenance_update",
"regulation": "CRA",
"family": "sbom",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part II (1)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "sbom_completeness_verification",
"regulation": "CRA",
"family": "sbom",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "sbom_tooling_automation",
"regulation": "CRA",
"family": "sbom",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "IMPLEMENTATION"
},
{
"obligation_id": "sbom_access_provision",
"regulation": "CRA",
"family": "sbom",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "sbom_authority_provision",
"regulation": "CRA",
"family": "sbom",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Art. 31 / Annex I Part II (1)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "sbom_confidentiality",
"regulation": "CRA",
"family": "sbom",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Art. 31(4)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "sbom_supply_chain_contracts",
"regulation": "CRA",
"family": "sbom",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "sbom_technical_documentation",
"regulation": "CRA",
"family": "sbom",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Art. 31 i.V.m. Annex VII"
],
"source_role": "EVIDENCE"
},
{
"obligation_id": "vuln_identification_inventory",
"regulation": "CRA",
"family": "vuln",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part II (1)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "vuln_assessment_prioritization",
"regulation": "CRA",
"family": "vuln",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part II (1)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "vuln_remediation_patching",
"regulation": "CRA",
"family": "vuln",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part II (2) & (8)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "vuln_handling_process",
"regulation": "CRA",
"family": "vuln",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Article 13(8) & Annex VII"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "coordinated_vulnerability_disclosure",
"regulation": "CRA",
"family": "vuln",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part II (5)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "exploited_vuln_reporting_authorities",
"regulation": "CRA",
"family": "vuln",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Article 14 & Article 16"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "vuln_info_dissemination_users",
"regulation": "CRA",
"family": "vuln",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part II (4) & (6)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "attack_surface_minimization",
"regulation": "CRA",
"family": "core",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part I (2)(j)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "software_integrity_protection",
"regulation": "CRA",
"family": "core",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part I (2)(f)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "user_authentication_required",
"regulation": "CRA",
"family": "authentication",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(d)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "authentication_policy_documented",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "auth_exceptions_documented",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "mfa_required",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "step_up_authentication",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "privileged_op_reauth",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "strong_crypto_authentication",
"regulation": "CRA",
"family": "authentication",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(e)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "credential_lifecycle_management",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "credential_confidentiality_protection",
"regulation": "CRA",
"family": "authentication",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(e)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "password_policy",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "no_default_credentials",
"regulation": "CRA",
"family": "authentication",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(a)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "account_lockout_failed_attempts",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "server_side_validation",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "session_binding_management",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "reauth_after_inactivity",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "token_validation_lifecycle",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "mutual_authentication",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "revocation_check",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "encrypted_auth_channel",
"regulation": "CRA",
"family": "authentication",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(e)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "tls_certificate_auth",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "service_to_service_auth",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "auth_key_management",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "biometric_authentication",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "federated_auth_assertions",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "separate_authn_authz",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_authentication",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "supplier_access_auth",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "personal_admin_accounts",
"regulation": "CRA",
"family": "authentication",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "firmware_software_authentication",
"regulation": "CRA",
"family": "authentication",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(c)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "event_logging_security_events",
"regulation": "CRA",
"family": "logging",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part I (2)(k)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "access_control_event_logging",
"regulation": "CRA",
"family": "logging",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part I (2)(k)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "audit_trail_admin_actions",
"regulation": "CRA",
"family": "logging",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part I (2)(k)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "log_integrity_immutability",
"regulation": "CRA",
"family": "logging",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part I (2)(k)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "log_access_control_protection",
"regulation": "CRA",
"family": "logging",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part I (2)(k)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "log_retention_archival",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "centralized_log_management",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "log_monitoring_alerting",
"regulation": "CRA",
"family": "logging",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I Part I (2)(k)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "log_data_minimization_privacy",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "log_format_standardization",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "log_timestamp_synchronization",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "logging_availability_resilience",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "logging_thread_safety_correctness",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "IMPLEMENTATION"
},
{
"obligation_id": "logging_library_supply_chain",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "logging_config_management",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "logging_governance_roles",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "incident_response_logging",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "log_transmission_security",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "network_traffic_logging",
"regulation": "CRA",
"family": "logging",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_control_least_privilege",
"regulation": "CRA",
"family": "remote_access",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (1)(2)(d)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "remote_access_confidentiality_integrity",
"regulation": "CRA",
"family": "remote_access",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (1)(2)(b)(c)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "remote_session_management",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_mfa",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_encryption",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "reject_insecure_remote_protocols",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_logging_audit",
"regulation": "CRA",
"family": "remote_access",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (1)(2)(g)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "remote_access_user_validation_ot",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_training",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_architecture_design",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_attack_surface_min",
"regulation": "CRA",
"family": "remote_access",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (1)(2)(a)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "remote_access_vuln_patch_mgmt",
"regulation": "CRA",
"family": "remote_access",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(1)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "remote_access_threat_detection",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_maintenance_governance",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "temporary_remote_access_mgmt",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_data_export_protection",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "component_remote_interface_security",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "remote_access_fallback_concept",
"regulation": "CRA",
"family": "remote_access",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "provide_security_updates",
"regulation": "CRA",
"family": "updates",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(c)",
"Art. 13"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "support_period_maintenance",
"regulation": "CRA",
"family": "updates",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Art. 13(8)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "signed_update_integrity",
"regulation": "CRA",
"family": "updates",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (1)(3)(f)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "trusted_update_source",
"regulation": "CRA",
"family": "updates",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (1)(3)(d)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "update_testing_validation",
"regulation": "CRA",
"family": "updates",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "update_rollback",
"regulation": "CRA",
"family": "updates",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "GUIDANCE"
},
{
"obligation_id": "automatic_updates_optout",
"regulation": "CRA",
"family": "updates",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (2)(c)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "update_risk_assessment",
"regulation": "CRA",
"family": "updates",
"tier": "LEGAL_MINIMUM",
"citation_units": [
"Annex I (1)(2)"
],
"source_role": "LEGAL_BASIS"
},
{
"obligation_id": "secure_modification_control",
"regulation": "CRA",
"family": "updates",
"tier": "BEST_PRACTICE",
"citation_units": [],
"source_role": "IMPLEMENTATION"
}
]
}
@@ -0,0 +1,126 @@
package handlers
import (
"net/http"
"strings"
"github.com/gin-gonic/gin"
"github.com/breakpilot/ai-compliance-sdk/internal/ucca"
)
// ComplianceGraphHandlers serves the read-only Compliance Execution Graph
// (Regulation -> Obligation -> Control -> Evidence) over the file-backed bridge artifacts.
// It is intentionally SEPARATE from the DB-backed ObligationsHandlers: this is the curated
// cross-session graph (Registry join keys + accepted control mappings + evidence requirements),
// loaded once at startup. Fail-closed: if the graph could not load, every request answers 503.
type ComplianceGraphHandlers struct {
joins *ucca.ObligationJoinKeys
mappings *ucca.ControlMappingSet
evidence *ucca.EvidenceRequirementSet
loadErr error
}
// NewComplianceGraphHandlers loads the graph once. Construction never fails; a load error is
// retained and surfaced as 503 per request (matches the codebase's load-warn-continue startup).
func NewComplianceGraphHandlers() *ComplianceGraphHandlers {
joins, mappings, evidence, err := ucca.LoadComplianceGraph()
return &ComplianceGraphHandlers{joins: joins, mappings: mappings, evidence: evidence, loadErr: err}
}
// LoadError exposes a startup load failure so the wiring can log a warning.
func (h *ComplianceGraphHandlers) LoadError() error { return h.loadErr }
// RegisterRoutes mounts the compliance-graph routes under /compliance.
func (h *ComplianceGraphHandlers) RegisterRoutes(r *gin.RouterGroup) {
g := r.Group("/compliance")
g.GET("/obligation-status", h.ObligationStatus)
}
type cgControlDTO struct {
Framework string `json:"framework"`
Control string `json:"control"`
MappingType string `json:"mapping_type"`
EvidenceRequired []string `json:"evidence_required"`
EvidenceStatus string `json:"evidence_status"` // missing | partial | present | none_required
}
type cgStatusResponse struct {
ObligationID string `json:"obligation_id"`
OverallStatus string `json:"overall_status"` // unknown_obligation | unmapped | not_assessed | open | met
LegalBasis []string `json:"legal_basis,omitempty"`
CitationSpans string `json:"citation_spans"` // "pending" until the Legal-KG attaches spans
Controls []cgControlDTO `json:"controls"`
Note string `json:"note,omitempty"`
}
// ObligationStatus answers GET /sdk/v1/compliance/obligation-status?obligation_id=...
//
// It NEVER asserts fulfillment automatically. With no evidence collection wired (MVP), a mapped
// obligation is "not_assessed" and every required evidence is "missing" — the honest picture is
// "required vs present evidence", not "a document exists". Fail-closed otherwise:
// - no obligation_id -> 400
// - graph not loaded -> 503
// - id not in the Registry -> 200 overall_status=unknown_obligation
// - mapped but no control yet -> 200 overall_status=unmapped
func (h *ComplianceGraphHandlers) ObligationStatus(c *gin.Context) {
if h.loadErr != nil {
c.JSON(http.StatusServiceUnavailable, gin.H{"error": "compliance graph unavailable", "detail": h.loadErr.Error()})
return
}
obID := strings.TrimSpace(c.Query("obligation_id"))
if obID == "" {
c.JSON(http.StatusBadRequest, gin.H{"error": "obligation_id query parameter required"})
return
}
resp := cgStatusResponse{ObligationID: obID, CitationSpans: "pending", Controls: []cgControlDTO{}}
if h.joins.FindObligation(obID) == nil {
resp.OverallStatus = "unknown_obligation"
resp.Note = "obligation_id not in the Registry join-key contract"
c.JSON(http.StatusOK, resp)
return
}
// MVP: hasEvidence=nil -> no collection wired -> all required evidence counts as missing.
st := ucca.AssessObligationStatus(h.joins, h.mappings, h.evidence, obID, nil)
resp.LegalBasis = st.LegalBasis
if len(st.Controls) == 0 {
resp.OverallStatus = "unmapped"
resp.Note = "no accepted control maps to this obligation yet"
c.JSON(http.StatusOK, resp)
return
}
for _, cs := range st.Controls {
types := make([]string, 0, len(cs.RequiredEvidence))
for _, e := range cs.RequiredEvidence {
types = append(types, e.EvidenceType)
}
resp.Controls = append(resp.Controls, cgControlDTO{
Framework: cs.Framework,
Control: cs.Control,
MappingType: cs.MappingType,
EvidenceRequired: types,
EvidenceStatus: cgEvidenceStatus(len(cs.RequiredEvidence), len(cs.MissingEvidence)),
})
}
// No fulfillment claim without real evidence collection.
resp.OverallStatus = "not_assessed"
resp.Note = "evidence collection not wired (MVP) — fulfillment not asserted"
c.JSON(http.StatusOK, resp)
}
func cgEvidenceStatus(required, missing int) string {
switch {
case required == 0:
return "none_required"
case missing == 0:
return "present"
case missing == required:
return "missing"
default:
return "partial"
}
}
@@ -0,0 +1,133 @@
package handlers
import (
"encoding/json"
"net/http"
"net/http/httptest"
"testing"
"github.com/gin-gonic/gin"
)
func newComplianceGraphTestRouter(t *testing.T) *gin.Engine {
t.Helper()
gin.SetMode(gin.TestMode)
h := NewComplianceGraphHandlers()
if err := h.LoadError(); err != nil {
t.Fatalf("compliance graph failed to load (candidate paths): %v", err)
}
r := gin.New()
h.RegisterRoutes(r.Group("/sdk/v1"))
return r
}
func getObligationStatus(t *testing.T, r *gin.Engine, query string) (int, cgStatusResponse) {
t.Helper()
w := httptest.NewRecorder()
req, _ := http.NewRequest(http.MethodGet, "/sdk/v1/compliance/obligation-status"+query, nil)
r.ServeHTTP(w, req)
var resp cgStatusResponse
if w.Code == http.StatusOK {
if err := json.Unmarshal(w.Body.Bytes(), &resp); err != nil {
t.Fatalf("decode body %q: %v", w.Body.String(), err)
}
}
return w.Code, resp
}
func TestObligationStatus(t *testing.T) {
r := newComplianceGraphTestRouter(t)
tests := []struct {
name string
query string
wantHTTP int
wantOverall string
wantControls bool // expect >=1 control
}{
{"missing param -> 400", "", http.StatusBadRequest, "", false},
{"unknown id -> unknown_obligation", "?obligation_id=does_not_exist", http.StatusOK, "unknown_obligation", false},
{"mapped (OWASP V6) -> not_assessed", "?obligation_id=user_authentication_required", http.StatusOK, "not_assessed", true},
{"NIST adopted (SI-2) -> not_assessed", "?obligation_id=provide_security_updates", http.StatusOK, "not_assessed", true},
{"CORE attack_surface_minimization -> CM-7", "?obligation_id=attack_surface_minimization", http.StatusOK, "not_assessed", true},
{"CORE software_integrity_protection -> SI-7", "?obligation_id=software_integrity_protection", http.StatusOK, "not_assessed", true},
{"in registry, no control -> unmapped", "?obligation_id=sbom_creation", http.StatusOK, "unmapped", false},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
code, resp := getObligationStatus(t, r, tt.query)
if code != tt.wantHTTP {
t.Fatalf("http %d, want %d", code, tt.wantHTTP)
}
if tt.wantHTTP != http.StatusOK {
return
}
if resp.OverallStatus != tt.wantOverall {
t.Errorf("overall_status=%q, want %q", resp.OverallStatus, tt.wantOverall)
}
if tt.wantControls && len(resp.Controls) == 0 {
t.Error("expected >=1 control")
}
if !tt.wantControls && len(resp.Controls) != 0 {
t.Errorf("expected 0 controls, got %d", len(resp.Controls))
}
if resp.CitationSpans != "pending" {
t.Errorf("citation_spans=%q, want pending", resp.CitationSpans)
}
})
}
}
// The MVP must NEVER auto-assert fulfillment: with no evidence collection wired, every required
// evidence is "missing" and the overall status stays "not_assessed".
func TestObligationStatus_NoFulfillmentClaim(t *testing.T) {
r := newComplianceGraphTestRouter(t)
code, resp := getObligationStatus(t, r, "?obligation_id=user_authentication_required")
if code != http.StatusOK {
t.Fatalf("http %d", code)
}
if resp.OverallStatus == "met" || resp.OverallStatus == "erfuellt" {
t.Fatalf("MVP must not assert fulfillment, got overall_status=%q", resp.OverallStatus)
}
for _, ctl := range resp.Controls {
if len(ctl.EvidenceRequired) > 0 && ctl.EvidenceStatus != "missing" {
t.Errorf("control %s/%s evidence_status=%q, want missing (no collection wired)", ctl.Framework, ctl.Control, ctl.EvidenceStatus)
}
}
}
// Pin the curated evidence_required set per NIST obligation. A required:false row silently
// drops from evidence_required, which the table test above (control-count only) would miss.
func TestObligationStatus_NISTEvidenceTypes(t *testing.T) {
r := newComplianceGraphTestRouter(t)
want := map[string][]string{
"attack_surface_minimization": {"config_export", "repo_scan"},
"software_integrity_protection": {"sbom", "config_export"},
"provide_security_updates": {"config_export", "test_report"},
}
for ob, exp := range want {
_, resp := getObligationStatus(t, r, "?obligation_id="+ob)
if len(resp.Controls) != 1 {
t.Fatalf("%s: want 1 control, got %d", ob, len(resp.Controls))
}
if got := resp.Controls[0].EvidenceRequired; !sameStringSet(got, exp) {
t.Errorf("%s evidence_required = %v, want %v", ob, got, exp)
}
}
}
func sameStringSet(a, b []string) bool {
if len(a) != len(b) {
return false
}
m := make(map[string]bool, len(a))
for _, x := range a {
m[x] = true
}
for _, x := range b {
if !m[x] {
return false
}
}
return true
}
+8 -1
View File
@@ -153,6 +153,12 @@ func buildRouter(cfg *config.Config, pool *pgxpool.Pool) *gin.Engine {
ragHandlers := handlers.NewRAGHandlers(corpusVersionStore)
obligationsHandlers := handlers.NewObligationsHandlersWithStore(obligationsStore)
// Compliance Execution Graph (file-backed: Registry join keys + accepted control mappings + evidence)
complianceGraphHandlers := handlers.NewComplianceGraphHandlers()
if err := complianceGraphHandlers.LoadError(); err != nil {
log.Printf("WARNING: compliance graph not loaded (obligation-status -> 503): %v", err)
}
// Regulatory News
allV2Regs, err := ucca.LoadAllV2Regulations()
if err != nil {
@@ -201,7 +207,8 @@ func buildRouter(cfg *config.Config, pool *pgxpool.Pool) *gin.Engine {
uccaHandlers, escalationHandlers, obligationsHandlers, ragHandlers,
roadmapHandlers, workshopHandlers, portfolioHandlers,
academyHandlers, trainingHandlers, whistleblowerHandlers, iaceHandler,
gapHandler, maximizerHandlers, regulatoryNewsHandlers, useCaseHandler)
gapHandler, maximizerHandlers, regulatoryNewsHandlers, useCaseHandler,
complianceGraphHandlers)
return router
}
+2
View File
@@ -30,6 +30,7 @@ func registerRoutes(
maximizerHandlers *handlers.MaximizerHandlers,
regulatoryNewsHandlers *handlers.RegulatoryNewsHandlers,
useCaseHandler *handlers.UseCaseHandler,
complianceGraphHandlers *handlers.ComplianceGraphHandlers,
) {
v1 := router.Group("/sdk/v1")
{
@@ -54,6 +55,7 @@ func registerRoutes(
registerMaximizerRoutes(v1, maximizerHandlers)
registerUseCaseRoutes(v1, useCaseHandler)
v1.GET("/regulatory-news", regulatoryNewsHandlers.GetNews)
complianceGraphHandlers.RegisterRoutes(v1)
}
}
@@ -36,6 +36,10 @@ type DictionarySuggestion struct {
Token string `json:"token"`
Field string `json:"field"`
PatternIDs []string `json:"pattern_ids"`
// SuggestedTags are the RequiredComponentTags shared by the naming patterns,
// ranked by frequency — the candidate tags a keyword_dictionary entry for this
// token would emit so narratives mentioning it can trigger those patterns.
SuggestedTags []string `json:"suggested_tags,omitempty"`
}
type VocabularyReport struct {
@@ -66,14 +66,19 @@ func runVocabulary(form map[string]any) VocabularyReport {
// For each unknown token check if any pattern names it
patterns := iace.AllPatterns()
byID := make(map[string]iace.HazardPattern, len(patterns))
for _, p := range patterns {
byID[p.ID] = p
}
for _, tok := range report.UnknownTokens {
hits := patternsMentioning(tok, patterns)
if len(hits) == 0 {
continue
}
report.SuggestedDictionaryEntries = append(report.SuggestedDictionaryEntries, DictionarySuggestion{
Token: tok,
PatternIDs: hits,
Token: tok,
PatternIDs: hits,
SuggestedTags: suggestTagsFor(hits, byID),
})
}
sort.Slice(report.SuggestedDictionaryEntries, func(i, j int) bool {
@@ -129,18 +134,24 @@ func dictTokenHit(tok string, dict map[string]bool) bool {
return false
}
// patternsMentioning returns up to 8 pattern IDs whose scenario/trigger/
// harm/zone text contains the token (case-insensitive substring).
// patternsMentioning returns up to 8 pattern IDs whose scenario/trigger/harm/
// zone text names the token as a WHOLE WORD. Whole-word (not substring) matching
// is essential: a substring match flags common fragments like "stehen" inside
// "entstehen", producing spurious hits and nonsensical tag suggestions.
func patternsMentioning(tok string, patterns []iace.HazardPattern) []string {
tokLower := strings.ToLower(tok)
seen := map[string]bool{}
var out []string
for _, p := range patterns {
hay := strings.ToLower(p.ScenarioDE + " " + p.TriggerDE + " " + p.HarmDE + " " + p.ZoneDE + " " + p.NameDE)
if !strings.Contains(hay, tokLower) {
continue
matched := false
for _, w := range tokenRE.FindAllString(hay, -1) {
if w == tokLower {
matched = true
break
}
}
if seen[p.ID] {
if !matched || seen[p.ID] {
continue
}
seen[p.ID] = true
@@ -151,3 +162,57 @@ func patternsMentioning(tok string, patterns []iace.HazardPattern) []string {
}
return out
}
// suggestTagsFor returns the RequiredComponentTags shared across the naming
// patterns, ranked by how many of them require each tag (ties broken by name),
// top 3. These are the candidate tags a dictionary entry for the token should
// emit so a narrative mentioning the token can trigger those patterns.
func suggestTagsFor(ids []string, byID map[string]iace.HazardPattern) []string {
freq := map[string]int{}
total := 0
for _, id := range ids {
p, ok := byID[id]
if !ok {
continue
}
total++
seen := map[string]bool{}
for _, tag := range p.RequiredComponentTags {
if seen[tag] {
continue
}
seen[tag] = true
freq[tag]++
}
}
if total == 0 {
return nil
}
type tf struct {
tag string
n int
}
ranked := make([]tf, 0, len(freq))
for t, n := range freq {
ranked = append(ranked, tf{t, n})
}
sort.Slice(ranked, func(i, j int) bool {
if ranked[i].n != ranked[j].n {
return ranked[i].n > ranked[j].n
}
return ranked[i].tag < ranked[j].tag
})
// Only suggest a tag shared by >= 40% of the naming patterns. Diffuse tokens
// (common verbs spread across categories) get no dominant tag and are dropped.
var out []string
for _, x := range ranked {
if float64(x.n)/float64(total) < 0.4 {
break
}
out = append(out, x.tag)
if len(out) >= 3 {
break
}
}
return out
}
@@ -0,0 +1,36 @@
package audit
import (
"testing"
"github.com/breakpilot/ai-compliance-sdk/internal/iace"
)
func TestSuggestTagsFor_RanksSharedRequiredTags(t *testing.T) {
byID := map[string]iace.HazardPattern{
"P1": {ID: "P1", RequiredComponentTags: []string{"backflow_risk", "dom_warewashing"}},
"P2": {ID: "P2", RequiredComponentTags: []string{"backflow_risk"}},
"P3": {ID: "P3", RequiredComponentTags: []string{"sharp_edge"}},
}
got := suggestTagsFor([]string{"P1", "P2", "P3"}, byID)
if len(got) == 0 || got[0] != "backflow_risk" {
t.Fatalf("want backflow_risk ranked first (2 patterns), got %v", got)
}
}
func TestSuggestTagsFor_TopThreeStableAlpha(t *testing.T) {
byID := map[string]iace.HazardPattern{
"P1": {ID: "P1", RequiredComponentTags: []string{"d", "b", "a", "c"}},
}
got := suggestTagsFor([]string{"P1"}, byID)
if len(got) != 3 || got[0] != "a" || got[1] != "b" || got[2] != "c" {
t.Fatalf("want stable alpha top-3 [a b c], got %v", got)
}
}
func TestSuggestTagsFor_UnknownPatternIgnored(t *testing.T) {
byID := map[string]iace.HazardPattern{}
if got := suggestTagsFor([]string{"missing"}, byID); len(got) != 0 {
t.Fatalf("want empty for unknown patterns, got %v", got)
}
}
@@ -7,8 +7,6 @@ import (
"path/filepath"
"sort"
"testing"
"github.com/google/uuid"
)
// TestKistenhub_GTCoverage runs the Kistenhubgeraet ground truth (37 entries)
@@ -110,65 +108,6 @@ func TestKistenhub_GTCoverage(t *testing.T) {
// patternsToHazardsAndMitigations converts a pattern match output into the
// Hazard/Mitigation shapes that CompareBenchmark expects. Mirrors what
// iace_handler_init.go does in production but without DB writes.
func patternsToHazardsAndMitigations(out *MatchOutput) ([]Hazard, []Mitigation) {
hazards := make([]Hazard, 0, len(out.MatchedPatterns))
patternToHazard := make(map[string]uuid.UUID, len(out.MatchedPatterns))
for _, pm := range out.MatchedPatterns {
cat := ""
if len(pm.HazardCats) > 0 {
cat = pm.HazardCats[0]
}
zone := pm.ZoneDE
lifecycle := ""
if len(pm.ApplicableLifecycles) > 0 {
lifecycle = pm.ApplicableLifecycles[0]
}
h := Hazard{
ID: uuid.New(),
Name: pm.ScenarioDE,
Category: cat,
Description: pm.ScenarioDE,
Scenario: pm.ScenarioDE,
TriggerEvent: pm.TriggerDE,
PossibleHarm: pm.HarmDE,
AffectedPerson: pm.AffectedDE,
HazardousZone: zone,
LifecyclePhase: lifecycle,
}
if h.Name == "" {
h.Name = pm.PatternName
}
hazards = append(hazards, h)
patternToHazard[pm.PatternID] = h.ID
}
measureNames := make(map[string]string)
for _, m := range GetProtectiveMeasureLibrary() {
measureNames[m.ID] = m.Name
}
var mitigations []Mitigation
for _, sm := range out.SuggestedMeasures {
name := measureNames[sm.MeasureID]
if name == "" {
name = sm.MeasureID
}
for _, srcPattern := range sm.SourcePatterns {
hid, ok := patternToHazard[srcPattern]
if !ok {
continue
}
mitigations = append(mitigations, Mitigation{
ID: uuid.New(),
HazardID: hid,
Name: name,
})
}
}
return hazards, mitigations
}
func abbrev(s string, max int) string {
if len(s) <= max {
return s
@@ -1,6 +1,7 @@
package iace
import (
"context"
"encoding/json"
"os"
"path/filepath"
@@ -45,7 +46,7 @@ var warewashingCyberCategories = map[string]bool{
// warewashingEngineOutput runs the production chain and returns the filtered
// hazards/mitigations the user would see for the UC-M.
func warewashingEngineOutput() ([]Hazard, []Mitigation, int) {
func warewashingEngineOutput() ([]Hazard, []Mitigation, []PatternMatch) {
res := ParseNarrative(warewashingNarrative, "Gewerbliche Untertisch-Geschirrspuelmaschine (vernetzt)")
var compIDs, compNames []string
@@ -94,7 +95,7 @@ func warewashingEngineOutput() ([]Hazard, []Mitigation, int) {
filtered := *out
filtered.MatchedPatterns = kept
hazards, mitigations := patternsToHazardsAndMitigations(&filtered)
return hazards, mitigations, len(kept)
return hazards, mitigations, kept
}
func TestWarewashing_GTCoverage(t *testing.T) {
@@ -119,8 +120,8 @@ func TestWarewashing_GTCoverage(t *testing.T) {
t.Logf("Parsed components: %v", cn)
}
hazards, mitigations, nPatterns := warewashingEngineOutput()
t.Logf("Engine: %d patterns kept (relevance+cyber filter) -> %d hazards", nPatterns, len(hazards))
hazards, mitigations, keptPatterns := warewashingEngineOutput()
t.Logf("Engine: %d patterns kept (relevance+cyber filter) -> %d hazards", len(keptPatterns), len(hazards))
result := CompareBenchmark(&gt, hazards, mitigations)
precision := 0.0
@@ -180,3 +181,57 @@ func TestWarewashing_GTCoverage(t *testing.T) {
t.Errorf("warewashing recall below 40%% floor: %.1f%%", result.CoverageScore*100)
}
}
// TestWarewashing_DedupProposer exercises the offline dedup-candidate proposer
// end-to-end on the real warewashing engine output: detect candidates, screen
// each against the GT, and log the human-review queue. It asserts the WALL is
// self-consistent — a PASS verdict may never coincide with a recall drop.
func TestWarewashing_DedupProposer(t *testing.T) {
raw, err := os.ReadFile(filepath.Join("testdata", "ground_truth_warewashing.json"))
if err != nil {
t.Fatalf("read GT: %v", err)
}
var gt GroundTruth
if err := json.Unmarshal(raw, &gt); err != nil {
t.Fatalf("parse GT: %v", err)
}
hazards, mits, kept := warewashingEngineOutput()
byID := map[string]PatternMatch{}
for _, pm := range kept {
byID[pm.PatternID] = pm
}
// 0.25 is a deliberately permissive candidate threshold: the proposer is meant
// to over-surface, because the deterministic GT wall below (and a human, and the
// LLM judge) is the precision filter — not the detector.
candidates := FindDedupCandidates(kept, 0.25)
t.Logf("Proposer: %d dedup candidate(s) from %d fired patterns", len(candidates), len(kept))
// Deterministic judge in the test; the dev-time CLI swaps in LLMJudge.
judge := HeuristicJudge{}
var judged []JudgedProposal
blocked := 0
for _, c := range candidates {
sr := ScreenSupersession(&gt, hazards, mits, c.KeepHazardName, c.DropName)
switch {
case sr.RecallAfter < sr.RecallBefore:
t.Logf("[BLOCK recall-load-bearing] keep %s / drop %s", c.KeepPattern, c.DropPattern)
blocked++
case sr.DistinctGT:
t.Logf("[BLOCK distinct GT %s vs %s] keep %s / drop %s", sr.KeepGT, sr.DropGT, c.KeepPattern, c.DropPattern)
blocked++
default:
if !sr.Safe {
t.Errorf("RECALL-SAFE branch but ScreenResult.Safe=false for drop %s", c.DropPattern)
}
v, conf, rat := judge.Judge(context.Background(), c, byID[c.KeepPattern], byID[c.DropPattern])
judged = append(judged, JudgedProposal{
Candidate: c, Screen: sr, Verdict: v, Confidence: conf, Rationale: rat, Judge: judge.Name(),
})
}
}
t.Logf("\n%s", RenderProposalQueue("Gewerbliche Geschirrspuelmaschine (vernetzt)", judged))
t.Logf("Proposer summary: %d candidate(s) in queue (judge=%s), %d BLOCKED by the GT wall — propose-only, nothing auto-applied",
len(judged), judge.Name(), blocked)
}
@@ -0,0 +1,50 @@
package iace
import "sort"
// EN ISO 12100 hazard-group ordering for the hazard log. Without it the log is
// returned in pattern-firing order, which reads as a jumble. This groups the
// hazards top-down by type (A. Mechanisch, B. Elektrisch, C. Thermisch, …),
// matching the frontend CATEGORY_LABELS.
var isoCategoryRank = map[string]int{
// A. Mechanisch
"mechanical_hazard": 10, "mechanical": 10, "maintenance_hazard": 11,
// B. Elektrisch
"electrical_hazard": 20, "electrical": 20, "emc_hazard": 21,
// C. Thermisch
"thermal_hazard": 30, "thermal": 30, "high_temperature": 31, "fire_explosion": 32,
// D. Pneumatik / Hydraulik
"pneumatic_hydraulic": 40,
// E. Laerm / Vibration
"noise_hazard": 50, "noise_vibration": 50, "vibration_hazard": 51,
// F. Ergonomie
"ergonomic_hazard": 60, "ergonomic": 60,
// G. Stoffe / Umwelt
"material_environmental": 70, "chemical_risk": 71, "radiation_hazard": 72,
// H. Software / Steuerung (funktionale Sicherheit)
"software_control": 80, "software_fault": 80, "safety_function_failure": 81,
"configuration_error": 82, "sensor_fault": 83, "hmi_error": 84, "mode_confusion": 85,
"communication_failure": 86, "update_failure": 87,
// I. Cyber / Netzwerk (zur Ordnungs-Vollstaendigkeit; im CE-Log ausgeschlossen)
"unauthorized_access": 90, "firmware_corruption": 91, "cyber_resilience": 92,
"cyber_network": 93, "logging_audit_failure": 94, "sensor_spoofing": 95,
// J. KI-spezifisch
"ai_specific": 100, "ai_misclassification": 100, "false_classification": 100,
"model_drift": 100, "data_poisoning": 100, "unintended_bias": 100,
}
func categoryRank(cat string) int {
if r, ok := isoCategoryRank[cat]; ok {
return r
}
return 999 // unknown categories last
}
// SortHazardsByISO12100 groups hazards by ISO 12100 hazard group. Stable: the
// relative order within a group (creation/priority order from the engine) is
// preserved.
func SortHazardsByISO12100(hazards []Hazard) {
sort.SliceStable(hazards, func(i, j int) bool {
return categoryRank(hazards[i].Category) < categoryRank(hazards[j].Category)
})
}
@@ -157,7 +157,7 @@ func GetGTBremseHazardPatterns() []HazardPattern {
// ════════════════════════════════════════════════════════════════
{
ID: "HP1717", NameDE: "Verletzung durch unvermittelt austretende pneumatische Restenergie", NameEN: "Injury from unexpectedly released pneumatic stored energy",
RequiredComponentTags: []string{"stored_energy"},
RequiredComponentTags: []string{"pneumatic_part"},
RequiredEnergyTags: []string{"pneumatic_pressure"},
GeneratedHazardCats: []string{"mechanical_hazard"},
SuggestedMeasureIDs: []string{"M485", "M534", "M527"},
@@ -375,7 +375,7 @@ func GetSpecificMachinePatterns() []HazardPattern {
// ================================================================
{
ID: "HP753", NameDE: "Thermal Runaway bei Lithium-Batterie", NameEN: "Thermal runaway of lithium battery",
RequiredComponentTags: []string{"stored_energy", "high_temperature"},
RequiredComponentTags: []string{"battery", "high_temperature"},
RequiredEnergyTags: []string{"electrical_energy", "thermal"},
GeneratedHazardCats: []string{"thermal_hazard", "electrical_hazard"},
SuggestedMeasureIDs: []string{"M005", "M141"},
@@ -390,7 +390,7 @@ func GetSpecificMachinePatterns() []HazardPattern {
},
{
ID: "HP754", NameDE: "Ausgasung giftiger Daempfe aus Batterie", NameEN: "Toxic gas emission from battery",
RequiredComponentTags: []string{"stored_energy", "chemical_risk"},
RequiredComponentTags: []string{"battery", "chemical_risk"},
RequiredEnergyTags: []string{},
GeneratedHazardCats: []string{"material_environmental"},
SuggestedMeasureIDs: []string{"M005", "M141"},
@@ -405,7 +405,7 @@ func GetSpecificMachinePatterns() []HazardPattern {
},
{
ID: "HP755", NameDE: "Elektrischer Schlag an Hochvolt-Batteriespeicher", NameEN: "Electric shock from high-voltage battery storage",
RequiredComponentTags: []string{"stored_energy", "electrical_part"},
RequiredComponentTags: []string{"battery", "electrical_part"},
RequiredEnergyTags: []string{"electrical_energy"},
GeneratedHazardCats: []string{"electrical_hazard"},
SuggestedMeasureIDs: []string{"M082", "M141"},
@@ -137,7 +137,7 @@ func GetKeywordDictionary() []KeywordEntry {
{Keywords: []string{"kreiselmaeher", "scheibenmaeher", "maehwerk"}, ExtraTags: []string{"agri_mower"}},
{Keywords: []string{"spruehduese", "spritzduese", "spruehkopf"}, ExtraTags: []string{"spray_nozzle"}},
{Keywords: []string{"galvanikbad", "tauchbad", "beizbad", "chemiebad"}, ExtraTags: []string{"chemical_bath"}},
{Keywords: []string{"batterie", "akku", "akkumulator", "traktionsbatterie"}, ExtraTags: []string{"battery"}},
{Keywords: []string{"batterie", "akku", "akkumulator", "traktionsbatterie", "lithium", "batteriespeicher", "hochvoltbatterie", "lithium-batterie"}, ExtraTags: []string{"battery"}},
{Keywords: []string{"heizelement", "heizpatrone", "heizband"}, ExtraTags: []string{"heating_element"}},
{Keywords: []string{"uv-lampe", "uv-strahler", "uv-c-strahler"}, ExtraTags: []string{"uv_source"}},
{Keywords: []string{"roentgen", "radioaktiv", "strahlenquelle", "gammastrahl", "isotop"}, ExtraTags: []string{"radiation_source"}},
@@ -42,3 +42,29 @@ func guardedLifecycles(p HazardPattern, tagSet map[string]bool) []string {
}
return p.ApplicableLifecycles
}
// Domain-specific supersession.
//
// A generic pattern that fires via a broad tag (e.g. high_temperature) can
// duplicate a domain-specific pattern that describes the same hazard more
// precisely. When the domain is present, the specific pattern wins and the
// generic duplicate is dropped. Scoped to the domain tag, so machines outside
// the domain keep the generic pattern — regression-safe by construction.
//
// HP016 (generic hot surfaces) -> HP2201 (Boiler/Tank/Spuelkammer)
// HP018 (actuator burn) -> HP2201 (same contact-burn hazard)
// HP013 (stored electrical NRG) -> HP144 (residual voltage; HP013's zone is
// framed for Batteriefaecher/USV-Anlagen a
// dishwasher does not have, HP144 is the
// Frequenzumrichter/Zwischenkreis variant)
var genericSupersededByWarewashing = map[string]bool{
"HP016": true,
"HP018": true,
"HP013": true,
}
// supersededByDomainSpecific reports whether a generic pattern is replaced by a
// more precise equivalent that the project's domain already provides.
func supersededByDomainSpecific(p HazardPattern, tagSet map[string]bool) bool {
return tagSet["dom_warewashing"] && genericSupersededByWarewashing[p.ID]
}
@@ -416,6 +416,11 @@ func patternMatches(p HazardPattern, tagSet map[string]bool, input MatchInput) b
return false
}
// Domain-specific supersession (generic duplicate replaced by a precise one).
if supersededByDomainSpecific(p, tagSet) {
return false
}
return true
}
@@ -0,0 +1,143 @@
package iace
import (
"context"
"encoding/json"
"fmt"
"strings"
)
// Coverage blind-spot proposer (P2 slice 6, type 4). DEV-TIME, propose-only.
//
// Deterministic skeleton: which EN ISO 12100 hazard groups (A-G, the classic CE
// groups; H-J are control/CRA and routinely routed elsewhere) did the engine
// leave with ZERO hazards for this machine? An empty group is a structural
// blind-spot signal — the machine may genuinely lack that hazard, or a pattern
// may be missing. The LLM then expands each gap into specific expected-but-missing
// hazards a safety assessor would name, for a human to confirm into a new pattern
// or GT case. The gaps alone are useful without any model.
type isoGroup struct {
Key string
Label string
Cats []string
}
var iso12100Groups = []isoGroup{
{"mechanical", "A. Mechanisch", []string{"mechanical_hazard", "mechanical", "maintenance_hazard"}},
{"electrical", "B. Elektrisch", []string{"electrical_hazard", "electrical", "emc_hazard"}},
{"thermal", "C. Thermisch", []string{"thermal_hazard", "thermal", "high_temperature", "fire_explosion"}},
{"pneumatic_hydraulic", "D. Pneumatik/Hydraulik", []string{"pneumatic_hydraulic"}},
{"noise_vibration", "E. Laerm/Vibration", []string{"noise_hazard", "noise_vibration", "vibration_hazard"}},
{"ergonomic", "F. Ergonomie", []string{"ergonomic_hazard", "ergonomic"}},
{"material", "G. Stoffe/Umwelt", []string{"material_environmental", "chemical_risk", "radiation_hazard"}},
}
// CoverageGap is an ISO 12100 hazard group with no engine hazard.
type CoverageGap struct {
Group string `json:"group"`
Key string `json:"key"`
Note string `json:"note"`
}
// FindCoverageGaps returns the A-G hazard groups that produced zero hazards.
func FindCoverageGaps(hazards []Hazard) []CoverageGap {
present := make(map[string]bool, len(hazards))
for _, h := range hazards {
present[h.Category] = true
}
var gaps []CoverageGap
for _, g := range iso12100Groups {
covered := false
for _, c := range g.Cats {
if present[c] {
covered = true
break
}
}
if !covered {
gaps = append(gaps, CoverageGap{
Group: g.Label, Key: g.Key,
Note: "no engine hazard in this ISO 12100 group — verify the machine truly lacks it, or a pattern is missing",
})
}
}
return gaps
}
// MissingHazard is an LLM-proposed hazard a safety assessor would expect.
type MissingHazard struct {
Group string `json:"group"`
Hazard string `json:"hazard"`
Why string `json:"why"`
}
// ProposeMissingHazards asks the LLM to expand the empty groups into specific
// expected hazards. Returns nil without a completer or on any error — propose-only,
// never breaks the run.
func ProposeMissingHazards(ctx context.Context, completer LLMCompleter, machineClass, narrative string, produced []Hazard, gaps []CoverageGap) []MissingHazard {
if completer == nil || len(gaps) == 0 {
return nil
}
system, user := BuildCoveragePrompt(machineClass, narrative, produced, gaps)
raw, err := completer.Complete(ctx, system, user)
if err != nil {
return nil
}
return parseMissingHazards(raw)
}
// BuildCoveragePrompt frames the "what is missing?" question for the LLM.
func BuildCoveragePrompt(machineClass, narrative string, produced []Hazard, gaps []CoverageGap) (system, user string) {
system = "Du bist Sachverstaendiger fuer Maschinensicherheit nach EN ISO 12100. " +
"Dir werden eine Maschine, die bereits erkannten Gefaehrdungen und Gefaehrdungsgruppen OHNE Eintrag genannt. " +
"Nenne nur Gefaehrdungen, die ein Sachverstaendiger fuer DIESE Maschine ERWARTET, die aber FEHLEN. " +
"Erfinde nichts Maschinenfremdes. Antworte AUSSCHLIESSLICH als JSON-Array: " +
`[{"group":"...","hazard":"...","why":"..."}].`
var have []string
seen := map[string]bool{}
for _, h := range produced {
if h.Category != "" && !seen[h.Category] {
seen[h.Category] = true
have = append(have, h.Category)
}
}
var empty []string
for _, g := range gaps {
empty = append(empty, g.Group)
}
user = fmt.Sprintf("Maschinenklasse: %s\n\nBeschreibung:\n%s\n\nBereits erkannte Kategorien: %s\n\nGruppen OHNE Eintrag (Fokus): %s\n\nWelche erwarteten Gefaehrdungen fehlen?",
machineClass, narrative, strings.Join(have, ", "), strings.Join(empty, ", "))
return system, user
}
func parseMissingHazards(raw string) []MissingHazard {
start, end := strings.Index(raw, "["), strings.LastIndex(raw, "]")
if start < 0 || end <= start {
return nil
}
var out []MissingHazard
if err := json.Unmarshal([]byte(raw[start:end+1]), &out); err != nil {
return nil
}
return out
}
// RenderCoverageQueue renders the deterministic gaps plus any LLM-proposed missing
// hazards as a markdown review queue.
func RenderCoverageQueue(machine string, gaps []CoverageGap, missing []MissingHazard) string {
var b strings.Builder
fmt.Fprintf(&b, "# Coverage blind-spot queue — %s\n\n", machine)
fmt.Fprintf(&b, "%d ISO 12100 group(s) (A-G) have no engine hazard. Propose-only — a human confirms whether the machine truly lacks it or a pattern/GT case is missing.\n\n", len(gaps))
for _, g := range gaps {
fmt.Fprintf(&b, "- **%s** — %s\n", g.Group, g.Note)
}
if len(missing) > 0 {
fmt.Fprintf(&b, "\n## LLM-proposed expected-but-missing hazards (%d)\n\n", len(missing))
for i, m := range missing {
fmt.Fprintf(&b, "%d. [%s] %s\n - why: %s\n", i+1, m.Group, m.Hazard, m.Why)
}
}
return b.String()
}
@@ -0,0 +1,59 @@
package iace
import (
"context"
"strings"
"testing"
)
func TestFindCoverageGaps(t *testing.T) {
hazards := []Hazard{
{Category: "mechanical_hazard"},
{Category: "thermal_hazard"},
{Category: "electrical_hazard"},
{Category: "material_environmental"},
}
gapKeys := map[string]bool{}
for _, g := range FindCoverageGaps(hazards) {
gapKeys[g.Key] = true
}
for _, want := range []string{"pneumatic_hydraulic", "noise_vibration", "ergonomic"} {
if !gapKeys[want] {
t.Errorf("expected gap %s", want)
}
}
for _, notWant := range []string{"mechanical", "thermal", "electrical", "material"} {
if gapKeys[notWant] {
t.Errorf("did not expect gap %s (covered)", notWant)
}
}
}
func TestBuildCoveragePrompt_ContainsContext(t *testing.T) {
produced := []Hazard{{Category: "thermal_hazard"}}
gaps := []CoverageGap{{Group: "F. Ergonomie", Key: "ergonomic"}}
system, user := BuildCoveragePrompt("Geschirrspuelmaschine", "Eine Spuelmaschine mit Tank.", produced, gaps)
if !strings.Contains(system, "EN ISO 12100") || !strings.Contains(system, "JSON") {
t.Errorf("system prompt missing framing")
}
for _, want := range []string{"Geschirrspuelmaschine", "thermal_hazard", "F. Ergonomie", "Spuelmaschine mit Tank"} {
if !strings.Contains(user, want) {
t.Errorf("user prompt missing %q", want)
}
}
}
func TestProposeMissingHazards_ParsesAndDegrades(t *testing.T) {
gaps := []CoverageGap{{Group: "F. Ergonomie", Key: "ergonomic"}}
c := fakeCompleter{out: `Hier: [{"group":"F. Ergonomie","hazard":"Heben schwerer Koerbe","why":"manuelles Beladen"}] fertig`}
got := ProposeMissingHazards(context.Background(), c, "x", "n", nil, gaps)
if len(got) != 1 || got[0].Hazard != "Heben schwerer Koerbe" {
t.Fatalf("parse: got %+v", got)
}
if ProposeMissingHazards(context.Background(), nil, "x", "n", nil, gaps) != nil {
t.Errorf("nil completer must return nil")
}
if ProposeMissingHazards(context.Background(), fakeCompleter{err: context.DeadlineExceeded}, "x", "n", nil, gaps) != nil {
t.Errorf("error must return nil")
}
}
@@ -0,0 +1,152 @@
package iace
import (
"fmt"
"math"
"regexp"
"sort"
"strings"
)
// Offline dedup-candidate proposer (P2, type 1). DEV-TIME ONLY.
//
// It inspects the patterns that fired for one machine and proposes which look
// like duplicates, so a human (later an LLM) can decide a supersession/merge. It
// NEVER mutates the pattern library or the runtime — it only surfaces candidates.
// The deterministic GT screen (ScreenSupersession, proposer_screen.go) is the
// wall that proves a proposal is safe before a human ever sees it.
//
// Detection here is purely structural (category + zone + measure + scenario
// overlap) and therefore reproducible. Two safety rules bake in what P1 taught
// us about the dishwasher review:
// - only patterns with the SAME primary category are ever compared;
// - a pair with DIFFERENT operational states is NEVER proposed, because
// normal-operation and maintenance are legitimately distinct contexts with
// different protective measures (e.g. HP011 vs HP077). Merging them would
// erase the maintenance view.
// DedupCandidate is a proposed near-duplicate pattern pair for one machine class.
type DedupCandidate struct {
KeepPattern string `json:"keep_pattern"` // higher-priority survivor
DropPattern string `json:"drop_pattern"` // supersession target
KeepName string `json:"keep_name"`
KeepHazardName string `json:"keep_hazard_name"` // keep pattern ScenarioDE (for the GT-distinctness screen)
DropName string `json:"drop_name"` // == generated hazard Name (ScenarioDE) of the drop pattern
Category string `json:"category"`
ZoneJaccard float64 `json:"zone_jaccard"`
MeasureJaccard float64 `json:"measure_jaccard"`
ScenarioJaccard float64 `json:"scenario_jaccard"`
Score float64 `json:"score"`
Rationale string `json:"rationale"`
}
// FindDedupCandidates compares the fired patterns pairwise and returns near-dup
// candidates whose combined overlap score meets threshold, deterministically
// ordered (score desc, then drop-pattern id). The combined score weights measure
// overlap highest (shared measures are the strongest duplicate signal), then zone
// and scenario equally.
func FindDedupCandidates(fired []PatternMatch, threshold float64) []DedupCandidate {
var out []DedupCandidate
for i := 0; i < len(fired); i++ {
for j := i + 1; j < len(fired); j++ {
a, b := fired[i], fired[j]
ca := primaryCat(a)
if ca == "" || ca != primaryCat(b) {
continue
}
if !sameOpStateSet(a.OperationalStates, b.OperationalStates) {
continue // legitimate lifecycle variants — never propose a merge
}
zj := tokenJaccard(zoneTokenSet(a.ZoneDE), zoneTokenSet(b.ZoneDE))
mj := tokenJaccard(toSet(a.SuggestedMeasureIDs), toSet(b.SuggestedMeasureIDs))
sj := tokenJaccard(wordTokenSet(a.ScenarioDE), wordTokenSet(b.ScenarioDE))
score := 0.4*mj + 0.3*zj + 0.3*sj
if score < threshold {
continue
}
keep, drop := a, b
if b.Priority > a.Priority {
keep, drop = b, a
}
out = append(out, DedupCandidate{
KeepPattern: keep.PatternID, DropPattern: drop.PatternID,
KeepName: keep.PatternName, KeepHazardName: keep.ScenarioDE, DropName: drop.ScenarioDE,
Category: ca, ZoneJaccard: round2(zj), MeasureJaccard: round2(mj),
ScenarioJaccard: round2(sj), Score: round2(score),
Rationale: fmt.Sprintf(
"same category %q · measure overlap %.0f%% · zone overlap %.0f%% · scenario overlap %.0f%% → keep %s (P%d), supersede %s (P%d)",
ca, mj*100, zj*100, sj*100, keep.PatternID, keep.Priority, drop.PatternID, drop.Priority),
})
}
}
sort.SliceStable(out, func(i, j int) bool {
if out[i].Score != out[j].Score {
return out[i].Score > out[j].Score
}
return out[i].DropPattern < out[j].DropPattern
})
return out
}
func primaryCat(pm PatternMatch) string {
if len(pm.HazardCats) == 0 {
return ""
}
return pm.HazardCats[0]
}
func sameOpStateSet(a, b []string) bool {
sa, sb := toSet(a), toSet(b)
if len(sa) != len(sb) {
return false
}
for k := range sa {
if !sb[k] {
return false
}
}
return true
}
var proposerWordSplit = regexp.MustCompile(`[^\p{L}]+`)
// zoneTokenSet splits a comma-separated zone string into its component terms.
func zoneTokenSet(zone string) map[string]bool {
out := map[string]bool{}
for _, part := range strings.Split(strings.ToLower(zone), ",") {
if t := strings.TrimSpace(part); len([]rune(t)) >= 3 {
out[t] = true
}
}
return out
}
// wordTokenSet tokenises free text into words of length >= 4 (drops connectives).
func wordTokenSet(s string) map[string]bool {
out := map[string]bool{}
for _, w := range proposerWordSplit.Split(strings.ToLower(s), -1) {
if len([]rune(w)) >= 4 {
out[w] = true
}
}
return out
}
func tokenJaccard(a, b map[string]bool) float64 {
if len(a) == 0 && len(b) == 0 {
return 0
}
inter := 0
for k := range a {
if b[k] {
inter++
}
}
union := len(a) + len(b) - inter
if union == 0 {
return 0
}
return float64(inter) / float64(union)
}
func round2(x float64) float64 { return math.Round(x*100) / 100 }
@@ -0,0 +1,67 @@
package iace
import "testing"
func mkPM(id, cat, zone, scenario string, prio int, measures, opstates []string) PatternMatch {
return PatternMatch{
PatternID: id, PatternName: id, Priority: prio,
HazardCats: []string{cat}, ZoneDE: zone, ScenarioDE: scenario,
SuggestedMeasureIDs: measures, OperationalStates: opstates,
}
}
func TestFindDedupCandidates_FindsOverlappingPair(t *testing.T) {
fired := []PatternMatch{
mkPM("HPa", "update_failure", "Steuerung, SPS", "Software-Update der Steuerung scheitert nach Abbruch", 80,
[]string{"M138", "M146"}, nil),
mkPM("HPb", "update_failure", "Steuerung, Antriebsregler", "Software-Update der Steuerung schlaegt fehl", 75,
[]string{"M138", "M146", "M141"}, nil),
mkPM("HPc", "mechanical_hazard", "Tuer", "Quetschen der Finger an der Tuer", 70,
[]string{"M003"}, nil),
}
got := FindDedupCandidates(fired, 0.4)
if len(got) != 1 {
t.Fatalf("want 1 candidate, got %d: %+v", len(got), got)
}
// Higher-priority pattern survives, lower one is the drop target.
if got[0].KeepPattern != "HPa" || got[0].DropPattern != "HPb" {
t.Errorf("want keep HPa / drop HPb, got keep %s / drop %s", got[0].KeepPattern, got[0].DropPattern)
}
if got[0].DropName != "Software-Update der Steuerung schlaegt fehl" {
t.Errorf("DropName must equal drop pattern ScenarioDE, got %q", got[0].DropName)
}
}
func TestFindDedupCandidates_LifecycleGuard(t *testing.T) {
// Same category, zone and measures — but normal-operation vs maintenance.
// These are legitimate variants (HP011 vs HP077) and must NOT be proposed.
fired := []PatternMatch{
mkPM("HP011", "electrical_hazard", "Schaltschrank, Klemmenkasten", "Person beruehrt spannungsfuehrende Teile", 95,
[]string{"M481", "M482"}, nil),
mkPM("HP077", "electrical_hazard", "Schaltschrank, Klemmenkasten", "Person beruehrt spannungsfuehrende Teile", 80,
[]string{"M481", "M482"}, []string{"maintenance"}),
}
if got := FindDedupCandidates(fired, 0.4); len(got) != 0 {
t.Fatalf("lifecycle guard failed: want 0 candidates, got %d: %+v", len(got), got)
}
}
func TestFindDedupCandidates_DifferentCategoryIgnored(t *testing.T) {
fired := []PatternMatch{
mkPM("HPa", "thermal_hazard", "Boiler", "Heisse Oberflaeche am Boiler", 80, []string{"M071"}, nil),
mkPM("HPb", "mechanical_hazard", "Boiler", "Heisse Oberflaeche am Boiler", 80, []string{"M071"}, nil),
}
if got := FindDedupCandidates(fired, 0.3); len(got) != 0 {
t.Fatalf("cross-category pair must not be proposed, got %d", len(got))
}
}
func TestFindDedupCandidates_BelowThresholdDropped(t *testing.T) {
fired := []PatternMatch{
mkPM("HPa", "mechanical_hazard", "Tuer", "Quetschen an der Tuer", 80, []string{"M003"}, nil),
mkPM("HPb", "mechanical_hazard", "Foerderband", "Einzug am Foerderband", 80, []string{"M540"}, nil),
}
if got := FindDedupCandidates(fired, 0.4); len(got) != 0 {
t.Fatalf("disjoint pair must be below threshold, got %d: %+v", len(got), got)
}
}
@@ -0,0 +1,154 @@
package iace
import (
"fmt"
"sort"
"strings"
)
// Foreign-framing proposer (P2 slice 4, type 2). DEV-TIME, propose-only.
//
// A pattern can fire for a machine yet describe its hazard with a zone text
// framed for a DIFFERENT machine (e.g. a dishwasher hazard whose zone names
// "Walzen, Transportbaender" or "Bearbeitungszone"). Such foreign framing leaks
// through terms that are NOT yet in domainGateTerms — once a term is a gate term,
// the ghost-pattern invariant already fences the pattern out. So we surface the
// candidates structurally: zone terms a fired pattern names that the machine's
// narrative never mentions (minus generic hazard-location vocabulary). A human
// (or the LLM) then decides: add a dom_* gate term, or re-frame the zone text.
//
// This OVER-surfaces by design — the human/LLM is the precision filter, not the
// detector (same contract as the dedup proposer).
// genericHazardStop are hazard-LOCATION words that legitimately appear in zones
// without being echoed in a narrative — they are not evidence of foreign framing.
var genericHazardStop = map[string]bool{
"quetschstelle": true, "einzugstelle": true, "einzugsstelle": true, "scherstelle": true,
"schneidstelle": true, "stossstelle": true, "fangstelle": true, "klemmstelle": true,
"gefahrbereich": true, "gefahrenbereich": true, "gefahrstelle": true, "gefahrenstelle": true,
"arbeitsbereich": true, "wirkbereich": true, "schutzbereich": true, "umgebung": true,
"bereich": true, "zugang": true, "oberflaeche": true, "oberflaechen": true,
"gehaeuse": true, "bauteil": true, "bauteile": true, "komponente": true, "maschine": true,
}
// FramingCandidate is a fired pattern whose zone text looks foreign for the machine.
type FramingCandidate struct {
Pattern string `json:"pattern"`
Name string `json:"name"`
Category string `json:"category"`
Zone string `json:"zone"`
OrphanTerms []string `json:"orphan_terms"`
OrphanFraction float64 `json:"orphan_fraction"`
Verdict string `json:"verdict"` // heuristic lean: foreign | plausible
Evidence string `json:"evidence"`
}
// FindFramingCandidates returns fired patterns whose zone is mostly not echoed in
// the narrative, sorted by orphan fraction descending (deterministic).
func FindFramingCandidates(fired []PatternMatch, narrative string, minFraction float64) []FramingCandidate {
nar := strings.ToLower(narrative)
var narStems []string
for _, w := range proposerWordSplit.Split(nar, -1) {
if len([]rune(w)) >= 5 {
narStems = append(narStems, w)
}
}
var out []FramingCandidate
for _, pm := range fired {
parts := zoneParts(pm.ZoneDE)
if len(parts) == 0 {
continue
}
var orphans []string
for _, p := range parts {
if !partEchoed(p, nar, narStems) {
orphans = append(orphans, p)
}
}
frac := float64(len(orphans)) / float64(len(parts))
if len(orphans) == 0 || frac < minFraction {
continue
}
out = append(out, FramingCandidate{
Pattern: pm.PatternID, Name: pm.PatternName, Category: primaryCat(pm),
Zone: pm.ZoneDE, OrphanTerms: orphans, OrphanFraction: round2(frac),
Verdict: framingHeuristicVerdict(frac),
Evidence: fmt.Sprintf("%d/%d zone terms have no narrative echo: %s", len(orphans), len(parts), strings.Join(orphans, ", ")),
})
}
sort.SliceStable(out, func(i, j int) bool {
if out[i].OrphanFraction != out[j].OrphanFraction {
return out[i].OrphanFraction > out[j].OrphanFraction
}
return out[i].Pattern < out[j].Pattern
})
return out
}
func framingHeuristicVerdict(frac float64) string {
if frac >= 0.99 {
return "foreign" // nothing in the zone is echoed by the narrative
}
return "plausible" // partial echo — likely generic vocabulary, human to confirm
}
// zoneParts splits a zone string into significant terms on commas, slashes,
// parentheses and semicolons, lowercased, length >= 4.
func zoneParts(zone string) []string {
fields := strings.FieldsFunc(strings.ToLower(zone), func(r rune) bool {
return r == ',' || r == '/' || r == ';' || r == '(' || r == ')'
})
var out []string
for _, f := range fields {
if t := strings.TrimSpace(f); len([]rune(t)) >= 4 {
out = append(out, t)
}
}
return out
}
// partEchoed reports whether a zone part is reflected in the narrative. Matching
// is bidirectional to survive German compounding: a zone word echoes if it is a
// generic hazard term, if it is a substring of the narrative, OR if any narrative
// stem (>= 5 chars) is a substring of the zone word (so narrative "Steuerung"
// echoes zone "Steuerungssystem").
func partEchoed(part, narrative string, narStems []string) bool {
for _, w := range strings.Fields(part) {
if genericHazardStop[w] {
return true
}
if len([]rune(w)) < 4 {
continue
}
if strings.Contains(narrative, w) {
return true
}
for _, ns := range narStems {
if strings.Contains(w, ns) {
return true
}
}
}
return false
}
// RenderFramingQueue renders foreign-framing candidates as a markdown review queue.
func RenderFramingQueue(machine string, candidates []FramingCandidate) string {
var b strings.Builder
fmt.Fprintf(&b, "# Foreign-framing review queue — %s\n\n", machine)
fmt.Fprintf(&b, "%d fired pattern(s) name zone terms the narrative never mentions. Propose-only — a human (or the LLM) decides: add a dom_* gate term, or re-frame the zone.\n\n", len(candidates))
for i, c := range candidates {
fmt.Fprintf(&b, "## %d. %s — %s [%s, orphan %.0f%%]\n", i+1, c.Pattern, c.Name, c.Verdict, c.OrphanFraction*100)
fmt.Fprintf(&b, "- category: %s\n- zone: %s\n", c.Category, c.Zone)
fmt.Fprintf(&b, "- orphan terms (no narrative echo): %s\n", strings.Join(c.OrphanTerms, ", "))
fmt.Fprintf(&b, "- suggested action: %s\n\n", framingAction(c.Verdict))
}
return b.String()
}
func framingAction(verdict string) string {
if verdict == "foreign" {
return "likely foreign-framed — propose a dom_* gate term for the orphan term(s), or re-frame the zone; human confirms + commits + pins a GT case"
}
return "partial echo — likely generic vocabulary; human to confirm whether any orphan term is a foreign-machine component"
}
@@ -0,0 +1,33 @@
package iace
import "testing"
func TestFindFramingCandidates_FlagsForeignZone(t *testing.T) {
narrative := "Gewerbliche Geschirrspuelmaschine mit Boiler und Tank. Die Tuer ist verriegelt."
fired := []PatternMatch{
mkPM("HPforeign", "mechanical_hazard", "Walzen, Transportbaender, Bearbeitungszone", "Einzug", 80, nil, nil),
mkPM("HPlocal", "thermal_hazard", "Boiler, Tank, Tuer", "Verbrennung", 80, nil, nil),
mkPM("HPgeneric", "mechanical_hazard", "Quetschstelle, Gefahrbereich", "Quetschen", 80, nil, nil),
}
got := FindFramingCandidates(fired, narrative, 0.6)
if len(got) != 1 || got[0].Pattern != "HPforeign" {
t.Fatalf("want only HPforeign flagged, got %+v", got)
}
if got[0].Verdict != "foreign" {
t.Errorf("fully-orphan zone should be 'foreign', got %s", got[0].Verdict)
}
}
func TestFindFramingCandidates_PartialEchoIsPlausible(t *testing.T) {
narrative := "Maschine mit Boiler und Tank."
fired := []PatternMatch{
mkPM("HPx", "thermal_hazard", "Boiler, Tank, Auspuffleitung", "x", 80, nil, nil),
}
got := FindFramingCandidates(fired, narrative, 0.3)
if len(got) != 1 {
t.Fatalf("want 1 candidate (1/3 orphan >= 0.3), got %d", len(got))
}
if got[0].Verdict != "plausible" || len(got[0].OrphanTerms) != 1 || got[0].OrphanTerms[0] != "auspuffleitung" {
t.Errorf("want plausible + orphan [auspuffleitung], got %s %v", got[0].Verdict, got[0].OrphanTerms)
}
}
@@ -0,0 +1,123 @@
package iace
import "github.com/google/uuid"
// Non-test plumbing for the offline proposer (P2 slice 3): run the engine for a
// narrative and produce the fired patterns + the engine-built hazards/mitigations
// the dedup proposer and GT screen consume. This is the same pipeline the GT
// benchmark tests use, lifted out of test scope so the dev-time CLI can call it.
// universalLifecyclePhases are appended so patterns gated to a specific lifecycle
// (maintenance/cleaning/setup/fault clearing) still fire — the proposer wants the
// full hazard picture, not only normal-operation hazards.
var universalLifecyclePhases = []string{"normal_operation", "maintenance", "cleaning", "setup", "fault_clearing"}
// BuildProposerInput parses a narrative, runs the pattern engine, keeps the
// narrative-relevant patterns, and returns the hazards, mitigations and fired
// patterns. NOTE: it does not apply the CE cyber-category skip, so the proposer
// view may include cyber/AI hazards that the CE log excludes — harmless for the
// GT recall screen (they match no CE ground-truth entry).
func BuildProposerInput(narrative, machineType string, extraMachineTypes []string) ([]Hazard, []Mitigation, []PatternMatch) {
res := ParseNarrative(narrative, machineType)
var compIDs, compNames, energyIDs []string
for _, c := range res.Components {
if c.Negated {
continue
}
compIDs = append(compIDs, c.LibraryID)
compNames = append(compNames, c.NameDE)
}
for _, e := range res.EnergySources {
energyIDs = append(energyIDs, e.SourceID)
}
machineTypes := append([]string{}, extraMachineTypes...)
if machineType != "" {
machineTypes = append(machineTypes, machineType)
}
lifecycles := append(append([]string{}, res.LifecyclePhases...), universalLifecyclePhases...)
out := NewPatternEngine().Match(MatchInput{
ComponentLibraryIDs: compIDs,
EnergySourceIDs: energyIDs,
LifecyclePhases: lifecycles,
CustomTags: res.CustomTags,
OperationalStates: res.OperationalStates,
StateTransitions: res.StateTransitions,
HumanRoles: res.Roles,
MachineTypes: machineTypes,
})
kept := make([]PatternMatch, 0, len(out.MatchedPatterns))
for _, pm := range out.MatchedPatterns {
if IsPatternRelevant(pm, narrative, compNames) {
kept = append(kept, pm)
}
}
filtered := *out
filtered.MatchedPatterns = kept
hazards, mits := patternsToHazardsAndMitigations(&filtered)
return hazards, mits, kept
}
// patternsToHazardsAndMitigations converts engine output into the hazard/mitigation
// entities the benchmark + proposer compare on. Simplified vs InitializeProject
// (no risk estimation, no norm refs) — it only needs category/zone/scenario/measures.
func patternsToHazardsAndMitigations(out *MatchOutput) ([]Hazard, []Mitigation) {
hazards := make([]Hazard, 0, len(out.MatchedPatterns))
patternToHazard := make(map[string]uuid.UUID, len(out.MatchedPatterns))
for _, pm := range out.MatchedPatterns {
cat := ""
if len(pm.HazardCats) > 0 {
cat = pm.HazardCats[0]
}
lifecycle := ""
if len(pm.ApplicableLifecycles) > 0 {
lifecycle = pm.ApplicableLifecycles[0]
}
h := Hazard{
ID: uuid.New(),
Name: pm.ScenarioDE,
Category: cat,
Description: pm.ScenarioDE,
Scenario: pm.ScenarioDE,
TriggerEvent: pm.TriggerDE,
PossibleHarm: pm.HarmDE,
AffectedPerson: pm.AffectedDE,
HazardousZone: pm.ZoneDE,
LifecyclePhase: lifecycle,
}
if h.Name == "" {
h.Name = pm.PatternName
}
hazards = append(hazards, h)
patternToHazard[pm.PatternID] = h.ID
}
measureNames := make(map[string]string)
for _, m := range GetProtectiveMeasureLibrary() {
measureNames[m.ID] = m.Name
}
var mitigations []Mitigation
for _, sm := range out.SuggestedMeasures {
name := measureNames[sm.MeasureID]
if name == "" {
name = sm.MeasureID
}
for _, srcPattern := range sm.SourcePatterns {
hid, ok := patternToHazard[srcPattern]
if !ok {
continue
}
mitigations = append(mitigations, Mitigation{
ID: uuid.New(),
HazardID: hid,
Name: name,
})
}
}
return hazards, mitigations
}
@@ -0,0 +1,25 @@
package iace
import "testing"
func TestBuildProposerInput_WarewashingFires(t *testing.T) {
hazards, _, fired := BuildProposerInput(
warewashingNarrative,
"Gewerbliche Untertisch-Geschirrspuelmaschine (vernetzt)",
[]string{"food_processing"},
)
if len(fired) == 0 || len(hazards) == 0 {
t.Fatalf("want fired patterns + hazards, got %d patterns / %d hazards", len(fired), len(hazards))
}
has := func(id string) bool {
for _, pm := range fired {
if pm.PatternID == id {
return true
}
}
return false
}
if !has("HP2201") {
t.Errorf("warewashing-specific HP2201 must fire via BuildProposerInput")
}
}
@@ -0,0 +1,174 @@
package iace
import (
"context"
"encoding/json"
"fmt"
"strings"
"github.com/breakpilot/ai-compliance-sdk/internal/llm"
)
// Semantic judgement over RECALL-SAFE dedup candidates (P2 slice 2). DEV-TIME,
// propose-only. The deterministic GT wall (proposer_screen.go) has already
// removed candidates that would drop recall or that credit different GT entries;
// the judge only adds an opinion on whether the survivors are truly the same
// hazard, plus a rationale, for the human review queue. It NEVER mutates anything.
//
// The judge is pluggable behind CandidateJudge so the runtime/tests stay
// deterministic (HeuristicJudge) while the dev-time CLI can plug in the
// non-deterministic LLM (LLMJudge over the shared llm.ProviderRegistry).
const (
VerdictDuplicate = "duplicate"
VerdictDistinct = "distinct"
VerdictUncertain = "uncertain"
)
// JudgedProposal is one candidate with its GT-wall result and the judge's opinion.
type JudgedProposal struct {
Candidate DedupCandidate `json:"candidate"`
Screen ScreenResult `json:"screen"`
Verdict string `json:"verdict"`
Confidence string `json:"confidence"`
Rationale string `json:"rationale"`
Judge string `json:"judge"`
}
// CandidateJudge decides whether two near-duplicate patterns are the same hazard.
type CandidateJudge interface {
Name() string
Judge(ctx context.Context, c DedupCandidate, a, b PatternMatch) (verdict, confidence, rationale string)
}
// HeuristicJudge is the deterministic default/fallback. It only ever returns "low"
// confidence — it is a placeholder for the LLM, and it deliberately punts to
// "uncertain" on the hard cases (low text overlap, shared measures) so the queue
// makes clear exactly where the LLM earns its keep.
type HeuristicJudge struct{}
func (HeuristicJudge) Name() string { return "heuristic" }
func (HeuristicJudge) Judge(_ context.Context, c DedupCandidate, _, _ PatternMatch) (string, string, string) {
switch {
case c.ScenarioJaccard >= 0.5 || (c.ZoneJaccard >= 0.5 && c.MeasureJaccard >= 0.5):
return VerdictDuplicate, "low", "structural: high scenario, or combined zone+measure, overlap"
case c.MeasureJaccard >= 0.99 && c.ZoneJaccard == 0 && c.ScenarioJaccard < 0.3:
return VerdictDistinct, "low", "structural: identical measures but no zone/scenario overlap — likely distinct hazards sharing generic measures"
default:
return VerdictUncertain, "low", "structural signal inconclusive — needs the LLM judge"
}
}
// LLMJudge asks an offline model to make the semantic call. Non-deterministic, so
// it lives only in the dev-time tool, never in tests or the runtime. It degrades
// to "uncertain" on any transport or parse error — it must never break the run.
type LLMJudge struct {
Completer LLMCompleter
MachineClass string
}
func (LLMJudge) Name() string { return "llm" }
func (j LLMJudge) Judge(ctx context.Context, c DedupCandidate, a, b PatternMatch) (string, string, string) {
system, user := BuildJudgePrompt(j.MachineClass, a, b)
raw, err := j.Completer.Complete(ctx, system, user)
if err != nil {
return VerdictUncertain, "low", "LLM error: " + err.Error()
}
return parseJudgeJSON(raw)
}
// BuildJudgePrompt is the real LLM artifact — built and unit-tested deterministically
// even though the call itself is not. It frames the ISO 12100 same-vs-distinct
// question and forces a JSON answer.
func BuildJudgePrompt(machineClass string, a, b PatternMatch) (system, user string) {
system = "Du bist Sachverstaendiger fuer Maschinensicherheit nach EN ISO 12100. " +
"Entscheide, ob zwei generierte Gefaehrdungen fuer DIESE Maschine DIESELBE Gefaehrdung " +
"beschreiben (Dublette) oder fachlich VERSCHIEDENE Gefaehrdungen sind, die nur zufaellig " +
"dieselben Schutzmassnahmen teilen. Verschieden, wenn Wirkort, Ausloeser oder " +
"Schadensmechanismus abweichen — auch bei gleicher Kategorie und gleichen Massnahmen. " +
"Antworte AUSSCHLIESSLICH als JSON: " +
`{"verdict":"duplicate|distinct|uncertain","confidence":"high|medium|low","rationale":"..."}.`
user = fmt.Sprintf(`Maschinenklasse: %s
Gefaehrdung A (%s):
Name: %s
Kategorie: %s
Zone: %s
Szenario: %s
Ausloeser: %s
Schaden: %s
Massnahmen: %s
Gefaehrdung B (%s):
Name: %s
Kategorie: %s
Zone: %s
Szenario: %s
Ausloeser: %s
Schaden: %s
Massnahmen: %s
Sind A und B dieselbe Gefaehrdung fuer diese Maschine?`,
machineClass,
a.PatternID, a.PatternName, primaryCat(a), a.ZoneDE, a.ScenarioDE, a.TriggerDE, a.HarmDE, strings.Join(a.SuggestedMeasureIDs, ", "),
b.PatternID, b.PatternName, primaryCat(b), b.ZoneDE, b.ScenarioDE, b.TriggerDE, b.HarmDE, strings.Join(b.SuggestedMeasureIDs, ", "))
return system, user
}
func parseJudgeJSON(raw string) (verdict, confidence, rationale string) {
start, end := strings.Index(raw, "{"), strings.LastIndex(raw, "}")
if start < 0 || end <= start {
return VerdictUncertain, "low", "unparseable LLM output"
}
var v struct {
Verdict string `json:"verdict"`
Confidence string `json:"confidence"`
Rationale string `json:"rationale"`
}
if err := json.Unmarshal([]byte(raw[start:end+1]), &v); err != nil {
return VerdictUncertain, "low", "unparseable LLM JSON: " + err.Error()
}
switch v.Verdict {
case VerdictDuplicate, VerdictDistinct, VerdictUncertain:
default:
v.Verdict = VerdictUncertain
}
if v.Confidence == "" {
v.Confidence = "low"
}
return v.Verdict, v.Confidence, v.Rationale
}
// LLMCompleter is the minimal text-in/text-out the LLM judge needs. Tests pass a
// stub; the dev-time tool passes a registry-backed adapter (NewRegistryCompleter).
type LLMCompleter interface {
Complete(ctx context.Context, system, user string) (string, error)
}
type registryCompleter struct {
reg *llm.ProviderRegistry
model string
}
// NewRegistryCompleter adapts the shared llm.ProviderRegistry to LLMCompleter so
// the proposer can reuse the platform's offline model wiring (e.g. self-hosted qwen).
func NewRegistryCompleter(reg *llm.ProviderRegistry, model string) LLMCompleter {
return &registryCompleter{reg: reg, model: model}
}
func (rc *registryCompleter) Complete(ctx context.Context, system, user string) (string, error) {
resp, err := rc.reg.Chat(ctx, &llm.ChatRequest{
Model: rc.model,
Messages: []llm.Message{
{Role: "system", Content: system},
{Role: "user", Content: user},
},
Temperature: 0,
})
if err != nil {
return "", err
}
return resp.Message.Content, nil
}
@@ -0,0 +1,104 @@
package iace
import (
"context"
"errors"
"strings"
"testing"
)
func TestHeuristicJudge_Verdicts(t *testing.T) {
tests := []struct {
name string
zone, meas float64
scenario float64
wantVerdict string
}{
{"high scenario overlap -> duplicate", 0, 0.3, 0.6, VerdictDuplicate},
{"high zone+measure -> duplicate", 0.6, 0.6, 0.1, VerdictDuplicate},
{"identical measures, no text -> distinct", 0, 1.0, 0.0, VerdictDistinct},
{"shared measures, low text -> uncertain", 0, 0.67, 0.19, VerdictUncertain},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
c := DedupCandidate{ZoneJaccard: tt.zone, MeasureJaccard: tt.meas, ScenarioJaccard: tt.scenario}
v, conf, _ := HeuristicJudge{}.Judge(context.Background(), c, PatternMatch{}, PatternMatch{})
if v != tt.wantVerdict {
t.Errorf("verdict: want %s, got %s", tt.wantVerdict, v)
}
if conf != "low" {
t.Errorf("heuristic confidence must be low, got %s", conf)
}
})
}
}
func TestBuildJudgePrompt_ContainsKeyFacts(t *testing.T) {
a := PatternMatch{PatternID: "HPa", PatternName: "Heisse Flaeche", HazardCats: []string{"thermal_hazard"},
ZoneDE: "Boiler", ScenarioDE: "Beruehrung heisser Boiler", SuggestedMeasureIDs: []string{"M071"}}
b := PatternMatch{PatternID: "HPb", PatternName: "Heisses Spuelgut", HazardCats: []string{"thermal_hazard"},
ZoneDE: "Spuelgut", ScenarioDE: "Beruehrung heisses Geschirr", SuggestedMeasureIDs: []string{"M071"}}
system, user := BuildJudgePrompt("Geschirrspuelmaschine", a, b)
for _, want := range []string{"EN ISO 12100", "JSON", "verdict"} {
if !strings.Contains(system, want) {
t.Errorf("system prompt missing %q", want)
}
}
for _, want := range []string{"Geschirrspuelmaschine", "HPa", "HPb", "Boiler", "Spuelgut", "thermal_hazard"} {
if !strings.Contains(user, want) {
t.Errorf("user prompt missing %q", want)
}
}
}
type fakeCompleter struct {
out string
err error
}
func (f fakeCompleter) Complete(_ context.Context, _, _ string) (string, error) { return f.out, f.err }
func TestLLMJudge_ParsesAndDegrades(t *testing.T) {
cand := DedupCandidate{KeepPattern: "HPa", DropPattern: "HPb"}
// Well-formed JSON, even wrapped in chatter, parses.
j := LLMJudge{Completer: fakeCompleter{out: "Sicher. {\"verdict\":\"distinct\",\"confidence\":\"high\",\"rationale\":\"andere Wirkorte\"}"}, MachineClass: "x"}
if v, conf, r := j.Judge(context.Background(), cand, PatternMatch{}, PatternMatch{}); v != VerdictDistinct || conf != "high" || r != "andere Wirkorte" {
t.Errorf("parse: got %s/%s/%q", v, conf, r)
}
// Unknown verdict value normalises to uncertain.
j2 := LLMJudge{Completer: fakeCompleter{out: `{"verdict":"maybe","confidence":"medium","rationale":"x"}`}}
if v, _, _ := j2.Judge(context.Background(), cand, PatternMatch{}, PatternMatch{}); v != VerdictUncertain {
t.Errorf("unknown verdict must normalise to uncertain, got %s", v)
}
// Transport error degrades gracefully, never panics.
j3 := LLMJudge{Completer: fakeCompleter{err: errors.New("connection refused")}}
if v, _, r := j3.Judge(context.Background(), cand, PatternMatch{}, PatternMatch{}); v != VerdictUncertain || !strings.Contains(r, "LLM error") {
t.Errorf("error path: got %s / %q", v, r)
}
// Garbage (no JSON) degrades to uncertain.
j4 := LLMJudge{Completer: fakeCompleter{out: "no json here"}}
if v, _, _ := j4.Judge(context.Background(), cand, PatternMatch{}, PatternMatch{}); v != VerdictUncertain {
t.Errorf("garbage must degrade to uncertain, got %s", v)
}
}
func TestRenderProposalQueue_ShowsActions(t *testing.T) {
proposals := []JudgedProposal{
{
Candidate: DedupCandidate{KeepPattern: "HP807", DropPattern: "HP033", Category: "update_failure", Score: 0.32},
Screen: ScreenResult{RecallBefore: 1, RecallAfter: 1},
Verdict: VerdictDuplicate, Confidence: "medium", Rationale: "same update failure", Judge: "llm",
},
}
out := RenderProposalQueue("Geschirrspuelmaschine", proposals)
for _, want := range []string{"HP807", "HP033", "update_failure", "supersession", "Propose-only"} {
if !strings.Contains(out, want) {
t.Errorf("queue missing %q\n%s", want, out)
}
}
}
@@ -0,0 +1,47 @@
package iace
import (
"fmt"
"strings"
)
// RenderProposalQueue turns judged dedup proposals into the human-review queue
// (markdown). Deterministic. Nothing here applies a change — every entry is a
// suggestion for a human to confirm, edit, commit, and pin with a GT case.
func RenderProposalQueue(machine string, proposals []JudgedProposal) string {
var b strings.Builder
fmt.Fprintf(&b, "# Dedup proposal queue — %s\n\n", machine)
fmt.Fprintf(&b, "%d candidate(s) survived the deterministic GT wall. Propose-only — nothing is applied automatically.\n\n", len(proposals))
for i, p := range proposals {
c := p.Candidate
fmt.Fprintf(&b, "## %d. keep %s ⊃ drop %s [%s → %s (%s)]\n",
i+1, c.KeepPattern, c.DropPattern, p.Judge, p.Verdict, p.Confidence)
fmt.Fprintf(&b, "- category %s · score %.2f (measures %.0f%%, zone %.0f%%, scenario %.0f%%)\n",
c.Category, c.Score, c.MeasureJaccard*100, c.ZoneJaccard*100, c.ScenarioJaccard*100)
fmt.Fprintf(&b, "- GT recall %.1f%% → %.1f%% when %s is dropped (wall: %s)\n",
p.Screen.RecallBefore*100, p.Screen.RecallAfter*100, c.DropPattern, wallNote(p.Screen))
fmt.Fprintf(&b, "- keep: %s\n- drop: %s\n", c.KeepHazardName, c.DropName)
fmt.Fprintf(&b, "- judge rationale: %s\n", p.Rationale)
fmt.Fprintf(&b, "- suggested action: %s\n\n", suggestedAction(p))
}
return b.String()
}
func wallNote(s ScreenResult) string {
if s.DistinctGT {
return fmt.Sprintf("distinct GT %s vs %s", s.KeepGT, s.DropGT)
}
return "recall-safe"
}
func suggestedAction(p JudgedProposal) string {
switch p.Verdict {
case VerdictDuplicate:
return fmt.Sprintf("add %s to a supersession set, then a human confirms + commits + pins a GT case", p.Candidate.DropPattern)
case VerdictDistinct:
return "keep both — judge considers them distinct hazards"
default:
return "needs human (or higher-confidence LLM) review — no automatic action"
}
}
@@ -0,0 +1,61 @@
package iace
import "github.com/google/uuid"
// ScreenResult is the deterministic GT verdict for one proposed supersession.
type ScreenResult struct {
RecallBefore float64 `json:"recall_before"`
RecallAfter float64 `json:"recall_after"`
KeepGT string `json:"keep_gt,omitempty"` // GT entry the keeper credits (if any)
DropGT string `json:"drop_gt,omitempty"` // GT entry the drop credits (if any)
DistinctGT bool `json:"distinct_gt"` // keep & drop credit DIFFERENT GT entries -> distinct hazards
Safe bool `json:"safe"` // recall preserved AND not distinct
}
// ScreenSupersession is the WALL between "propose" and "decide". A proposal is
// safe only if BOTH deterministic checks pass:
//
// 1. RECALL is not reduced when the drop-hazard (and its mitigations) is removed
// — otherwise the drop is load-bearing for GT coverage.
// 2. The two hazards do NOT credit DIFFERENT ground-truth entries. Recall alone
// is necessary but not sufficient: two genuinely distinct hazards that share
// the same measures (e.g. hot boiler surface vs hot ware on unloading) keep
// recall at 100% when one is dropped, yet must NOT be merged. If keep and
// drop each match a different GT entry, they are distinct.
//
// Whatever survives both is still only RECALL-SAFE — a candidate for a human (and
// in slice 2, an LLM) to confirm semantically. Deterministic; reuses
// CompareBenchmark; touches neither the library nor the runtime.
func ScreenSupersession(gt *GroundTruth, hazards []Hazard, mits []Mitigation, keepHazardName, dropHazardName string) ScreenResult {
before := CompareBenchmark(gt, hazards, mits)
gtOf := map[string]string{}
for _, p := range before.MatchedPairs {
gtOf[p.EngineHazard.Name] = p.GTEntry.Nr
}
keepGT, dropGT := gtOf[keepHazardName], gtOf[dropHazardName]
distinct := keepGT != "" && dropGT != "" && keepGT != dropGT
kept := make([]Hazard, 0, len(hazards))
dropped := map[uuid.UUID]bool{}
for _, h := range hazards {
if h.Name == dropHazardName {
dropped[h.ID] = true
continue
}
kept = append(kept, h)
}
keptMits := make([]Mitigation, 0, len(mits))
for _, m := range mits {
if !dropped[m.HazardID] {
keptMits = append(keptMits, m)
}
}
after := CompareBenchmark(gt, kept, keptMits)
return ScreenResult{
RecallBefore: before.CoverageScore, RecallAfter: after.CoverageScore,
KeepGT: keepGT, DropGT: dropGT, DistinctGT: distinct,
Safe: after.CoverageScore >= before.CoverageScore && !distinct,
}
}
@@ -160,6 +160,7 @@ func (s *Store) ListHazards(ctx context.Context, projectID uuid.UUID) ([]Hazard,
hazards = append(hazards, h)
}
SortHazardsByISO12100(hazards)
return hazards, nil
}
@@ -0,0 +1,89 @@
package ucca
import (
"fmt"
"os"
"path/filepath"
"runtime"
)
// graphCallerRel resolves a path relative to THIS source file (build-time location), so the
// graph data is findable under `go test` (cwd = package dir) regardless of working directory.
// In a built container the source is gone, so cwd-relative candidates carry the load instead.
func graphCallerRel(rel string) string {
_, file, _, ok := runtime.Caller(0)
if !ok {
return ""
}
return filepath.Join(filepath.Dir(file), rel)
}
// firstExisting returns the first candidate path that exists with the requested kind (dir vs
// file). Empty candidates (e.g. unset env overrides) are skipped.
func firstExisting(candidates []string, wantDir bool) string {
for _, p := range candidates {
if p == "" {
continue
}
info, err := os.Stat(p)
if err != nil || info.IsDir() != wantDir {
continue
}
return p
}
return ""
}
// LoadComplianceGraph loads the file-backed Compliance Execution Graph: the Registry join-key
// contract (obligations/obligation_join_keys.json — owned by the Obligation session) + our
// curated, accepted control mappings + evidence requirements. Locations are resolved across
// three layouts: dev (cwd = ai-compliance-sdk/, canonical contract at ../obligations), container
// (WORKDIR /app, data/ copied in incl. a synced data/obligations/ copy) and `go test`
// (cwd = package dir, via graphCallerRel). Fail-closed: a missing/invalid source returns an
// error so the handler serves 503 — never a half-built graph.
//
// NOTE: data/obligations/obligation_join_keys.json is a SYNCED COPY of the repo-root contract
// (the canonical owner is the Obligation session). Re-sync it when the Registry grows; dev/test
// prefer the canonical repo-root path, only the container falls back to the copy.
func LoadComplianceGraph() (*ObligationJoinKeys, *ControlMappingSet, *EvidenceRequirementSet, error) {
joinPath := firstExisting([]string{
os.Getenv("BP_OBLIGATION_JOIN_KEYS"),
"../obligations/obligation_join_keys.json",
graphCallerRel("../../../obligations/obligation_join_keys.json"),
"data/obligations/obligation_join_keys.json",
graphCallerRel("../../data/obligations/obligation_join_keys.json"),
}, false)
if joinPath == "" {
return nil, nil, nil, fmt.Errorf("obligation_join_keys.json not found in any candidate path")
}
mapDir := firstExisting([]string{
os.Getenv("BP_CONTROL_MAPPINGS_DIR"),
"data/control_mappings",
graphCallerRel("../../data/control_mappings"),
}, true)
if mapDir == "" {
return nil, nil, nil, fmt.Errorf("control_mappings dir not found in any candidate path")
}
evDir := firstExisting([]string{
os.Getenv("BP_EVIDENCE_DIR"),
"data/evidence_requirements",
graphCallerRel("../../data/evidence_requirements"),
}, true)
if evDir == "" {
return nil, nil, nil, fmt.Errorf("evidence_requirements dir not found in any candidate path")
}
joins, err := LoadObligationJoinKeys(joinPath)
if err != nil {
return nil, nil, nil, fmt.Errorf("load join keys (%s): %w", joinPath, err)
}
mappings, err := LoadControlMappings(mapDir)
if err != nil {
return nil, nil, nil, fmt.Errorf("load control mappings (%s): %w", mapDir, err)
}
evidence, err := LoadEvidenceRequirements(evDir)
if err != nil {
return nil, nil, nil, fmt.Errorf("load evidence (%s): %w", evDir, err)
}
return joins, mappings, evidence, nil
}
@@ -23,7 +23,7 @@ type ControlMapping struct {
SourceRole string `json:"source_role"` // source_role of the norm (operational_requirement, ...)
TargetFramework string `json:"target_framework"` // e.g. "OWASP ASVS"
TargetControl string `json:"target_control"` // e.g. "V6.3.1"
MappingType string `json:"mapping_type"` // supports | partially_supports | implements | related | contradicts
MappingType string `json:"mapping_type"` // primary_implementation | implements | supports | partially_supports | related | contradicts
MappingStatus string `json:"mapping_status"` // candidate | accepted | rejected | superseded
Provenance string `json:"provenance"` // retriever_candidate | human_curated | rule_based
ObligationID string `json:"obligation_id,omitempty"` // stable cross-session join key (Obligation Registry); empty until adopted, citation_unit is the interim bridge
@@ -36,7 +36,7 @@ type ControlMapping struct {
// Allowed enum values — the deterministic "rule" layer that keeps the curated store clean.
var (
mappingTypeValues = map[string]bool{"supports": true, "partially_supports": true, "implements": true, "related": true, "contradicts": true}
mappingTypeValues = map[string]bool{"primary_implementation": true, "implements": true, "supports": true, "partially_supports": true, "related": true, "contradicts": true}
mappingStatusValues = map[string]bool{"candidate": true, "accepted": true, "rejected": true, "superseded": true}
provenanceValues = map[string]bool{"retriever_candidate": true, "human_curated": true, "rule_based": true}
)
@@ -77,6 +77,7 @@ _ROUTER_MODULES = [
"licenses_routes",
"template_rule_routes",
"specialist_agent_routes",
"reasoning_routes",
]
_loaded_count = 0
@@ -0,0 +1,98 @@
"""HTTP endpoints for the Regulatory Reasoning Engine (spec §7).
Thin handlers — all reasoning lives in `compliance.reasoning.*`. No DB, no RAG;
pure deterministic rule evaluation.
POST /reasoning/scope -> which regulations apply + missing facts
POST /reasoning/obligations -> obligations, overlaps, multi-evidence
POST /reasoning/implementation-reasoning -> claim->obligation mapping (Welt 1, no verdict)
POST /reasoning/interpretation-assessment -> verdict on a customer interpretation
POST /reasoning/product-scope -> gate on facts, else run discover_scope once
POST /reasoning/regulatory-map -> customer-readable read-model over the scope
POST /reasoning/interpretation-in-map -> judge a customer interpretation within the map
"""
from __future__ import annotations
from fastapi import APIRouter
from compliance.interpretation_map import (
InterpretationInMapRequest,
InterpretationInMapResult,
interpret_in_map,
)
from compliance.product_scope import (
ProductScopeRequest,
ProductScopeResponse,
resolve_product_scope,
)
from compliance.regulatory_map import RegulatoryMap, RegulatoryMapRequest, render_regulatory_map
from compliance.reasoning import (
assess_interpretation,
derive_obligations,
discover_scope,
reason_implementation_claim,
)
from compliance.reasoning.schemas import (
ImplementationReasoningRequest,
ImplementationReasoningResponse,
InterpretationRequest,
InterpretationResponse,
ObligationsRequest,
ObligationsResponse,
ScopeRequest,
ScopeResponse,
)
router = APIRouter(prefix="/reasoning", tags=["reasoning"])
@router.post("/scope", response_model=ScopeResponse)
def scope_discovery(req: ScopeRequest) -> ScopeResponse:
scope = discover_scope(req.product_profile)
return ScopeResponse(
regulatory_scope=scope,
missing_facts=scope.missing_facts,
confidence=scope.confidence,
)
@router.post("/obligations", response_model=ObligationsResponse)
def applicable_obligations(req: ObligationsRequest) -> ObligationsResponse:
return derive_obligations(req.product_profile, req.regulatory_scope)
@router.post("/implementation-reasoning", response_model=ImplementationReasoningResponse)
def implementation_reasoning(req: ImplementationReasoningRequest) -> ImplementationReasoningResponse:
return reason_implementation_claim(req.product_profile, req.customer_claim)
@router.post("/product-scope", response_model=ProductScopeResponse)
def product_scope(req: ProductScopeRequest) -> ProductScopeResponse:
return resolve_product_scope(req.product_profile)
@router.post("/regulatory-map", response_model=RegulatoryMap)
def regulatory_map(req: RegulatoryMapRequest) -> RegulatoryMap:
return render_regulatory_map(req.product_profile)
@router.post("/interpretation-in-map", response_model=InterpretationInMapResult)
def interpretation_in_map(req: InterpretationInMapRequest) -> InterpretationInMapResult:
reg_map = render_regulatory_map(req.product_profile)
return interpret_in_map(reg_map, req.customer_interpretation)
@router.post("/interpretation-assessment", response_model=InterpretationResponse)
def interpretation_assessment(req: InterpretationRequest) -> InterpretationResponse:
result = assess_interpretation(req.customer_interpretation, req.product_profile)
return InterpretationResponse(
assessment=result.assessment,
affected_regulations=result.affected_regulations,
affected_obligations=result.affected_obligations,
corrected_interpretation=result.corrected_interpretation,
risks=result.risks,
legal_basis_refs=result.legal_basis_refs,
explanation=result.explanation,
confidence=result.confidence,
)
@@ -0,0 +1,46 @@
"""Company Intelligence (Phase 2A) — Company Capability Profile foundation.
The HEAD of the spine Company -> Capability -> Product -> Regulation -> Obligation
-> Procedure -> Evidence. Builds a CompanyContext into a CompanyCapabilityProfile
with a four-state trust model (declared/inferred/confirmed/unknown). A certification
yields at most an INFERRED candidate — never "erfuellt".
Reasoning OWNS the container + trust-state; it CONSUMES the Certification->Capability
mapping (Execution-owned) via an injected contract — no mapping data in product code.
"""
from __future__ import annotations
from .contract import CapabilityMappingEntry, CertificationCapabilityMap, EMPTY_MAPPING
from .engine import build_company_profile
from .schemas import (
CapabilityEvidence,
Certification,
CompanyCapabilityProfile,
CompanyContext,
Declaration,
ExistingEvidence,
ExistingProcess,
ExistingSystem,
OperationalCapability,
OperationalCapabilityCandidate,
VerificationStatus,
)
__all__ = [
"build_company_profile",
"CompanyContext",
"CompanyCapabilityProfile",
"Certification",
"Declaration",
"ExistingProcess",
"ExistingSystem",
"ExistingEvidence",
"CapabilityEvidence",
"OperationalCapabilityCandidate",
"OperationalCapability",
"VerificationStatus",
"CapabilityMappingEntry",
"CertificationCapabilityMap",
"EMPTY_MAPPING",
]
@@ -0,0 +1,43 @@
"""Consumption contract for the Certification -> Capability mapping.
OWNERSHIP BOUNDARY (hard): the Capability Registry, CapabilityDefinition and the
Certification->Capability / Feature->Capability mapping RULES live in the Compliance
Execution domain. This Reasoning layer defines ONLY the shape it consumes and never
ships mapping DATA in product code — tests inject mocks, so the real table can only
ever live in Execution.
Execution will eventually provide CapabilityRegistry / CapabilityMapping /
CapabilityDefinition; Reasoning consumes exactly `OperationalCapabilityCandidate`
{capability_id, source, confidence, verification_status} (see schemas.py) and the
minimal mapping SHAPE below — nothing more.
Python 3.9 compatible (no `|` unions).
"""
from __future__ import annotations
from typing import Dict, List
from pydantic import BaseModel, Field
from compliance.reasoning.enums import Confidence
class CapabilityMappingEntry(BaseModel):
"""One mapping rule SHAPE: a certification implies candidate capabilities.
Contract type only. The actual table (which capabilities ISO27001 implies) is
Execution's DATA and MUST NOT be hard-coded here or anywhere in product code.
"""
capability_ids: List[str] = Field(default_factory=list)
confidence: Confidence = Confidence.MEDIUM
# certification_id -> entry. Injected at call time; product code holds NO entries.
CertificationCapabilityMap = Dict[str, CapabilityMappingEntry]
# Intentionally empty: without an injected mapping there are zero inferred
# candidates. This is the architectural guarantee that the registry lives only in
# the Compliance Execution domain.
EMPTY_MAPPING: CertificationCapabilityMap = {}
@@ -0,0 +1,114 @@
"""Company Intelligence engine (Phase 2A) — build the Company Capability Profile.
Deterministic, no LLM/RAG. Turns a raw CompanyContext into capability evidence,
candidates and (only via explicit verification) confirmed capabilities.
HARD RULE enforced here: a certification yields at most an INFERRED candidate; it
can NEVER produce a CONFIRMED capability on its own. Only real ExistingEvidence
(`proves_capability_id`) promotes a capability to CONFIRMED. Certifications without
a known mapping yield evidence-of-claim but NO inferred capability (the mapping is
Execution's data, injected — never hard-coded here).
Python 3.9 compatible (no `|` unions).
"""
from __future__ import annotations
from typing import Dict, List, Optional, Tuple
from compliance.reasoning.enums import Confidence
from .contract import EMPTY_MAPPING, CertificationCapabilityMap
from .schemas import (
CapabilityEvidence,
CompanyCapabilityProfile,
CompanyContext,
OperationalCapability,
OperationalCapabilityCandidate,
VerificationStatus,
)
def _declared(context: CompanyContext) -> List[OperationalCapabilityCandidate]:
out: List[OperationalCapabilityCandidate] = []
for d in context.declarations:
out.append(
OperationalCapabilityCandidate(
capability_id=d.capability_id,
source="declaration:%s" % context.company_id,
confidence=Confidence.MEDIUM,
verification_status=VerificationStatus.DECLARED,
)
)
return out
def _from_certifications(
context: CompanyContext, mapping: CertificationCapabilityMap
) -> Tuple[List[CapabilityEvidence], List[OperationalCapabilityCandidate]]:
# refinement 1: certification -> evidence-of-capability (claim) -> inferred candidate
evidence: List[CapabilityEvidence] = []
inferred: List[OperationalCapabilityCandidate] = []
for cert in context.certifications:
source = "certification:%s" % cert.certification_id
evidence.append(
CapabilityEvidence(
source=source,
claim="Company holds %s" % (cert.name or cert.certification_id),
certification_id=cert.certification_id,
)
)
entry = mapping.get(cert.certification_id)
if entry is None:
continue # no mapping known -> NO inferred capability (data is Execution's)
for cap_id in entry.capability_ids:
inferred.append(
OperationalCapabilityCandidate(
capability_id=cap_id,
source=source,
confidence=entry.confidence,
verification_status=VerificationStatus.INFERRED,
)
)
return evidence, inferred
def _confirmed_from_evidence(context: CompanyContext) -> List[OperationalCapability]:
proven: Dict[str, List[str]] = {}
for ev in context.evidence:
cap = ev.proves_capability_id
if not cap:
continue
proven.setdefault(cap, []).append(ev.evidence_id)
return [
OperationalCapability(
capability_id=cap,
verification_status=VerificationStatus.CONFIRMED,
confidence=Confidence.HIGH,
sources=sources,
)
for cap, sources in proven.items()
]
def build_company_profile(
context: CompanyContext, mapping: Optional[CertificationCapabilityMap] = None
) -> CompanyCapabilityProfile:
"""Build the Company Capability Profile from raw context + an injected mapping.
`mapping` defaults to EMPTY (no inferred candidates) so that the cert->capability
table can only ever come from the Compliance Execution domain.
"""
mapping = EMPTY_MAPPING if mapping is None else mapping
evidence, inferred = _from_certifications(context, mapping)
declared = _declared(context)
confirmed = _confirmed_from_evidence(context)
confirmed_ids = {oc.capability_id for oc in confirmed}
# a confirmed capability is no longer a mere candidate
candidates = [c for c in (declared + inferred) if c.capability_id not in confirmed_ids]
return CompanyCapabilityProfile(
company_id=context.company_id,
capability_evidence=evidence,
candidate_capabilities=candidates,
confirmed_capabilities=confirmed,
)
@@ -0,0 +1,150 @@
"""Company Intelligence (Phase 2A) — Company Capability Profile (domain objects).
This is the HEAD of the spine
Company -> (Operational) Capability -> Product -> Applicable Regulation ->
Obligation -> Procedure -> Evidence
and answers a DIFFERENT question than Regulatory Intelligence: not "which laws
apply to my product" but "which capabilities does my company already have, and
which regulatory obligations might they already cover".
HARD RULE (structural, not convention): a capability derived from a certification
is at most INFERRED — never CONFIRMED, never "erfuellt". A certification produces
EVIDENCE for a capability, an inference produces a CANDIDATE, and only checked
evidence produces a CONFIRMED capability. This keeps the company side inside
Welt 1 (potential), mirroring `ClaimCoverage` on the obligation side; it is NOT a
conformity verdict (`ComplianceStatus`, Welt 2, owned by Compliance Execution).
OWNERSHIP: Reasoning OWNS this CompanyContext container + the trust-state machine.
It does NOT own the Certification->Capability mapping RULES — those are the same
kind of rule as Feature->Capability and belong to the Compliance Execution
Capability Registry. This layer only CONSUMES `OperationalCapabilityCandidate`
{capability_id, source, confidence, verification_status} via an injected mapping
(see contract.py). No mapping DATA lives in product code (tests inject mocks).
Application/reasoning types, NOT compliance-meta-model classes (architecture
freeze v1.0 untouched). Python 3.9 compatible (no `|` unions).
"""
from __future__ import annotations
from enum import Enum
from typing import List, Optional
from pydantic import BaseModel, Field
from compliance.reasoning.enums import Confidence
class VerificationStatus(str, Enum):
"""Trust state of an operational capability — a FOURTH vocabulary.
Disjoint from ClaimCoverage (Welt 1, customer claim vs obligation),
ComplianceStatus (Welt 2, verified conformity) and DeltaType (RCI). It says
only how well-established a company CAPABILITY is, never whether an obligation
is met. Progression: DECLARED (customer says) -> INFERRED (a certification
implies it) -> CONFIRMED (checked against real evidence); UNKNOWN = no signal.
"""
DECLARED = "declared"
INFERRED = "inferred"
CONFIRMED = "confirmed"
UNKNOWN = "unknown"
# ── raw company inputs (the CompanyContext children) ──────────────────────
class Certification(BaseModel):
certification_id: str # e.g. "ISO27001"
name: str = ""
scope: str = "" # what the cert covers, customer-stated
class Declaration(BaseModel):
"""A customer statement that they have a capability ("we do patch management")."""
capability_id: str
statement: str = ""
class ExistingProcess(BaseModel):
process_id: str
name: str = ""
class ExistingSystem(BaseModel):
system_id: str
name: str = ""
class ExistingEvidence(BaseModel):
"""A concrete artefact the company already holds (policy, audit log, SBOM ...).
`proves_capability_id` is the ONLY thing that may lift a capability to
CONFIRMED — and only when a human/engine has attached real evidence.
"""
evidence_id: str
evidence_type: str = "" # config_export/test_report/policy/audit_log/...
proves_capability_id: Optional[str] = None
# ── intermediate: certification -> evidence-of-capability (refinement 1) ──
class CapabilityEvidence(BaseModel):
"""A certification does not yield a capability directly — only EVIDENCE for one.
"Company holds a certified ISMS" is the evidence/claim; capabilities are then
INFERRED from it via the injected (Execution-owned) mapping, never directly.
"""
source: str # provenance, e.g. "certification:ISO27001"
claim: str = ""
certification_id: str = ""
# ── consumed contract type (refinement 2) ─────────────────────────────────
class OperationalCapabilityCandidate(BaseModel):
"""The ONLY thing Reasoning consumes from Execution's capability mapping.
Named "operational" (organisational ability) to stay distinct from later
Product/AI/Safety capabilities. A candidate is always Welt 1 — DECLARED or
INFERRED — and never CONFIRMED on its own.
"""
capability_id: str
source: str
confidence: Confidence = Confidence.MEDIUM
verification_status: VerificationStatus = VerificationStatus.INFERRED
class OperationalCapability(BaseModel):
"""A capability the company actually has, CONFIRMED against real evidence."""
capability_id: str
verification_status: VerificationStatus
confidence: Confidence = Confidence.MEDIUM
sources: List[str] = Field(default_factory=list)
# ── the container Reasoning OWNS (raw inputs) ─────────────────────────────
class CompanyContext(BaseModel):
company_id: str
certifications: List[Certification] = Field(default_factory=list)
declarations: List[Declaration] = Field(default_factory=list)
processes: List[ExistingProcess] = Field(default_factory=list)
systems: List[ExistingSystem] = Field(default_factory=list)
evidence: List[ExistingEvidence] = Field(default_factory=list)
# ── derived view (the Company Capability Profile) ─────────────────────────
class CompanyCapabilityProfile(BaseModel):
"""Derived: capability evidence + candidates (declared/inferred) + confirmed.
`candidate_capabilities` NEVER auto-promote to `confirmed_capabilities`; only
explicit ExistingEvidence does that. The hard rule is enforced in engine.py.
"""
company_id: str
capability_evidence: List[CapabilityEvidence] = Field(default_factory=list)
candidate_capabilities: List[OperationalCapabilityCandidate] = Field(default_factory=list)
confirmed_capabilities: List[OperationalCapability] = Field(default_factory=list)
@@ -0,0 +1,18 @@
"""Interpretation-in-Map — evaluate a customer interpretation within the map.
Thin adapter over the existing `assess_interpretation`: it judges the customer's
reading against the regulations/obligations actually present in the product's
RegulatoryMap, and flags touched unsupported domains as future_corpus_needed
instead of pseudo-evaluating them. No new legal reasoning, no RCI, no UI.
"""
from __future__ import annotations
from .adapter import interpret_in_map
from .schemas import InterpretationInMapRequest, InterpretationInMapResult
__all__ = [
"interpret_in_map",
"InterpretationInMapRequest",
"InterpretationInMapResult",
]
@@ -0,0 +1,90 @@
"""Interpretation-in-Map adapter (step 5).
Evaluates a customer interpretation WITHIN the already-built RegulatoryMap. It
reuses the existing `assess_interpretation` (no new legal engine), restricts the
affected regulations/obligations to those present in the map, and reports any
touched unsupported domain (wastewater/chemicals/...) as future_corpus_needed
rather than pseudo-evaluating it.
"""
from __future__ import annotations
from typing import Dict, List
from compliance.reasoning.enums import InterpretationVerdict
from compliance.reasoning.interpretation_engine import assess_interpretation
from compliance.regulatory_map.schemas import RegulatoryMap
from .schemas import InterpretationInMapResult
_LABEL: Dict[InterpretationVerdict, str] = {
InterpretationVerdict.PLAUSIBLE: "plausibel",
InterpretationVerdict.TOO_NARROW: "zu eng",
InterpretationVerdict.TOO_BROAD: "zu weit",
InterpretationVerdict.PARTIALLY_CORRECT: "teilweise korrekt",
InterpretationVerdict.UNSUPPORTED: "nicht belegt",
InterpretationVerdict.UNCERTAIN: "unsicher",
}
# domain -> keywords that signal the interpretation is ABOUT that (uncovered) domain.
_ENV_KEYWORDS: Dict[str, List[str]] = {
"environment_water": ["abwasser", "wastewater", "gewässer", "gewaesser", "einleitung", "abfluss"],
"chemicals": ["chemikalie", "reach", "clp", "reinigungsmittel", "biozid", "gefahrstoff", "detergenz", "lösemittel", "loesemittel"],
"environment_air": ["luft", "emission", "voc", "immission", "abluft", "verbrennung"],
"waste": ["abfall", "entsorgung", "weee", "recycling"],
"energy_resources": ["energie", "ökodesign", "oekodesign", "verbrauch"],
}
def _touches(text: str, domain: str) -> bool:
low = text.lower()
return any(kw in low for kw in _ENV_KEYWORDS.get(domain, []))
def _explain(label: str, detail: str, affected_regs: List[str], future_domains: List[str], in_scope: bool) -> str:
base = "Ihre Interpretation ist wahrscheinlich %s." % label
if detail:
base += " " + detail
if affected_regs:
base += " Betroffen in Ihrer Map: %s." % ", ".join(affected_regs)
if future_domains:
base += (
" Für %s liegt noch kein Regelkorpus vor — diese Aspekte werden nicht bewertet (future_corpus_needed)."
% ", ".join(future_domains)
)
if not in_scope and not future_domains:
base += " Diese Auslegung betrifft kein Regelwerk Ihrer aktuellen Produkt-Map."
return base
def interpret_in_map(reg_map: RegulatoryMap, interpretation: str) -> InterpretationInMapResult:
a = assess_interpretation(interpretation) # existing engine — no new reasoning
map_reg_ids = (
{v.regulation_id for v in reg_map.applicable_regulations}
| {v.regulation_id for v in reg_map.uncertain_regulations}
| {v.regulation_id for v in reg_map.excluded_regulations}
)
map_ob_ids = {o.obligation_id for v in reg_map.applicable_regulations for o in v.obligations}
uncertain_ids = {v.regulation_id for v in reg_map.uncertain_regulations}
affected_regs = [r for r in a.affected_regulations if r in map_reg_ids]
affected_obs = [o for o in a.affected_obligations if o in map_ob_ids]
related_unc = [r for r in a.affected_regulations if r in uncertain_ids]
future = [d for d in reg_map.unsupported_domains if _touches(interpretation, d.domain)]
in_scope = bool(affected_regs or affected_obs)
return InterpretationInMapResult(
raw_interpretation=interpretation,
assessment=a.assessment,
in_scope_of_map=in_scope,
affected_regulations=affected_regs,
affected_obligations=affected_obs,
related_uncertainties=related_unc,
future_corpus_domains=future,
corrected_interpretation=a.corrected_interpretation,
risks=a.risks,
legal_basis_refs=a.legal_basis_refs,
explanation=_explain(_LABEL[a.assessment], a.explanation, affected_regs, [d.domain for d in future], in_scope),
confidence=a.confidence,
)
@@ -0,0 +1,36 @@
"""Schemas for Interpretation-in-Map (step 5).
A thin adapter that evaluates a customer interpretation WITHIN the already-built
RegulatoryMap — it does not assess abstract legal questions. Application types
only; no compliance-meta-model classes (freeze v1.0 untouched).
"""
from __future__ import annotations
from typing import List
from pydantic import BaseModel, Field
from compliance.product_scope.schemas import UnsupportedDomain
from compliance.profile.canonical import CanonicalProductRegulatoryProfile
from compliance.reasoning.enums import Confidence, InterpretationVerdict
class InterpretationInMapRequest(BaseModel):
product_profile: CanonicalProductRegulatoryProfile
customer_interpretation: str
class InterpretationInMapResult(BaseModel):
raw_interpretation: str
assessment: InterpretationVerdict
in_scope_of_map: bool # True if it touches a regulation/obligation present in the map
affected_regulations: List[str] = Field(default_factory=list) # intersected with the map
affected_obligations: List[str] = Field(default_factory=list) # intersected (registry-linked)
related_uncertainties: List[str] = Field(default_factory=list) # map-uncertain regs it touches
future_corpus_domains: List[UnsupportedDomain] = Field(default_factory=list) # NOT evaluated
corrected_interpretation: str = ""
risks: List[str] = Field(default_factory=list)
legal_basis_refs: List[str] = Field(default_factory=list)
explanation: str = ""
confidence: Confidence = Confidence.MEDIUM
@@ -0,0 +1,29 @@
"""Product Regulatory Navigator — thin missing-facts layer.
Sits above the CanonicalProductRegulatoryProfile (prefilled from company-profile /
ProductWizard) and reports only which facts are still missing + prioritized
questions to collect them. It decides which facts are needed, NOT what regulation
applies — that stays with the Scope Engine (step 3). No regulation logic, no UI,
no Go, no RAG.
"""
from __future__ import annotations
from .engine import CompletenessSummary, NavigatorResult, apply_answers, navigate
from .questions import (
QUESTION_CATALOG,
AnswerType,
NavigatorQuestion,
QuestionPriority,
)
__all__ = [
"navigate",
"apply_answers",
"NavigatorResult",
"CompletenessSummary",
"NavigatorQuestion",
"AnswerType",
"QuestionPriority",
"QUESTION_CATALOG",
]
@@ -0,0 +1,116 @@
"""Product Regulatory Navigator engine — missing-facts only.
`navigate(profile)` reports which canonical fields are still unknown and the
prioritized questions to fill them. `apply_answers(profile, answers)` returns the
updated profile. It NEVER decides what applies — that is the Scope Engine (step 3).
Pure field-presence checking; no scope-engine import, no regulation evaluation.
"""
from __future__ import annotations
from typing import Any, Dict, List, Type
from pydantic import BaseModel, Field
from compliance.profile.canonical import (
CanonicalLifecyclePhase,
CanonicalProductRegulatoryProfile,
EconomicOperatorRole,
ProductComponent,
)
from .questions import QUESTION_CATALOG, NavigatorQuestion, QuestionPriority
_ENUM_FIELDS: Dict[str, Type[Any]] = {
"economic_operator_role": EconomicOperatorRole,
"lifecycle_phase": CanonicalLifecyclePhase,
}
class CompletenessSummary(BaseModel):
total_relevant: int
answered: int
missing: int
missing_by_priority: Dict[str, int] = Field(default_factory=dict)
ready_for_scope: bool # True once no P0 fact is missing
note: str = ""
class NavigatorResult(BaseModel):
missing_facts: List[str] = Field(default_factory=list) # canonical target fields
suggested_questions: List[NavigatorQuestion] = Field(default_factory=list)
completeness_summary: CompletenessSummary
def _value(profile: CanonicalProductRegulatoryProfile, dotted: str) -> Any:
if "." in dotted:
head, tail = dotted.split(".", 1)
return getattr(getattr(profile, head), tail, None)
return getattr(profile, dotted, None)
def _is_unknown(profile: CanonicalProductRegulatoryProfile, q: NavigatorQuestion) -> bool:
value = _value(profile, q.target_field)
if value is None:
return True
if isinstance(value, list) and not value:
return True
return False
def navigate(profile: CanonicalProductRegulatoryProfile) -> NavigatorResult:
missing = [q for q in QUESTION_CATALOG if _is_unknown(profile, q)]
missing.sort(key=lambda q: q.order())
by_priority: Dict[str, int] = {}
for q in missing:
by_priority[q.priority.value] = by_priority.get(q.priority.value, 0) + 1
ready = QuestionPriority.P0.value not in by_priority
total = len(QUESTION_CATALOG)
summary = CompletenessSummary(
total_relevant=total,
answered=total - len(missing),
missing=len(missing),
missing_by_priority=by_priority,
ready_for_scope=ready,
note=(
"%d von %d Fakten vorhanden; %d offen. Scope-Engine startklar: %s."
% (total - len(missing), total, len(missing), "ja" if ready else "nein (P0 fehlt)")
),
)
return NavigatorResult(
missing_facts=[q.target_field for q in missing],
suggested_questions=missing,
completeness_summary=summary,
)
def _coerce(q: NavigatorQuestion, value: Any) -> Any:
if q.target_field in _ENUM_FIELDS:
return _ENUM_FIELDS[q.target_field](value)
if q.target_field == "components":
return [c if isinstance(c, ProductComponent) else ProductComponent(**c) for c in (value or [])]
if q.answer_type.value in {"country_list", "multiselect"}:
return list(value or [])
if q.answer_type.value == "bool":
return bool(value)
return value
def apply_answers(
profile: CanonicalProductRegulatoryProfile, answers: Dict[str, Any]
) -> CanonicalProductRegulatoryProfile:
updated = profile.model_copy(deep=True)
by_id = {q.question_id: q for q in QUESTION_CATALOG}
for question_id, raw in answers.items():
q = by_id.get(question_id)
if q is None or raw is None:
continue
value = _coerce(q, raw)
if "." in q.target_field:
head, tail = q.target_field.split(".", 1)
setattr(getattr(updated, head), tail, value)
else:
setattr(updated, q.target_field, value)
return updated
@@ -0,0 +1,171 @@
"""Product Regulatory Navigator — question catalog.
The Navigator is a THIN missing-facts layer over CanonicalProductRegulatoryProfile.
It does NOT decide what applies — `regulatory_domains_unblocked` is static metadata
(which domains a fact would help the Scope Engine decide later), never an
evaluation. No regulation logic, no UI, no Go, no RAG.
`NavigatorQuestion` is an interaction type, NOT a compliance-meta-model class
(architecture freeze v1.0 untouched).
"""
from __future__ import annotations
from enum import Enum
from typing import List
from pydantic import BaseModel, Field
from compliance.profile.canonical import CanonicalLifecyclePhase, EconomicOperatorRole
class AnswerType(str, Enum):
BOOL = "bool"
ENUM = "enum"
MULTISELECT = "multiselect"
TEXT = "text"
COUNTRY_LIST = "country_list"
COMPONENT_LIST = "component_list"
class QuestionPriority(str, Enum):
P0 = "P0" # blocks scope: EU-vs-not, role, lifecycle, machine/component
P1 = "P1" # unblocks a specific domain: RED, Data Act, environment, security
P2 = "P2" # refinement: structured BOM
_PRIORITY_ORDER = {QuestionPriority.P0: 0, QuestionPriority.P1: 1, QuestionPriority.P2: 2}
class NavigatorQuestion(BaseModel):
question_id: str
target_field: str # dotted path into the canonical profile
label: str
why_needed: str
regulatory_domains_unblocked: List[str] = Field(default_factory=list)
answer_type: AnswerType
options: List[str] = Field(default_factory=list)
priority: QuestionPriority
def order(self) -> int:
return _PRIORITY_ORDER[self.priority]
_ROLE_OPTIONS = [e.value for e in EconomicOperatorRole]
_PHASE_OPTIONS = [e.value for e in CanonicalLifecyclePhase]
QUESTION_CATALOG: List[NavigatorQuestion] = [
# ── P0: block the scope decision itself ───────────────────────────
NavigatorQuestion(
question_id="markets",
target_field="markets",
label="In welche Märkte / Länder liefern Sie das Produkt?",
why_needed="Bestimmt EU- vs. Nicht-EU-Anwendbarkeit und nationale Pflichten.",
regulatory_domains_unblocked=["cyber", "machine_safety", "data", "radio", "emv", "environment"],
answer_type=AnswerType.COUNTRY_LIST,
priority=QuestionPriority.P0,
),
NavigatorQuestion(
question_id="economic_operator_role",
target_field="economic_operator_role",
label="Welche Rolle nehmen Sie ein?",
why_needed="Pflichten hängen von der Rolle ab (Hersteller/Importeur/Händler/Betreiber/Service).",
regulatory_domains_unblocked=["cyber", "machine_safety", "data"],
answer_type=AnswerType.ENUM,
options=_ROLE_OPTIONS,
priority=QuestionPriority.P0,
),
NavigatorQuestion(
question_id="lifecycle_phase",
target_field="lifecycle_phase",
label="In welcher Lebenszyklusphase betrachten Sie das Produkt?",
why_needed="Manche Pflichten greifen nur beim Inverkehrbringen oder in der Wartung.",
regulatory_domains_unblocked=["cyber", "machine_safety"],
answer_type=AnswerType.ENUM,
options=_PHASE_OPTIONS,
priority=QuestionPriority.P0,
),
NavigatorQuestion(
question_id="is_machine",
target_field="is_machine",
label="Ist das Produkt eine (vollständige) Maschine?",
why_needed="Entscheidet die Anwendbarkeit der Maschinenverordnung.",
regulatory_domains_unblocked=["machine_safety"],
answer_type=AnswerType.BOOL,
priority=QuestionPriority.P0,
),
NavigatorQuestion(
question_id="is_component",
target_field="is_component",
label="Ist das Produkt ein Bauteil / eine unvollständige Maschine?",
why_needed="Sicherheitsbauteil vs. vollständige Maschine ändert die Pflichten.",
regulatory_domains_unblocked=["machine_safety"],
answer_type=AnswerType.BOOL,
priority=QuestionPriority.P0,
),
# ── P1: unblock one specific domain ───────────────────────────────
NavigatorQuestion(
question_id="has_radio_module",
target_field="has_radio_module",
label="Enthält das Produkt ein Funkmodul (WLAN/Bluetooth/Mobilfunk)?",
why_needed="Ein Funkmodul löst die Funkanlagen-Richtlinie (RED) aus.",
regulatory_domains_unblocked=["radio"],
answer_type=AnswerType.BOOL,
priority=QuestionPriority.P1,
),
NavigatorQuestion(
question_id="generates_usage_data",
target_field="generates_usage_data",
label="Erzeugt das vernetzte Produkt nutzbare Produkt-/Nutzungsdaten?",
why_needed="Erzeugte Nutzungsdaten entscheiden über Data-Act-Pflichten.",
regulatory_domains_unblocked=["data"],
answer_type=AnswerType.BOOL,
priority=QuestionPriority.P1,
),
NavigatorQuestion(
question_id="has_security_function",
target_field="has_security_function",
label="Hat das Produkt eine dedizierte Security-Funktion (gegen böswillige Akteure)?",
why_needed="Trennt Security- von Safety-Funktion (CRA vs. MaschinenVO).",
regulatory_domains_unblocked=["cyber", "machine_safety"],
answer_type=AnswerType.BOOL,
priority=QuestionPriority.P1,
),
NavigatorQuestion(
question_id="env_wastewater",
target_field="environmental.discharges_to_wastewater",
label="Gibt das Produkt Stoffe an Wasser / Abwasser ab?",
why_needed="Abwassereinleitung löst Abwasser-/Gewässerrecht aus.",
regulatory_domains_unblocked=["environment_water"],
answer_type=AnswerType.BOOL,
priority=QuestionPriority.P1,
),
NavigatorQuestion(
question_id="env_air",
target_field="environmental.emits_to_air",
label="Entstehen Luftemissionen (VOC, Staub, Verbrennung, Aerosole)?",
why_needed="Luftemissionen lösen Immissionsschutzrecht aus.",
regulatory_domains_unblocked=["environment_air"],
answer_type=AnswerType.BOOL,
priority=QuestionPriority.P1,
),
NavigatorQuestion(
question_id="env_chemicals",
target_field="environmental.uses_cleaning_chemicals",
label="Werden Reinigungs-, Desinfektions- oder Biozidmittel verwendet/mitgeliefert?",
why_needed="Chemikalien lösen REACH/CLP/Detergenzien-/Biozidrecht aus.",
regulatory_domains_unblocked=["chemicals"],
answer_type=AnswerType.BOOL,
priority=QuestionPriority.P1,
),
# ── P2: refinement ────────────────────────────────────────────────
NavigatorQuestion(
question_id="components",
target_field="components",
label="Aus welchen wesentlichen Komponenten besteht das Produkt?",
why_needed="Eine strukturierte Stückliste verfeinert komponenten-abgeleitete Pflichten.",
regulatory_domains_unblocked=["radio", "emv", "environment_water", "chemicals"],
answer_type=AnswerType.COMPONENT_LIST,
priority=QuestionPriority.P2,
),
]
@@ -0,0 +1,26 @@
"""Product-scope orchestration (step 3).
Connects the Navigator's fact-gate to the existing reasoning `discover_scope`:
decide regulatory scope only once the minimum (P0) facts are present, otherwise
return the missing facts. Reuses discover_scope unchanged — no new scope logic.
"""
from __future__ import annotations
from .orchestrator import resolve_product_scope
from .schemas import (
ProductScopeRequest,
ProductScopeResponse,
RegulatoryScopeResult,
ScopeStatus,
UnsupportedDomain,
)
__all__ = [
"resolve_product_scope",
"ProductScopeRequest",
"ProductScopeResponse",
"RegulatoryScopeResult",
"UnsupportedDomain",
"ScopeStatus",
]
@@ -0,0 +1,77 @@
"""Product-scope orchestrator (step 3) — gate, then reuse discover_scope.
THE rule: the Scope Engine decides only once the Navigator has released the
minimum facts. If P0 facts are missing, return the missing facts/questions and do
NOT run discover_scope. Otherwise project the canonical into the reasoning profile
and run the EXISTING `discover_scope` exactly once.
No new scope rules, no new regulations, no environmental-law evaluation (those
domains are surfaced only as unsupported_domains / future_corpus_needed).
"""
from __future__ import annotations
from typing import List, Tuple
from compliance.navigator.engine import navigate
from compliance.profile.canonical import CanonicalProductRegulatoryProfile
from compliance.profile.to_reasoning import to_reasoning_profile
from compliance.reasoning.scope_engine import discover_scope
from .schemas import (
ProductScopeResponse,
RegulatoryScopeResult,
ScopeStatus,
UnsupportedDomain,
)
# environmental trigger field -> (domain, note). Transparency only — not a verdict.
_ENV_DOMAINS: List[Tuple[str, str, str]] = [
("discharges_to_wastewater", "environment_water", "Abwasser-/Gewässerrecht (z. B. AbwV, WRRL) — noch nicht im Korpus."),
("has_cooling_or_spraying_water", "environment_water", "Wasserbezogene Anforderungen — noch nicht im Korpus."),
("emits_to_air", "environment_air", "Immissionsschutz-/Luftreinhalterecht (z. B. BImSchG, IED) — noch nicht im Korpus."),
("uses_solvents", "environment_air", "Lösemittel-/VOC-Recht (z. B. 31. BImSchV) — noch nicht im Korpus."),
("uses_cleaning_chemicals", "chemicals", "Chemikalienrecht (REACH/CLP/Detergenzien/Biozide) — noch nicht im Korpus."),
("supplies_chemicals", "chemicals", "Chemikalienrecht (REACH/CLP) — noch nicht im Korpus."),
("contains_restricted_substances", "chemicals", "Stoffbeschränkungen (REACH/RoHS) — noch nicht im Korpus."),
("creates_waste", "waste", "Abfall-/Entsorgungsrecht (u. a. WEEE) — noch nicht im Korpus."),
("consumes_energy_or_water", "energy_resources", "Energie-/Ökodesign-Recht — noch nicht im Korpus."),
]
def _unsupported_domains(profile: CanonicalProductRegulatoryProfile) -> List[UnsupportedDomain]:
env = profile.environmental
seen = set()
out: List[UnsupportedDomain] = []
for field, domain, note in _ENV_DOMAINS:
if getattr(env, field) is True and domain not in seen:
seen.add(domain)
out.append(UnsupportedDomain(domain=domain, trigger=field, note=note))
return out
def resolve_product_scope(profile: CanonicalProductRegulatoryProfile) -> ProductScopeResponse:
nav = navigate(profile)
if not nav.completeness_summary.ready_for_scope:
return ProductScopeResponse(
status=ScopeStatus.NEEDS_FACTS,
completeness_summary=nav.completeness_summary,
missing_facts=nav.missing_facts,
suggested_questions=nav.suggested_questions,
)
scope = discover_scope(to_reasoning_profile(profile)) # exactly once
result = RegulatoryScopeResult(
applicable_regulations=scope.applicable_regulations,
excluded_regulations=scope.excluded_regulations,
uncertain_regulations=scope.uncertain_regulations,
unsupported_domains=_unsupported_domains(profile),
reasoning_summary=scope.reasoning_summary,
confidence=scope.confidence,
)
return ProductScopeResponse(
status=ScopeStatus.RESOLVED,
completeness_summary=nav.completeness_summary,
regulatory_scope=result,
)
@@ -0,0 +1,63 @@
"""Response schemas for the product-scope orchestrator (step 3).
These are application/API types — NOT compliance-meta-model classes (architecture
freeze v1.0 untouched). The scope verdict itself is produced by the existing
`discover_scope`; nothing here adds scope rules.
"""
from __future__ import annotations
from enum import Enum
from typing import List, Optional
from pydantic import BaseModel, Field
from compliance.navigator.engine import CompletenessSummary
from compliance.navigator.questions import NavigatorQuestion
from compliance.profile.canonical import CanonicalProductRegulatoryProfile
from compliance.reasoning.enums import Confidence
from compliance.reasoning.schemas import (
ApplicableRegulation,
ExcludedRegulation,
UncertainRegulation,
)
class ScopeStatus(str, Enum):
NEEDS_FACTS = "needs_facts" # P0 facts missing -> ask, do not decide
RESOLVED = "resolved" # minimum facts present -> scope decided
class UnsupportedDomain(BaseModel):
"""A domain the product triggers but the corpus does not yet cover.
Surfaced for transparency (no false completeness) — NEVER a legal evaluation.
"""
domain: str
trigger: str
status: str = "future_corpus_needed"
note: str = ""
class RegulatoryScopeResult(BaseModel):
applicable_regulations: List[ApplicableRegulation] = Field(default_factory=list)
excluded_regulations: List[ExcludedRegulation] = Field(default_factory=list)
uncertain_regulations: List[UncertainRegulation] = Field(default_factory=list)
unsupported_domains: List[UnsupportedDomain] = Field(default_factory=list)
reasoning_summary: str = ""
confidence: Confidence = Confidence.MEDIUM
class ProductScopeRequest(BaseModel):
product_profile: CanonicalProductRegulatoryProfile
class ProductScopeResponse(BaseModel):
status: ScopeStatus
completeness_summary: CompletenessSummary
# case NEEDS_FACTS
missing_facts: List[str] = Field(default_factory=list)
suggested_questions: List[NavigatorQuestion] = Field(default_factory=list)
# case RESOLVED
regulatory_scope: Optional[RegulatoryScopeResult] = None
@@ -0,0 +1,38 @@
"""Product profile convergence layer.
ONE canonical product profile (`CanonicalProductRegulatoryProfile`) that the Go
gap engine and the Python reasoning engine both project from so "SPS mit
Remote Access" means the same thing everywhere. gap.ProductProfile leads; the
reasoning ProductProfile is an adapter/DTO. Types + mappers only no regulation
logic, no UI, no new questions.
"""
from __future__ import annotations
from .canonical import (
CanonicalLifecyclePhase,
CanonicalProductRegulatoryProfile,
CanonicalProductType,
ComponentKind,
EconomicOperatorRole,
EnvironmentalImpact,
ProductComponent,
)
from .from_company_profile import from_company_profile
from .from_product_wizard import from_product_wizard
from .to_gap import to_gap_profile
from .to_reasoning import to_reasoning_profile
__all__ = [
"CanonicalProductRegulatoryProfile",
"CanonicalProductType",
"EconomicOperatorRole",
"CanonicalLifecyclePhase",
"ComponentKind",
"ProductComponent",
"EnvironmentalImpact",
"from_product_wizard",
"from_company_profile",
"to_gap_profile",
"to_reasoning_profile",
]
@@ -0,0 +1,158 @@
"""CanonicalProductRegulatoryProfile — the single semantic product profile.
Convergence layer (spec 2026-06-26): instead of letting the Go `gap.ProductProfile`
and the Python reasoning `ProductProfile` drift, ONE canonical type is the source
of truth. The Go gap engine LEADS (it carries real engine logic), so the canonical
mirrors gap's field names and adds the Navigator gaps the audit found missing
(economic-operator role, radio module, generates_usage_data, lifecycle phase,
structured BOM, safety-vs-security split, machine-vs-component) plus a
forward-looking Environmental-Impact domain.
No regulation logic lives here types only. Mappers live in sibling modules.
Python 3.9 compatible (no `|` unions).
"""
from __future__ import annotations
from enum import Enum
from typing import List, Optional
from pydantic import BaseModel, Field
class CanonicalProductType(str, Enum): # mirrors gap.ProductType
SOFTWARE = "software"
HARDWARE = "hardware"
IOT = "iot"
SAAS = "saas"
EXCHANGE = "exchange"
MEDICAL_DEVICE = "medical_device"
MACHINERY = "machinery"
OTHER = "other"
class EconomicOperatorRole(str, Enum): # CE/CRA role — gap.ProductProfile has none
MANUFACTURER = "manufacturer"
IMPORTER = "importer"
DISTRIBUTOR = "distributor"
INTEGRATOR = "integrator"
OPERATOR = "operator"
SERVICE_PROVIDER = "service_provider"
class CanonicalLifecyclePhase(str, Enum):
DEVELOPMENT = "development"
PLACING_ON_MARKET = "placing_on_market"
OPERATION = "operation"
MAINTENANCE = "maintenance"
UPDATE = "update"
END_OF_LIFE = "end_of_life"
class ComponentKind(str, Enum):
MOTOR = "motor"
PUMP = "pump"
HEATING = "heating"
COOLING = "cooling"
CONTROLLER = "controller"
PLC = "plc"
HMI = "hmi"
SENSOR = "sensor"
ACTUATOR = "actuator"
CAMERA = "camera"
NETWORK_INTERFACE = "network_interface"
RADIO_MODULE = "radio_module"
CHEMICAL_DOSING = "chemical_dosing"
WATER_INLET = "water_inlet"
WASTEWATER_OUTLET = "wastewater_outlet"
BATTERY = "battery"
OTHER = "other"
class ProductComponent(BaseModel):
"""One structured BOM node — these nodes are what later trigger domains."""
name: str
kind: ComponentKind = ComponentKind.OTHER
notes: Optional[str] = None
class EnvironmentalImpact(BaseModel):
"""Forward-looking Umweltmedien-Trigger (own Navigator domain).
No regulation logic consumes these yet profile fields only, so the model
is not blind to wastewater/air/chemicals/waste questions when that domain
is wired later (AbwV/WRRL/REACH/CLP/IED/BImSchG ...).
"""
discharges_to_wastewater: Optional[bool] = None
uses_cleaning_chemicals: Optional[bool] = None
supplies_chemicals: Optional[bool] = None
emits_to_air: Optional[bool] = None
uses_solvents: Optional[bool] = None
creates_waste: Optional[bool] = None
contains_restricted_substances: Optional[bool] = None
consumes_energy_or_water: Optional[bool] = None
has_cooling_or_spraying_water: Optional[bool] = None
class CanonicalProductRegulatoryProfile(BaseModel):
# --- identity ---
name: str = ""
description: str = ""
product_type: Optional[CanonicalProductType] = None
product_profile_id: Optional[str] = None
tenant_id: Optional[str] = None
iace_project_id: Optional[str] = None
# --- gap-native lists ---
technologies: List[str] = Field(default_factory=list)
data_processing: List[str] = Field(default_factory=list)
markets: List[str] = Field(default_factory=list) # real list — never hardcoded ['EU']
existing_certifications: List[str] = Field(default_factory=list)
applied_norms: List[str] = Field(default_factory=list)
# --- gap-native product / IST-state booleans (tri-state: None = unknown) ---
connected_to_internet: Optional[bool] = None
has_software_updates: Optional[bool] = None
uses_ai: Optional[bool] = None
processes_personal_data: Optional[bool] = None
is_critical_infra_supplier: Optional[bool] = None
has_risk_assessment: Optional[bool] = None
has_technical_file: Optional[bool] = None
has_operating_manual: Optional[bool] = None
has_sbom: Optional[bool] = None
has_vuln_management: Optional[bool] = None
has_update_mechanism: Optional[bool] = None
has_incident_response: Optional[bool] = None
has_supply_chain_mgmt: Optional[bool] = None
ce_marking_since: Optional[str] = None
product_age: Optional[str] = None
# --- NEW Navigator-gap fields (audit 2026-06-26) ---
economic_operator_role: Optional[EconomicOperatorRole] = None
has_radio_module: Optional[bool] = None
generates_usage_data: Optional[bool] = None
lifecycle_phase: Optional[CanonicalLifecyclePhase] = None
components: List[ProductComponent] = Field(default_factory=list)
has_safety_function: Optional[bool] = None
safety_function_description: Optional[str] = None
has_security_function: Optional[bool] = None # safety vs security split
has_remote_access: Optional[bool] = None
has_embedded_software: Optional[bool] = None
is_machine: Optional[bool] = None
is_component: Optional[bool] = None
is_spare_part: Optional[bool] = None
# --- company / market context (NIS2 + scope; from company-profile) ---
b2b_or_b2c: Optional[str] = None
sector_industry: Optional[str] = None
company_size: Optional[str] = None
primary_jurisdiction: Optional[str] = None
# --- AI context (classification stays delegated to ai-act/ucca) ---
ai_integration_type: List[str] = Field(default_factory=list)
human_oversight_level: Optional[str] = None
# --- forward-looking environmental domain ---
environmental: EnvironmentalImpact = Field(default_factory=EnvironmentalImpact)
@@ -0,0 +1,59 @@
"""company-profile -> CanonicalProductRegulatoryProfile (prefill, acceptance #2).
Pulls master data (industry, business model, size, markets) and the conditional
`machine_builder` block (camelCase JSONB keys, defined frontend-side) so the user
re-answers nothing. The machineBuilder block is the richest product/safety/
connectivity source note it is industry-gated in the UI, so a prefill may find
it empty; that is fine (fields stay None = unknown).
"""
from __future__ import annotations
from typing import Any, Dict, List
from .canonical import CanonicalProductRegulatoryProfile
_EU_MEMBER_HINTS = {"DE", "AT", "FR", "IT", "NL", "LU", "LI", "EU", "EWR", "EEA", "DACH"}
def _markets(p: Dict[str, Any], mb: Dict[str, Any]) -> List[str]:
out: List[str] = []
for source in (p.get("target_markets"), mb.get("exportMarkets"), [p.get("primary_jurisdiction")], [p.get("headquarters_country")]):
for m in source or []:
if m and m not in out:
out.append(m)
return out
def _is_machine(mb: Dict[str, Any]) -> Any:
types = mb.get("productTypes")
if types:
return True
return None
def from_company_profile(profile: Dict[str, Any]) -> CanonicalProductRegulatoryProfile:
p = profile
mb = p.get("machine_builder") or {}
contains_ai = mb.get("containsAI")
uses_ai = contains_ai if contains_ai is not None else p.get("uses_ai")
return CanonicalProductRegulatoryProfile(
description=mb.get("productDescription") or "",
sector_industry=p.get("industry") or None,
b2b_or_b2c=p.get("business_model") or None,
company_size=p.get("company_size") or None,
primary_jurisdiction=p.get("primary_jurisdiction") or None,
markets=_markets(p, mb),
uses_ai=uses_ai,
ai_integration_type=list(mb.get("aiIntegrationType") or []),
human_oversight_level=mb.get("humanOversightLevel") or None,
has_embedded_software=mb.get("containsFirmware"),
has_safety_function=mb.get("hasSafetyFunction"),
safety_function_description=mb.get("safetyFunctionDescription") or None,
has_remote_access=mb.get("hasRemoteAccess"),
connected_to_internet=mb.get("isNetworked"),
has_software_updates=mb.get("hasOTAUpdates"),
has_risk_assessment=mb.get("hasRiskAssessment"),
is_machine=_is_machine(mb),
is_critical_infra_supplier=mb.get("criticalSectorClients"),
)
@@ -0,0 +1,50 @@
"""ProductWizard payload -> CanonicalProductRegulatoryProfile (lossless).
The gap-analysis ProductWizard POSTs exactly the gap.ProductProfile JSON shape
(see admin-compliance/.../ProductWizard.tsx handleSubmit). This mapper copies
every gap field verbatim so that `to_gap_profile(from_product_wizard(p))`
reproduces the gap subset of `p` byte-for-byte (acceptance #1). New Navigator
fields the wizard does not ask stay None.
"""
from __future__ import annotations
from typing import Any, Dict, Optional
from .canonical import CanonicalProductRegulatoryProfile, CanonicalProductType
def _as_product_type(value: Any) -> Optional[CanonicalProductType]:
try:
return CanonicalProductType(value)
except ValueError:
return None
def from_product_wizard(payload: Dict[str, Any]) -> CanonicalProductRegulatoryProfile:
g = payload.get
return CanonicalProductRegulatoryProfile(
name=g("name", ""),
description=g("description", ""),
product_type=_as_product_type(g("product_type")),
technologies=list(g("technologies") or []),
data_processing=list(g("data_processing") or []),
markets=list(g("markets") or []),
existing_certifications=list(g("existing_certifications") or []),
applied_norms=list(g("applied_norms") or []),
connected_to_internet=g("connected_to_internet"),
has_software_updates=g("has_software_updates"),
uses_ai=g("uses_ai"),
processes_personal_data=g("processes_personal_data"),
is_critical_infra_supplier=g("is_critical_infra_supplier"),
has_risk_assessment=g("has_risk_assessment"),
has_technical_file=g("has_technical_file"),
has_operating_manual=g("has_operating_manual"),
has_sbom=g("has_sbom"),
has_vuln_management=g("has_vuln_management"),
has_update_mechanism=g("has_update_mechanism"),
has_incident_response=g("has_incident_response"),
has_supply_chain_mgmt=g("has_supply_chain_mgmt"),
ce_marking_since=g("ce_marking_since"),
product_age=g("product_age"),
)
@@ -0,0 +1,41 @@
"""CanonicalProductRegulatoryProfile -> gap.ProductProfile JSON shape.
Emits exactly the keys the Go gap engine already consumes (gap/models.go json
tags), so the gap engine runs UNCHANGED the canonical is a superset and gap is
its lossless projection. Canonical-only fields (role/radio/components/...) are
intentionally not emitted here; they reach the reasoning side via to_reasoning.
"""
from __future__ import annotations
from typing import Any, Dict
from .canonical import CanonicalProductRegulatoryProfile
def to_gap_profile(c: CanonicalProductRegulatoryProfile) -> Dict[str, Any]:
return {
"name": c.name,
"description": c.description,
"product_type": c.product_type.value if c.product_type else "",
"technologies": list(c.technologies),
"data_processing": list(c.data_processing),
"markets": list(c.markets),
"existing_certifications": list(c.existing_certifications),
"applied_norms": list(c.applied_norms),
"connected_to_internet": bool(c.connected_to_internet),
"has_software_updates": bool(c.has_software_updates),
"uses_ai": bool(c.uses_ai),
"processes_personal_data": bool(c.processes_personal_data),
"is_critical_infra_supplier": bool(c.is_critical_infra_supplier),
"has_risk_assessment": bool(c.has_risk_assessment),
"has_technical_file": bool(c.has_technical_file),
"has_operating_manual": bool(c.has_operating_manual),
"has_sbom": bool(c.has_sbom),
"has_vuln_management": bool(c.has_vuln_management),
"has_update_mechanism": bool(c.has_update_mechanism),
"has_incident_response": bool(c.has_incident_response),
"has_supply_chain_mgmt": bool(c.has_supply_chain_mgmt),
"ce_marking_since": c.ce_marking_since if c.ce_marking_since is not None else "",
"product_age": c.product_age if c.product_age is not None else "",
}
@@ -0,0 +1,88 @@
"""CanonicalProductRegulatoryProfile -> reasoning ProductProfile (adapter/DTO).
The reasoning engine stays the consumer, never the source of truth (spec): the
canonical leads, this projects it into the Python reasoning ProductProfile so the
Reasoning engine and the Go gap engine run off ONE semantic profile (acceptance
#10). AI classification is NOT done here — only `uses_ai` is forwarded; risk
classification stays delegated to ai-act/ucca (acceptance #3).
This is the ONLY one-way coupling profile -> reasoning; reasoning never imports
profile, so the reasoning layer stays hermetic.
"""
from __future__ import annotations
from typing import List, Optional
from compliance.reasoning.enums import ManufacturerRole, MarketModel, ProductLifecyclePhase
from compliance.reasoning.schemas import ProductProfile
from .canonical import CanonicalProductRegulatoryProfile, CanonicalProductType
_SOFTWARE_TYPES = {CanonicalProductType.SOFTWARE, CanonicalProductType.SAAS, CanonicalProductType.IOT}
_SOFTWARE_TECH = {"ai", "api", "database", "encryption", "ota_updates", "cloud", "blockchain"}
_EU_HINTS = {"DE", "AT", "FR", "IT", "NL", "LU", "LI", "EU", "EWR", "EEA", "DACH"}
_B2X = {"B2B": MarketModel.B2B, "B2C": MarketModel.B2C, "B2B_B2C": MarketModel.BOTH, "B2B2C": MarketModel.BOTH}
def _or_none(*values: Optional[bool]) -> Optional[bool]:
"""True if any value is truthy; None if all are None/absent; else False."""
if any(v is True for v in values):
return True
if all(v is None for v in values):
return None
return False
def _has_software(c: CanonicalProductRegulatoryProfile) -> Optional[bool]:
type_sig = True if c.product_type in _SOFTWARE_TYPES else None
tech_sig = True if (set(c.technologies) & _SOFTWARE_TECH) else None
return _or_none(c.has_embedded_software, c.has_software_updates, c.uses_ai, type_sig, tech_sig)
def _eu_market(markets: List[str]) -> Optional[bool]:
if not markets:
return None
return True if (set(markets) & _EU_HINTS) else False
def _has_radio(c: CanonicalProductRegulatoryProfile) -> Optional[bool]:
if c.has_radio_module is not None:
return c.has_radio_module
if any(comp.kind.value == "radio_module" for comp in c.components):
return True
return None
def to_reasoning_profile(c: CanonicalProductRegulatoryProfile) -> ProductProfile:
role = ManufacturerRole(c.economic_operator_role.value) if c.economic_operator_role else None
phase = ProductLifecyclePhase(c.lifecycle_phase.value) if c.lifecycle_phase else None
b2x = _B2X.get(c.b2b_or_b2c) if c.b2b_or_b2c else None
is_machine = c.is_machine if c.is_machine is not None else (
True if c.product_type == CanonicalProductType.MACHINERY else None
)
generates_data = c.generates_usage_data if c.generates_usage_data is not None else (
True if "telemetry" in c.data_processing else None
)
return ProductProfile(
product_name=c.name or "Produkt",
product_profile_id=c.product_profile_id,
manufacturer_role=role,
product_type=[c.product_type.value] if c.product_type else [],
has_software=_has_software(c),
has_embedded_software=c.has_embedded_software,
has_remote_access=c.has_remote_access,
has_cloud_connection=True if "cloud" in c.technologies else None,
has_ai_functionality=c.uses_ai,
has_radio_module=_has_radio(c),
has_safety_function=c.has_safety_function,
generates_usage_data=generates_data,
is_machine=is_machine,
is_component=c.is_component,
is_spare_part=c.is_spare_part,
eu_market=_eu_market(c.markets),
b2b_or_b2c=b2x,
lifecycle_phase=phase,
company_size=c.company_size,
sector=c.sector_industry,
)
@@ -0,0 +1,34 @@
"""Regulatory Change Intelligence (RCI) — delta layer over the product-first map.
Answers "what changes relative to my existing Regulatory Map?" NOT "what does
the new law say in general". Snapshot the pipeline into a ComplianceBaseline, then
assess a (simulated/provided) RegulatoryChange into per-obligation deltas + a
management ChangeImpactSummary. Read/reasoning only no UI, no ingestion, no RAG,
no new regulations/controls, no legal evaluation outside the stored map.
"""
from __future__ import annotations
from .baseline import create_baseline
from .delta_engine import assess_change
from .schemas import (
ChangeAssessment,
ChangeImpactSummary,
ChangeType,
ComplianceBaseline,
DeltaType,
ObligationDelta,
RegulatoryChange,
)
__all__ = [
"create_baseline",
"assess_change",
"ComplianceBaseline",
"RegulatoryChange",
"ObligationDelta",
"ChangeImpactSummary",
"ChangeAssessment",
"DeltaType",
"ChangeType",
]
@@ -0,0 +1,44 @@
"""Snapshot the current product-first pipeline into a ComplianceBaseline.
This is the ONLY place RCI runs the pipeline to freeze a point-in-time map +
registry-linked obligations + their required evidence. Everything downstream
(delta computation) works purely against this snapshot, never re-evaluating.
"""
from __future__ import annotations
from typing import Dict, List, Optional
from compliance.profile.canonical import CanonicalProductRegulatoryProfile
from compliance.profile.to_reasoning import to_reasoning_profile
from compliance.reasoning.obligation_engine import derive_obligations
from compliance.regulatory_map.renderer import render_regulatory_map
from .schemas import ComplianceBaseline
def create_baseline(
profile: CanonicalProductRegulatoryProfile,
evidence_refs: Optional[Dict[str, List[str]]] = None,
baseline_id: str = "baseline",
created_at: Optional[str] = None,
) -> ComplianceBaseline:
reg_map = render_regulatory_map(profile)
obligations = derive_obligations(to_reasoning_profile(profile)).applicable_obligations
applicable: List[str] = []
required: Dict[str, List[str]] = {}
for ob in obligations:
if ob.registry_anchor: # only registry-linked obligations enter the baseline
applicable.append(ob.obligation_id)
required[ob.obligation_id] = list(ob.required_evidence)
return ComplianceBaseline(
baseline_id=baseline_id,
product_profile_snapshot=profile,
regulatory_map_snapshot=reg_map,
applicable_obligations=applicable,
obligation_evidence_required=required,
evidence_refs=dict(evidence_refs or {}),
created_at=created_at,
)
@@ -0,0 +1,114 @@
"""RCI delta engine — assess a RegulatoryChange against a ComplianceBaseline.
Answers "what changes relative to my existing Map?" deterministically, working
ONLY against the stored baseline (no re-evaluation of scope, no new legal
assessment outside the map). Per-obligation classification -> ObligationDelta;
aggregate -> ChangeImpactSummary.
"""
from __future__ import annotations
from typing import List, Tuple
from compliance.reasoning.enums import Confidence
from .schemas import (
ChangeAssessment,
ChangeImpactSummary,
ChangeType,
ComplianceBaseline,
DeltaType,
ObligationDelta,
RegulatoryChange,
)
_ACTION = {DeltaType.NEW, DeltaType.CHANGED, DeltaType.NEEDS_REVIEW}
def _classify(
in_base: bool, has_ev: bool, change_type: ChangeType, rel_app: bool, rel_unc: bool
) -> Tuple[DeltaType, str, Confidence]:
if not (rel_app or rel_unc):
return DeltaType.NOT_APPLICABLE, "Die Änderung betrifft kein Regelwerk Ihrer Map.", Confidence.HIGH
if rel_unc and not rel_app:
return (
DeltaType.NEEDS_REVIEW,
"Betrifft ein für Ihr Produkt noch UNSICHERES Regelwerk — erst Anwendbarkeit klären.",
Confidence.LOW,
)
if change_type == ChangeType.REPEAL:
if in_base:
return DeltaType.REMOVED, "Regelwerk/Pflicht aufgehoben — entfällt für Ihr Produkt.", Confidence.HIGH
return DeltaType.NOT_APPLICABLE, "Aufhebung betrifft keine Ihrer bestehenden Pflichten.", Confidence.HIGH
if not in_base:
return DeltaType.NEW, "Neue Pflicht durch die Änderung — bisher nicht in Ihrer Map.", Confidence.MEDIUM
if change_type == ChangeType.GUIDANCE_UPDATE:
if has_ev:
return (
DeltaType.ALREADY_COVERED,
"Bestehende Pflicht mit vorhandenen Nachweisen — Leitlinien-Update vermutlich abgedeckt.",
Confidence.MEDIUM,
)
return DeltaType.NEEDS_REVIEW, "Bestehende Pflicht ohne Nachweis — Leitlinien-Update prüfen.", Confidence.MEDIUM
return DeltaType.CHANGED, "Bestehende Pflicht inhaltlich geändert — Umsetzung und Nachweis prüfen.", Confidence.MEDIUM
def assess_change(baseline: ComplianceBaseline, change: RegulatoryChange) -> ChangeAssessment:
snap = baseline.regulatory_map_snapshot
app_regs = {v.regulation_id for v in snap.applicable_regulations}
unc_regs = {v.regulation_id for v in snap.uncertain_regulations}
base_obs = set(baseline.applicable_obligations)
affected = set(change.affected_regulations)
rel_app = bool(affected & app_regs)
rel_unc = bool(affected & unc_regs)
affects_product = rel_app or rel_unc
deltas: List[ObligationDelta] = []
for ob in change.affected_obligations:
present = baseline.evidence_refs.get(ob, [])
required = baseline.obligation_evidence_required.get(ob, [])
dt, reason, conf = _classify(ob in base_obs, bool(present), change.change_type, rel_app, rel_unc)
missing = [e for e in required if e not in present] if dt in _ACTION else []
deltas.append(
ObligationDelta(
obligation_id=ob,
delta_type=dt,
reason=reason,
affected_evidence=list(present),
missing_evidence=missing,
confidence=conf,
)
)
return ChangeAssessment(
change_id=change.change_id,
affects_product=affects_product,
deltas=deltas,
summary=_summary(deltas, [d.domain for d in snap.unsupported_domains]),
)
def _ids(deltas: List[ObligationDelta], *types: DeltaType) -> List[str]:
wanted = set(types)
return [d.obligation_id for d in deltas if d.delta_type in wanted]
def _summary(deltas: List[ObligationDelta], unsupported: List[str]) -> ChangeImpactSummary:
n_new = len(_ids(deltas, DeltaType.NEW))
n_changed = len(_ids(deltas, DeltaType.CHANGED))
n_removed = len(_ids(deltas, DeltaType.REMOVED))
n_covered = len(_ids(deltas, DeltaType.ALREADY_COVERED))
n_review = len(_ids(deltas, DeltaType.NEEDS_REVIEW, DeltaType.CHANGED))
n_na = len(_ids(deltas, DeltaType.NOT_APPLICABLE))
return ChangeImpactSummary(
what_changed=(
"%d neu, %d geändert, %d entfällt, %d bereits abgedeckt, %d zu prüfen, %d nicht relevant."
% (n_new, n_changed, n_removed, n_covered, n_review, n_na)
),
what_matters_for_this_product=_ids(deltas, *_ACTION),
already_covered=_ids(deltas, DeltaType.ALREADY_COVERED),
needs_review=_ids(deltas, DeltaType.NEEDS_REVIEW, DeltaType.CHANGED),
not_relevant=_ids(deltas, DeltaType.NOT_APPLICABLE),
unsupported_domains=unsupported,
)
@@ -0,0 +1,92 @@
"""Regulatory Change Intelligence (RCI) — domain objects.
RCI is a read-/reasoning layer ON TOP of the product-first pipeline. It answers
"what changes relative to my existing Regulatory Map?" NOT "what does the new
law say in general". A RegulatoryChange is simulated/provided INPUT (no ingestion,
no newsletter/mailbox, no RAG); the delta is computed against a stored
ComplianceBaseline (snapshot of the map).
`delta_type` is a THIRD vocabulary distinct from `ClaimCoverage` (Welt 1, what
the customer claims) and `ComplianceStatus` (Welt 2, verified evidence). The three
must never be conflated. These are application/reasoning types, NOT
compliance-meta-model classes (architecture freeze v1.0 untouched).
"""
from __future__ import annotations
from enum import Enum
from typing import Dict, List, Optional
from pydantic import BaseModel, Field
from compliance.profile.canonical import CanonicalProductRegulatoryProfile
from compliance.reasoning.enums import AuthorityLevel, Confidence
from compliance.regulatory_map.schemas import RegulatoryMap
class DeltaType(str, Enum):
NEW = "new" # obligation now applies that was not in the baseline
CHANGED = "changed" # existing obligation substantively modified
REMOVED = "removed" # obligation no longer applies (repeal)
ALREADY_COVERED = "already_covered" # existing obligation, evidence likely suffices
NEEDS_REVIEW = "needs_review" # a human must check
NOT_APPLICABLE = "not_applicable" # change does not touch this product's map
class ChangeType(str, Enum):
NEW_REGULATION = "new_regulation"
AMENDMENT = "amendment"
REPEAL = "repeal"
GUIDANCE_UPDATE = "guidance_update"
# ── stored snapshot ──────────────────────────────────────────────────────
class ComplianceBaseline(BaseModel):
baseline_id: str
product_profile_snapshot: CanonicalProductRegulatoryProfile
regulatory_map_snapshot: RegulatoryMap
applicable_obligations: List[str] = Field(default_factory=list) # registry-linked obligation_ids
# required evidence per obligation (derived) — to compute missing_evidence
obligation_evidence_required: Dict[str, List[str]] = Field(default_factory=dict)
# evidence the customer ALREADY has, per obligation (provided)
evidence_refs: Dict[str, List[str]] = Field(default_factory=dict)
created_at: Optional[str] = None
# ── simulated/provided change (INPUT — never ingested) ───────────────────
class RegulatoryChange(BaseModel):
change_id: str
source: str = "simulated"
affected_regulations: List[str] = Field(default_factory=list)
affected_obligations: List[str] = Field(default_factory=list)
change_type: ChangeType
effective_date: Optional[str] = None
authority_level: AuthorityLevel = AuthorityLevel.LEGAL_TEXT
summary: str = ""
# ── per-obligation delta ─────────────────────────────────────────────────
class ObligationDelta(BaseModel):
obligation_id: str
delta_type: DeltaType
reason: str
affected_evidence: List[str] = Field(default_factory=list) # evidence already present for it
missing_evidence: List[str] = Field(default_factory=list) # required but not yet present
confidence: Confidence
# ── management-level summary ──────────────────────────────────────────────
class ChangeImpactSummary(BaseModel):
what_changed: str = ""
what_matters_for_this_product: List[str] = Field(default_factory=list) # need action
already_covered: List[str] = Field(default_factory=list)
needs_review: List[str] = Field(default_factory=list)
not_relevant: List[str] = Field(default_factory=list)
unsupported_domains: List[str] = Field(default_factory=list)
class ChangeAssessment(BaseModel):
change_id: str
affects_product: bool
deltas: List[ObligationDelta] = Field(default_factory=list)
summary: ChangeImpactSummary
@@ -0,0 +1,27 @@
"""Regulatory Reasoning Engine.
A deterministic reasoning layer ON TOP of the Legal Knowledge Graph (obligation
registry) and the Compliance Execution Graph (control mapping / evidence). It
answers, for a concrete product: which regulations apply, which obligations
follow, whether the customer's implementation covers them, and whether a
customer interpretation is legally sound.
No new RAG, no new controls, no DB schema changes scope & reasoning metamodel
only (spec §14).
"""
from __future__ import annotations
from .claim_normalizer import normalize_claim
from .implementation_engine import reason_implementation_claim
from .interpretation_engine import assess_interpretation
from .obligation_engine import derive_obligations
from .scope_engine import discover_scope
__all__ = [
"discover_scope",
"derive_obligations",
"normalize_claim",
"reason_implementation_claim",
"assess_interpretation",
]
@@ -0,0 +1,45 @@
"""Customer implementation claim normaliser (spec §4.6).
Turns a free-text statement ("Wir haben einen Update-Prozess.") into structured
capabilities + related topics + weakness qualifiers. Deterministic substring
matching the claim_id is a stable hash so the same statement always maps to
the same id (no randomness, replay-safe).
"""
from __future__ import annotations
import hashlib
from typing import List, Optional
from .schemas import CustomerImplementationClaim
from .taxonomy_claims import match_capabilities, match_qualifiers, topics_for
def _claim_id(raw_statement: str) -> str:
digest = hashlib.sha1(raw_statement.strip().lower().encode("utf-8")).hexdigest()
return "claim_%s" % digest[:10]
def _normalized(capabilities: List[str], qualifiers: List[str]) -> str:
if not capabilities:
return "Keine bekannte Compliance-Fähigkeit aus der Aussage ableitbar."
text = "Fähigkeiten: " + ", ".join(capabilities)
if qualifiers:
text += " | Einschränkungen: " + ", ".join(qualifiers)
return text
def normalize_claim(
raw_statement: str, claim_id: Optional[str] = None, evidence_refs: Optional[List[str]] = None
) -> CustomerImplementationClaim:
capabilities = match_capabilities(raw_statement)
qualifiers = match_qualifiers(raw_statement)
return CustomerImplementationClaim(
claim_id=claim_id or _claim_id(raw_statement),
raw_statement=raw_statement,
normalized_claim=_normalized(capabilities, qualifiers),
claimed_capability=capabilities,
related_topics=topics_for(capabilities),
qualifiers=qualifiers,
evidence_refs=evidence_refs or [],
)
@@ -0,0 +1,92 @@
"""Enumerations for the Regulatory Reasoning Engine.
Kept dependency-free and Python 3.9 compatible (str-Enums, no `|` unions).
The reasoning layer sits ON TOP of the Legal Knowledge Graph (obligation
registry) and the Compliance Execution Graph (control mapping / evidence).
See memory `project_compliance_graph.md` for the cross-session contract.
"""
from __future__ import annotations
from enum import Enum
class ManufacturerRole(str, Enum):
MANUFACTURER = "manufacturer"
IMPORTER = "importer"
DISTRIBUTOR = "distributor"
INTEGRATOR = "integrator"
OPERATOR = "operator"
SERVICE_PROVIDER = "service_provider"
class ProductLifecyclePhase(str, Enum):
DEVELOPMENT = "development"
PLACING_ON_MARKET = "placing_on_market"
OPERATION = "operation"
MAINTENANCE = "maintenance"
UPDATE = "update"
END_OF_LIFE = "end_of_life"
class MarketModel(str, Enum):
B2B = "b2b"
B2C = "b2c"
BOTH = "both"
class ApplicabilityStatus(str, Enum):
APPLICABLE = "applicable"
PARTIALLY_APPLICABLE = "partially_applicable"
UNCERTAIN = "uncertain"
NOT_APPLICABLE = "not_applicable"
class Confidence(str, Enum):
HIGH = "high"
MEDIUM = "medium"
LOW = "low"
class AuthorityLevel(str, Enum):
"""How binding a statement is — answers MUST visibly separate these."""
LEGAL_TEXT = "legal_text"
RECITAL = "recital"
GUIDANCE = "guidance"
HARMONIZED_STANDARD = "harmonized_standard"
TECHNICAL_STANDARD = "technical_standard"
BEST_PRACTICE = "best_practice"
INTERNAL_INTERPRETATION = "internal_interpretation"
class OverlapType(str, Enum):
IDENTICAL = "identical"
SIMILAR = "similar"
COMPLEMENTARY = "complementary"
CONFLICTING = "conflicting"
DIFFERENT_SCOPE = "different_scope"
class ClaimCoverage(str, Enum):
"""How a customer's *claim* relates to an obligation — Welt 1 (reasoning).
This is NOT a conformity verdict. It judges only the customer's statement,
never whether the obligation is actually met. The real compliance verdict
(erfüllt/offen/unklar from verified evidence) is `ComplianceStatus`, owned by
the Compliance Execution Graph the two must never be conflated.
"""
POTENTIALLY_ADDRESSES = "potentially_addresses"
PARTIALLY_ADDRESSES = "partially_addresses"
DOES_NOT_ADDRESS = "does_not_address"
INSUFFICIENT_INFORMATION = "insufficient_information"
class InterpretationVerdict(str, Enum):
PLAUSIBLE = "plausible"
TOO_NARROW = "too_narrow"
TOO_BROAD = "too_broad"
PARTIALLY_CORRECT = "partially_correct"
UNSUPPORTED = "unsupported"
UNCERTAIN = "uncertain"
@@ -0,0 +1,158 @@
"""Implementation reasoning (spec Modus 3) — Welt 1 only.
Maps a free-text claim ("Wir haben SBOMs und machen Updates, wenn Kunden Fehler
melden.") onto the product's applicable obligations and reports, per obligation,
whether the *claim* potentially/partially/does-not address it plus the
evidence that WOULD be needed to prove real implementation.
This is NOT a conformity verdict. It judges the customer's statement, never
whether the obligation is met. The real verdict (ComplianceStatus: erfüllt/
offen/unklar from verified evidence) lives in the Compliance Execution Graph.
The four reasoning layers: claim -> interpretation (capabilities/topics on the
claim) -> potential obligation coverage (`claim_coverage`) -> evidence required.
"""
from __future__ import annotations
from typing import Dict, List
from .claim_normalizer import normalize_claim
from .enums import ClaimCoverage, Confidence
from .obligation_engine import derive_obligations
from .schemas import (
ClaimObligationMapping,
CustomerImplementationClaim,
ImplementationReasoningResponse,
ProductProfile,
)
from .taxonomy_claims import topics_for
DISCLAIMER = (
"Diese Auswertung interpretiert ausschließlich die Kundenaussage (ClaimCoverage, Welt 1). "
"Sie ist KEINE Konformitätsaussage — der tatsächliche Compliance-Status (ComplianceStatus, "
"Welt 2) ergibt sich erst aus geprüften Nachweisen im Compliance Execution Graph."
)
# Typical sub-elements a capability still misses when only partially claimed.
STANDARD_GAPS: Dict[str, List[str]] = {
"software_bill_of_materials": [
"Vulnerability-Monitoring der Komponenten",
"Bewertung betroffener Komponenten",
"Lieferantenprozess",
],
"secure_updates": [
"aktive Schwachstellenüberwachung",
"Patch-Bewertung",
"Fristen und Verantwortlichkeiten",
"Nachweis der Updatefähigkeit",
],
"vulnerability_management": [
"definierter Vulnerability-Handling-Prozess",
"Priorisierung und Fristen",
],
"authentication": ["MFA für privilegierte Zugänge", "keine Standard-Zugangsdaten"],
"security_logging": ["Schutz der Logs vor Manipulation", "Monitoring/Alerting"],
"software_integrity": ["Signierung der Updates", "Verifikation der Update-Signatur"],
"secure_by_default": ["Härtung der Auslieferungskonfiguration", "Minimierung der Angriffsfläche"],
"secure_communication": ["verschlüsselte Übertragung", "Integritätsschutz der Verbindung"],
"risk_assessment": ["dokumentierte Risikobewertung", "Aufnahme in die technische Doku"],
"technical_documentation": ["vollständige technische Unterlagen", "Aktualisierung über den Lebenszyklus"],
}
def _missing_for(capabilities: List[str]) -> List[str]:
out: List[str] = []
for cap in capabilities:
for gap in STANDARD_GAPS.get(cap, []):
if gap not in out:
out.append(gap)
return out
def _coverage(required: List[str], claimed: List[str], qualifiers: List[str]) -> ClaimCoverage:
if not required:
return ClaimCoverage.INSUFFICIENT_INFORMATION
req, have = set(required), set(claimed)
hit = req & have
if not hit:
return ClaimCoverage.DOES_NOT_ADDRESS
if "absent" in qualifiers or "planned" in qualifiers:
return ClaimCoverage.DOES_NOT_ADDRESS
if "reactive" in qualifiers and hit & {"secure_updates", "vulnerability_management"}:
return ClaimCoverage.PARTIALLY_ADDRESSES
if req <= have:
return ClaimCoverage.POTENTIALLY_ADDRESSES
return ClaimCoverage.PARTIALLY_ADDRESSES
def reason_implementation_claim(
profile: ProductProfile, customer_claim: str
) -> ImplementationReasoningResponse:
claim = normalize_claim(customer_claim)
obligations = derive_obligations(profile).applicable_obligations
claimed = claim.claimed_capability
claim_topics = set(claim.related_topics) | set(claimed)
mappings: List[ClaimObligationMapping] = []
missing_evidence: List[str] = []
for ob in obligations:
from .rules_obligations import obligation_rule
rule = obligation_rule(ob.obligation_id)
required_caps = rule.required_capabilities if rule else []
ob_topics = set(topics_for(required_caps)) | set(required_caps)
directly_claimed = bool(set(required_caps) & set(claimed))
related = bool(ob_topics & claim_topics)
if not directly_claimed and not related:
continue # unrelated to the claim -> don't reason about it
coverage = _coverage(required_caps, claimed, claim.qualifiers)
missing = [] if coverage == ClaimCoverage.POTENTIALLY_ADDRESSES else _missing_for(required_caps)
if coverage != ClaimCoverage.POTENTIALLY_ADDRESSES:
for ev in ob.required_evidence:
if ev not in missing_evidence:
missing_evidence.append(ev)
mappings.append(
ClaimObligationMapping(
claim_id=claim.claim_id,
obligation_id=ob.obligation_id,
claim_coverage=coverage,
missing_elements=missing,
required_evidence=ob.required_evidence,
explanation=_explain(coverage, ob.title, claim.qualifiers),
confidence=Confidence.MEDIUM,
)
)
return ImplementationReasoningResponse(
claim=claim,
mappings=mappings,
missing_evidence=missing_evidence,
summary=_summary(claim, mappings),
disclaimer=DISCLAIMER,
)
def _explain(coverage: ClaimCoverage, title: str, qualifiers: List[str]) -> str:
if coverage == ClaimCoverage.POTENTIALLY_ADDRESSES:
return "Die Aussage adressiert die Pflicht '%s' direkt — Nachweise erforderlich für eine Bewertung der Umsetzung." % title
if coverage == ClaimCoverage.PARTIALLY_ADDRESSES:
extra = " Der beschriebene Prozess wirkt reaktiv." if "reactive" in qualifiers else ""
return "Die Aussage adressiert die Pflicht '%s' nur teilweise.%s" % (title, extra)
if coverage == ClaimCoverage.DOES_NOT_ADDRESS:
return "Die Aussage adressiert die Pflicht '%s' nicht." % title
return "Zur Pflicht '%s' liegen zu wenige Angaben für eine Einordnung vor." % title
def _summary(claim: CustomerImplementationClaim, mappings: List[ClaimObligationMapping]) -> str:
if not claim.claimed_capability:
return "Die Aussage ist zu unspezifisch — bitte konkretisieren, was umgesetzt wurde."
full = sum(1 for m in mappings if m.claim_coverage == ClaimCoverage.POTENTIALLY_ADDRESSES)
partial = sum(1 for m in mappings if m.claim_coverage == ClaimCoverage.PARTIALLY_ADDRESSES)
none = sum(1 for m in mappings if m.claim_coverage == ClaimCoverage.DOES_NOT_ADDRESS)
return (
"Die beschriebene Maßnahme adressiert wahrscheinlich %d Pflicht(en) direkt und %d "
"teilweise; %d werden durch die Aussage nicht berührt. Für eine Bewertung der tatsächlichen "
"Umsetzung sind Nachweise erforderlich. Dies ist keine Konformitätsaussage." % (full, partial, none)
)
@@ -0,0 +1,65 @@
"""Interpretation review engine (spec Modus 4).
Evaluates whether a customer's legal interpretation is plausible, too narrow,
too broad, etc. Matches the interpretation against a curated pattern library;
no match -> `uncertain` plus a request for the missing context (never invent a
verdict, spec §6.3).
"""
from __future__ import annotations
import hashlib
from typing import Optional
from .enums import Confidence, InterpretationVerdict
from .schemas import InterpretationAssessment, ProductProfile
from .taxonomy_interpretations import INTERPRETATION_PATTERNS, InterpretationPattern
def _interpretation_id(raw: str) -> str:
digest = hashlib.sha1(raw.strip().lower().encode("utf-8")).hexdigest()
return "interp_%s" % digest[:10]
def _best_match(text: str) -> Optional[InterpretationPattern]:
low = text.lower()
best: Optional[InterpretationPattern] = None
best_score = 0
for pattern in INTERPRETATION_PATTERNS:
score = sum(1 for t in pattern.triggers if t in low)
if score > best_score:
best, best_score = pattern, score
return best
def assess_interpretation(
raw_interpretation: str, profile: Optional[ProductProfile] = None
) -> InterpretationAssessment:
interp_id = _interpretation_id(raw_interpretation)
pattern = _best_match(raw_interpretation)
if pattern is None:
return InterpretationAssessment(
interpretation_id=interp_id,
raw_interpretation=raw_interpretation,
assessment=InterpretationVerdict.UNCERTAIN,
corrected_interpretation=(
"Diese Auslegung lässt sich ohne weitere Angaben nicht bewerten. Bitte Produkt, "
"Rolle, Marktzugang und die konkret betroffene Pflicht benennen."
),
explanation="Kein bekanntes Auslegungsmuster erkannt — bewusst keine Scheinsicherheit.",
confidence=Confidence.LOW,
)
return InterpretationAssessment(
interpretation_id=interp_id,
raw_interpretation=raw_interpretation,
affected_regulations=pattern.affected_regulations,
affected_obligations=pattern.affected_obligations,
assessment=pattern.verdict,
risks=pattern.risks,
corrected_interpretation=pattern.corrected_interpretation,
legal_basis_refs=pattern.legal_basis_refs,
explanation=pattern.explanation,
confidence=pattern.confidence,
)
@@ -0,0 +1,116 @@
"""Applicable-obligation engine (spec Modus 2).
Maps a product profile (optionally a precomputed scope) to the concrete legal
obligations, the overlaps between them, and which evidence types satisfy more
than one obligation at once (the core USP, spec §16).
"""
from __future__ import annotations
from typing import Dict, List, Optional
from .predicates import evaluate, true_leaves
from .rules_obligations import ALL_OBLIGATIONS
from .rules_overlaps import OVERLAP_GROUPS
from .rules_regulations import FIELD_LABELS
from .rules_types import ObligationRule
from .schemas import (
ApplicableObligation,
ObligationOverlap,
ObligationsResponse,
ProductProfile,
RegulatoryScope,
)
from .scope_engine import discover_scope
def _applicable_regulation_ids(profile: ProductProfile, scope: Optional[RegulatoryScope]) -> List[str]:
if scope is None:
scope = discover_scope(profile)
return [r.regulation_id for r in scope.applicable_regulations]
def _applies_because(rule: ObligationRule, profile: ProductProfile) -> List[str]:
labels: List[str] = []
for leaf in true_leaves(rule.applies_if, profile):
label = FIELD_LABELS.get(leaf[0])
if label and label not in labels:
labels.append(label)
if not labels:
labels.append("%s ist für dieses Produkt anwendbar." % rule.source_regulation)
return labels
def _role_ok(rule: ObligationRule, profile: ProductProfile) -> bool:
role = profile.manufacturer_role
if role is None:
return True # unknown role -> do not exclude
return role.value in rule.applies_to_role
def derive_obligations(
profile: ProductProfile, scope: Optional[RegulatoryScope] = None
) -> ObligationsResponse:
active_regs = set(_applicable_regulation_ids(profile, scope))
response = ObligationsResponse()
applied_ids: List[str] = []
for rule in ALL_OBLIGATIONS:
if rule.source_regulation not in active_regs:
continue
if rule.applies_unless is not None and evaluate(rule.applies_unless, profile) is True:
continue
verdict = evaluate(rule.applies_if, profile)
if verdict is not True or not _role_ok(rule, profile):
if verdict is False:
response.excluded_obligations.append(rule.obligation_id)
continue
applied_ids.append(rule.obligation_id)
response.applicable_obligations.append(
ApplicableObligation(
obligation_id=rule.obligation_id,
title=rule.title,
source_regulation=rule.source_regulation,
legal_basis_refs=rule.legal_basis_refs,
obligation_text=rule.obligation_text,
authority_level=rule.authority_level,
applies_because=_applies_because(rule, profile),
applies_to_role=rule.applies_to_role,
lifecycle_phase=rule.lifecycle_phase,
overlap_group_id=rule.overlap_group_id,
required_evidence=rule.required_evidence,
confidence=rule.base_confidence,
registry_anchor=rule.registry_anchor,
proposed=rule.proposed,
)
)
response.overlaps = _overlaps(applied_ids)
response.evidence_for_multiple = _evidence_for_multiple(response.applicable_obligations)
return response
def _overlaps(applied_ids: List[str]) -> List[ObligationOverlap]:
applied = set(applied_ids)
out: List[ObligationOverlap] = []
for group in OVERLAP_GROUPS:
present = [m for m in group.members if m in applied]
if len(present) >= 2:
out.append(
ObligationOverlap(
overlap_group_id=group.overlap_group_id,
obligations=present,
overlap_type=group.overlap_type,
canonical_obligation_id=group.canonical_obligation_id,
explanation=group.explanation,
)
)
return out
def _evidence_for_multiple(obligations: List[ApplicableObligation]) -> Dict[str, List[str]]:
by_evidence: Dict[str, List[str]] = {}
for ob in obligations:
for ev in ob.required_evidence:
by_evidence.setdefault(ev, []).append(ob.obligation_id)
return {ev: ids for ev, ids in by_evidence.items() if len(ids) > 1}
@@ -0,0 +1,100 @@
"""Safe, tri-state condition evaluator for applicability rules.
Conditions are plain data (no `eval`): a *leaf* is a 3-tuple
``(field, op, value)``; a *composite* is ``{"all": [...]}`` or
``{"any": [...]}``. Evaluation is tri-state ``True`` / ``False`` /
``None`` (unknown) so a missing product fact yields *uncertain*, never a
false negative.
"""
from __future__ import annotations
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Union
Leaf = Tuple[str, str, Any]
Condition = Union[Leaf, Dict[str, Any]]
def _attr(profile: Any, field: str) -> Any:
value = getattr(profile, field, None)
if isinstance(value, Enum):
return value.value
return value
def _eval_leaf(leaf: Leaf, profile: Any) -> Optional[bool]:
field, op, expected = leaf
actual = _attr(profile, field)
if op == "not_none":
return actual is not None
if op == "is_none":
return actual is None
if op == "contains_any":
# list-valued field (e.g. product_type); empty list = known-empty.
items = actual or []
hay = " ".join(str(x).lower() for x in items)
return any(str(k).lower() in hay for k in expected)
if actual is None:
return None # unknown fact -> unknown result
if op == "eq":
return bool(actual == expected)
if op == "ne":
return bool(actual != expected)
if op == "truthy":
return bool(actual)
if op == "falsy":
return not bool(actual)
if op == "in":
return bool(actual in expected)
if op == "not_in":
return bool(actual not in expected)
if op == "date_after":
return bool(actual > expected)
raise ValueError("unknown predicate op: %r" % (op,))
def evaluate(condition: Optional[Condition], profile: Any) -> Optional[bool]:
"""Return True/False/None(unknown) for a condition tree."""
if condition is None:
return True
if isinstance(condition, tuple):
return _eval_leaf(condition, profile)
if "all" in condition:
results = [evaluate(c, profile) for c in condition["all"]]
if any(r is False for r in results):
return False
if any(r is None for r in results):
return None
return True
if "any" in condition:
results = [evaluate(c, profile) for c in condition["any"]]
if any(r is True for r in results):
return True
if any(r is None for r in results):
return None
return False
raise ValueError("malformed condition: %r" % (condition,))
def true_leaves(condition: Optional[Condition], profile: Any) -> List[Leaf]:
"""Collect the leaf conditions that evaluated True (for trigger_facts)."""
if condition is None:
return []
if isinstance(condition, tuple):
return [condition] if _eval_leaf(condition, profile) is True else []
members = condition.get("all") or condition.get("any") or []
out: List[Leaf] = []
for c in members:
out.extend(true_leaves(c, profile))
return out
def unknown_fields(fields: List[str], profile: Any) -> List[str]:
"""Subset of `fields` whose value on the profile is None (unknown)."""
return [f for f in fields if _attr(profile, f) is None]
@@ -0,0 +1,23 @@
"""Aggregated obligation scope rules + lookup helpers."""
from __future__ import annotations
from typing import Dict, List, Optional
from .rules_obligations_cra import CRA_OBLIGATIONS
from .rules_obligations_machine_data import DATA_ACT_OBLIGATIONS, MACHINE_OBLIGATIONS
from .rules_types import ObligationRule
ALL_OBLIGATIONS: List[ObligationRule] = (
CRA_OBLIGATIONS + MACHINE_OBLIGATIONS + DATA_ACT_OBLIGATIONS
)
_BY_ID: Dict[str, ObligationRule] = {o.obligation_id: o for o in ALL_OBLIGATIONS}
def obligation_rule(obligation_id: str) -> Optional[ObligationRule]:
return _BY_ID.get(obligation_id)
def obligations_for_regulation(regulation_id: str) -> List[ObligationRule]:
return [o for o in ALL_OBLIGATIONS if o.source_regulation == regulation_id]
@@ -0,0 +1,271 @@
"""CRA obligation scope rules.
`obligation_id`s in the six CRA-P1 families (sbom/vuln/authentication/logging/
remote_access/updates) are RE-USED verbatim from the Legal-KG registry
(`obligations/obligation_join_keys.json`) never re-minted (control_uuid trap,
memory `project_compliance_graph.md`). Cross-cutting CRA *process* obligations
(risk assessment, technical documentation, CE, instructions, secure-by-design
umbrella) are not yet in the registry and are flagged `proposed=True`.
"""
from __future__ import annotations
from typing import List
from .enums import AuthorityLevel, Confidence
from .rules_types import ObligationRule
_HAS_SW = ("has_software", "eq", True)
_EU = ("eu_market", "eq", True)
_REMOTE_OR_CLOUD = {"any": [("has_remote_access", "eq", True), ("has_cloud_connection", "eq", True)]}
_LM = AuthorityLevel.LEGAL_TEXT
CRA_OBLIGATIONS: List[ObligationRule] = [
ObligationRule(
obligation_id="sbom_creation",
title="Software Bill of Materials erstellen",
source_regulation="CRA",
obligation_text="Eine SBOM erstellen, die mindestens die obersten Abhängigkeiten des Produkts dokumentiert.",
legal_basis_refs=["CRA Annex I Part II (1)"],
authority_level=_LM,
family="sbom",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["software_bill_of_materials"],
required_evidence=["sbom", "repo_scan"],
lifecycle_phase=["development", "placing_on_market", "maintenance"],
registry_anchor=True,
),
ObligationRule(
obligation_id="provide_security_updates",
title="Sicherheitsupdates bereitstellen",
source_regulation="CRA",
obligation_text="Sicherheitsrelevante Updates zeitnah und über den Supportzeitraum bereitstellen.",
legal_basis_refs=["CRA Annex I (2)(c)", "CRA Art. 13"],
authority_level=_LM,
family="updates",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["secure_updates"],
required_evidence=["policy", "ticket", "test_report"],
lifecycle_phase=["maintenance", "update"],
overlap_group_id="SECURITY_UPDATES",
registry_anchor=True,
),
ObligationRule(
obligation_id="support_period_maintenance",
title="Supportzeitraum definieren und einhalten",
source_regulation="CRA",
obligation_text="Einen angemessenen Supportzeitraum festlegen, in dem Schwachstellen behandelt werden.",
legal_basis_refs=["CRA Art. 13(8)"],
authority_level=_LM,
family="updates",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["secure_updates"],
required_evidence=["policy"],
lifecycle_phase=["placing_on_market", "maintenance", "update"],
registry_anchor=True,
),
ObligationRule(
obligation_id="signed_update_integrity",
title="Integrität von Updates sicherstellen",
source_regulation="CRA",
obligation_text="Updates signieren und ihre Integrität bei der Verteilung verifizieren.",
legal_basis_refs=["CRA Annex I (1)(3)(f)"],
authority_level=_LM,
family="updates",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["software_integrity"],
required_evidence=["config_export", "test_report"],
lifecycle_phase=["development", "maintenance", "update"],
overlap_group_id="SECURITY_UPDATES",
registry_anchor=True,
),
ObligationRule(
obligation_id="vuln_handling_process",
title="Schwachstellenbehandlungs-Prozess",
source_regulation="CRA",
obligation_text="Einen dokumentierten Prozess zur Identifikation, Bewertung und Behebung von Schwachstellen betreiben.",
legal_basis_refs=["CRA Art. 13(8)", "CRA Annex VII"],
authority_level=_LM,
family="vuln",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["vulnerability_management"],
required_evidence=["policy", "ticket"],
lifecycle_phase=["development", "operation", "maintenance"],
overlap_group_id="VULNERABILITY_HANDLING",
registry_anchor=True,
),
ObligationRule(
obligation_id="coordinated_vulnerability_disclosure",
title="Coordinated Vulnerability Disclosure",
source_regulation="CRA",
obligation_text="Eine Richtlinie zur koordinierten Offenlegung von Schwachstellen bereitstellen.",
legal_basis_refs=["CRA Annex I Part II (5)"],
authority_level=_LM,
family="vuln",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["coordinated_disclosure"],
required_evidence=["policy"],
lifecycle_phase=["operation", "maintenance"],
overlap_group_id="VULNERABILITY_HANDLING",
registry_anchor=True,
),
ObligationRule(
obligation_id="exploited_vuln_reporting_authorities",
title="Meldung aktiv ausgenutzter Schwachstellen / Vorfälle",
source_regulation="CRA",
obligation_text="Aktiv ausgenutzte Schwachstellen und schwerwiegende Vorfälle an die zuständigen Behörden melden.",
legal_basis_refs=["CRA Art. 14", "CRA Art. 16"],
authority_level=_LM,
family="vuln",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["incident_reporting"],
required_evidence=["policy", "ticket"],
lifecycle_phase=["operation", "maintenance"],
registry_anchor=True,
),
ObligationRule(
obligation_id="user_authentication_required",
title="Authentifizierung vorsehen",
source_regulation="CRA",
obligation_text="Den Zugang über einen geeigneten Authentifizierungsmechanismus schützen.",
legal_basis_refs=["CRA Annex I (2)(d)"],
authority_level=_LM,
family="authentication",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["authentication"],
required_evidence=["config_export", "pentest"],
lifecycle_phase=["development", "operation"],
registry_anchor=True,
),
ObligationRule(
obligation_id="no_default_credentials",
title="Keine unveränderlichen Standard-Zugangsdaten",
source_regulation="CRA",
obligation_text="Sichere Standardkonfiguration; keine fest hinterlegten oder unveränderlichen Standard-Passwörter.",
legal_basis_refs=["CRA Annex I (2)(a)", "CRA Annex I (2)(b)"],
authority_level=_LM,
family="authentication",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["secure_by_default"],
required_evidence=["config_export", "test_report"],
lifecycle_phase=["development", "placing_on_market"],
registry_anchor=True,
),
ObligationRule(
obligation_id="event_logging_security_events",
title="Sicherheitsrelevante Ereignisse protokollieren",
source_regulation="CRA",
obligation_text="Sicherheitsrelevante Ereignisse und Zugriffe aufzeichnen, um Vorfälle nachvollziehen zu können.",
legal_basis_refs=["CRA Annex I Part I (2)(k)"],
authority_level=_LM,
family="logging",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["security_logging"],
required_evidence=["config_export", "audit_log"],
lifecycle_phase=["operation", "maintenance"],
registry_anchor=True,
),
ObligationRule(
obligation_id="remote_access_attack_surface_min",
title="Angriffsfläche minimieren",
source_regulation="CRA",
obligation_text="Die Angriffsfläche begrenzen, insbesondere exponierte Remote-/Cloud-Schnittstellen.",
legal_basis_refs=["CRA Annex I (1)(2)(a)"],
authority_level=_LM,
family="remote_access",
applies_if={"all": [_REMOTE_OR_CLOUD, _EU]},
required_capabilities=["secure_by_default"],
required_evidence=["config_export", "repo_scan", "pentest"],
lifecycle_phase=["development", "operation"],
registry_anchor=True,
),
ObligationRule(
obligation_id="remote_access_confidentiality_integrity",
title="Vertraulichkeit/Integrität der Fernverbindung",
source_regulation="CRA",
obligation_text="Daten bei Fernzugriff/Cloud-Anbindung verschlüsselt und integritätsgeschützt übertragen.",
legal_basis_refs=["CRA Annex I (1)(2)(b)", "CRA Annex I (1)(2)(c)"],
authority_level=_LM,
family="remote_access",
applies_if={"all": [_REMOTE_OR_CLOUD, _EU]},
required_capabilities=["secure_communication"],
required_evidence=["config_export", "pentest"],
lifecycle_phase=["operation"],
registry_anchor=True,
),
# --- Cross-cutting CRA process obligations (not yet in registry) ---------
ObligationRule(
obligation_id="cra_secure_by_design",
title="Security by Design",
source_regulation="CRA",
obligation_text="Das Produkt so entwerfen, entwickeln und herstellen, dass ein angemessenes Cybersicherheitsniveau gewährleistet ist.",
legal_basis_refs=["CRA Annex I Part I (1)"],
authority_level=_LM,
family="cra_process",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["secure_by_default", "risk_assessment"],
required_evidence=["policy", "test_report"],
lifecycle_phase=["development", "placing_on_market"],
proposed=True,
),
ObligationRule(
obligation_id="cra_risk_assessment",
title="Cybersicherheits-Risikobewertung",
source_regulation="CRA",
obligation_text="Eine Cybersicherheits-Risikobewertung durchführen und dokumentieren; in die technische Dokumentation aufnehmen.",
legal_basis_refs=["CRA Art. 13(2)", "CRA Annex I Part I (1)"],
authority_level=_LM,
family="cra_process",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["risk_assessment"],
required_evidence=["policy"],
lifecycle_phase=["development", "placing_on_market"],
overlap_group_id="RISK_ASSESSMENT",
proposed=True,
),
ObligationRule(
obligation_id="cra_technical_documentation",
title="Technische Dokumentation",
source_regulation="CRA",
obligation_text="Technische Dokumentation erstellen und aktuell halten, die Konformität mit den Anforderungen belegt.",
legal_basis_refs=["CRA Art. 31", "CRA Annex VII"],
authority_level=_LM,
family="cra_process",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["technical_documentation"],
required_evidence=["policy"],
lifecycle_phase=["placing_on_market", "maintenance"],
overlap_group_id="TECHNICAL_DOCUMENTATION",
proposed=True,
),
ObligationRule(
obligation_id="cra_ce_conformity_assessment",
title="Konformitätsbewertung / CE-Kennzeichnung",
source_regulation="CRA",
obligation_text="Vor dem Inverkehrbringen das passende Konformitätsbewertungsverfahren durchlaufen und CE kennzeichnen.",
legal_basis_refs=["CRA Art. 32", "CRA Art. 28"],
authority_level=_LM,
family="cra_process",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["conformity_assessment"],
required_evidence=["test_report", "policy"],
lifecycle_phase=["placing_on_market"],
overlap_group_id="CE_CONFORMITY",
proposed=True,
),
ObligationRule(
obligation_id="cra_instructions_for_use",
title="Informationen und Anweisungen für Nutzer",
source_regulation="CRA",
obligation_text="Nutzern verständliche Sicherheitsinformationen und -anweisungen bereitstellen (z. B. zu Updates und Support-Ende).",
legal_basis_refs=["CRA Annex II"],
authority_level=_LM,
family="cra_process",
applies_if={"all": [_HAS_SW, _EU]},
required_capabilities=["technical_documentation"],
required_evidence=["policy"],
lifecycle_phase=["placing_on_market"],
overlap_group_id="INSTRUCTIONS_FOR_USE",
proposed=True,
),
]
@@ -0,0 +1,139 @@
"""MaschinenVO and Data Act obligation scope rules.
These regulations are NOT yet in the Legal-KG registry (which currently covers
the six CRA-P1 families). Every obligation here is therefore `proposed=True`:
the reasoning layer proposes the snake_case id, the Obligation Registry session
remains the only authority that may canonicalise it (re-link, never re-mint).
"""
from __future__ import annotations
from typing import List
from .enums import AuthorityLevel, Confidence
from .rules_types import ObligationRule
_EU = ("eu_market", "eq", True)
_IS_MACHINE = ("is_machine", "eq", True)
_LM = AuthorityLevel.LEGAL_TEXT
MACHINE_OBLIGATIONS: List[ObligationRule] = [
ObligationRule(
obligation_id="machine_risk_assessment",
title="Maschinen-Risikobeurteilung",
source_regulation="MaschinenVO",
obligation_text="Eine Risikobeurteilung der Maschine durchführen, um Gefährdungen zu ermitteln und zu mindern.",
legal_basis_refs=["MaschinenVO (EU) 2023/1230 Anhang III (1.1.1)", "EN ISO 12100"],
authority_level=_LM,
family="machine_safety",
applies_if={"all": [_IS_MACHINE, _EU]},
required_capabilities=["risk_assessment"],
required_evidence=["policy"],
lifecycle_phase=["development", "placing_on_market"],
overlap_group_id="RISK_ASSESSMENT",
proposed=True,
),
ObligationRule(
obligation_id="machine_safety_control_systems",
title="Sichere Steuerungssysteme",
source_regulation="MaschinenVO",
obligation_text="Sicherheitsbezogene Teile der Steuerung so auslegen, dass Ausfälle nicht zu gefährlichen Zuständen führen.",
legal_basis_refs=["MaschinenVO (EU) 2023/1230 Anhang III (1.2.1)", "EN ISO 13849-1"],
authority_level=_LM,
family="machine_safety",
applies_if={"all": [_IS_MACHINE, ("has_safety_function", "eq", True), _EU]},
required_capabilities=["functional_safety"],
required_evidence=["test_report", "policy"],
lifecycle_phase=["development", "placing_on_market"],
proposed=True,
),
ObligationRule(
obligation_id="machine_protection_against_corruption",
title="Schutz gegen Korrumpierung sicherheitsrelevanter Funktionen",
source_regulation="MaschinenVO",
obligation_text="Sicherstellen, dass eine (auch beabsichtigte) Korrumpierung der Software/Verbindung keine gefährliche Situation auslöst.",
legal_basis_refs=["MaschinenVO (EU) 2023/1230 Anhang III (1.1.9)"],
authority_level=_LM,
family="machine_safety",
applies_if={
"all": [
_IS_MACHINE,
("has_safety_function", "eq", True),
{"any": [("has_remote_access", "eq", True), ("has_software", "eq", True)]},
_EU,
]
},
required_capabilities=["software_integrity", "secure_by_default"],
required_evidence=["test_report", "config_export"],
lifecycle_phase=["development", "operation", "maintenance"],
overlap_group_id="VULNERABILITY_HANDLING",
proposed=True,
),
ObligationRule(
obligation_id="machine_instructions_for_use",
title="Betriebsanleitung",
source_regulation="MaschinenVO",
obligation_text="Eine vollständige Betriebsanleitung mit Sicherheitshinweisen bereitstellen.",
legal_basis_refs=["MaschinenVO (EU) 2023/1230 Anhang III (1.7.4)"],
authority_level=_LM,
family="machine_safety",
applies_if={"all": [_IS_MACHINE, _EU]},
required_capabilities=["technical_documentation"],
required_evidence=["policy"],
lifecycle_phase=["placing_on_market"],
overlap_group_id="INSTRUCTIONS_FOR_USE",
proposed=True,
),
ObligationRule(
obligation_id="machine_ce_conformity",
title="Konformitätsbewertung / CE (Maschine)",
source_regulation="MaschinenVO",
obligation_text="Das passende Konformitätsbewertungsverfahren der MaschinenVO durchlaufen und CE kennzeichnen.",
legal_basis_refs=["MaschinenVO (EU) 2023/1230 Art. 25", "Anhang IV"],
authority_level=_LM,
family="machine_safety",
applies_if={"all": [_IS_MACHINE, _EU]},
required_capabilities=["conformity_assessment"],
required_evidence=["test_report", "policy"],
lifecycle_phase=["placing_on_market"],
overlap_group_id="CE_CONFORMITY",
proposed=True,
),
]
DATA_ACT_OBLIGATIONS: List[ObligationRule] = [
ObligationRule(
obligation_id="data_act_data_access_by_design",
title="Datenzugang by design",
source_regulation="DataAct",
obligation_text="Vernetzte Produkte so gestalten, dass die erzeugten Produktdaten standardmäßig zugänglich sind.",
legal_basis_refs=["Data Act (EU) 2023/2854 Art. 3"],
authority_level=_LM,
family="data_act",
applies_if={
"all": [
("generates_usage_data", "eq", True),
{"any": [("has_cloud_connection", "eq", True), ("has_remote_access", "eq", True)]},
_EU,
]
},
required_capabilities=["data_access_provision"],
required_evidence=["config_export", "policy"],
lifecycle_phase=["development", "placing_on_market"],
proposed=True,
),
ObligationRule(
obligation_id="data_act_user_data_access",
title="Datenzugang für Nutzer",
source_regulation="DataAct",
obligation_text="Nutzern Zugang zu den von ihnen erzeugten Daten gewähren und Weitergabe an Dritte ermöglichen.",
legal_basis_refs=["Data Act (EU) 2023/2854 Art. 4", "Art. 5"],
authority_level=_LM,
family="data_act",
applies_if={"all": [("generates_usage_data", "eq", True), _EU]},
required_capabilities=["data_access_provision"],
required_evidence=["policy"],
lifecycle_phase=["operation"],
proposed=True,
),
]
@@ -0,0 +1,91 @@
"""Obligation overlap groups (spec §4.5 / Modus 2).
Overlaps are emitted only for the members that are actually applicable to the
product. `canonical_obligation_id` points at the strongest / most specific
obligation in the group (preferring a registry-anchored CRA id).
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import List
from .enums import OverlapType
@dataclass(frozen=True)
class OverlapGroup:
overlap_group_id: str
members: List[str]
overlap_type: OverlapType
canonical_obligation_id: str
explanation: str
OVERLAP_GROUPS: List[OverlapGroup] = [
OverlapGroup(
overlap_group_id="VULNERABILITY_HANDLING",
members=[
"vuln_handling_process",
"coordinated_vulnerability_disclosure",
"machine_protection_against_corruption",
],
overlap_type=OverlapType.COMPLEMENTARY,
canonical_obligation_id="vuln_handling_process",
explanation=(
"CRA adressiert die Schwachstellenbehandlung des Produkts. Die MaschinenVO wird "
"komplementär relevant, sobald eine Cyber-Schwachstelle eine Sicherheitsfunktion "
"beeinflussen kann (Anhang III 1.1.9). Nicht identisch, aber gemeinsam zu erfüllen."
),
),
OverlapGroup(
overlap_group_id="SECURITY_UPDATES",
members=["provide_security_updates", "signed_update_integrity"],
overlap_type=OverlapType.COMPLEMENTARY,
canonical_obligation_id="provide_security_updates",
explanation=(
"Updates bereitstellen und ihre Integrität sichern sind zwei Seiten desselben "
"Update-Prozesses; ein Nachweis (Update-Policy, Release Notes) deckt teils beide ab."
),
),
OverlapGroup(
overlap_group_id="RISK_ASSESSMENT",
members=["cra_risk_assessment", "machine_risk_assessment"],
overlap_type=OverlapType.DIFFERENT_SCOPE,
canonical_obligation_id="cra_risk_assessment",
explanation=(
"Zwei getrennte Risikobetrachtungen: CRA = Cybersicherheits-Risiko, MaschinenVO = "
"Sicherheits-/Gefährdungsbeurteilung. Methodisch verwandt, inhaltlich unterschiedlich."
),
),
OverlapGroup(
overlap_group_id="TECHNICAL_DOCUMENTATION",
members=["cra_technical_documentation", "machine_risk_assessment"],
overlap_type=OverlapType.SIMILAR,
canonical_obligation_id="cra_technical_documentation",
explanation=(
"Beide Regime verlangen eine technische Dokumentation; Teile (Risikobetrachtung, "
"Konstruktionsunterlagen) lassen sich in einem konsolidierten technischen Dossier führen."
),
),
OverlapGroup(
overlap_group_id="CE_CONFORMITY",
members=["cra_ce_conformity_assessment", "machine_ce_conformity"],
overlap_type=OverlapType.COMPLEMENTARY,
canonical_obligation_id="machine_ce_conformity",
explanation=(
"Ein Produkt kann zwei CE-Regime gleichzeitig erfüllen müssen (MaschinenVO + CRA). "
"Eine gemeinsame CE-Kennzeichnung, aber getrennte Konformitätsbewertungen."
),
),
OverlapGroup(
overlap_group_id="INSTRUCTIONS_FOR_USE",
members=["cra_instructions_for_use", "machine_instructions_for_use"],
overlap_type=OverlapType.SIMILAR,
canonical_obligation_id="machine_instructions_for_use",
explanation=(
"Betriebsanleitung (MaschinenVO) und Sicherheitsinformationen (CRA) überschneiden sich; "
"ein integriertes Anleitungsdokument kann beide Pflichten bedienen."
),
),
]
@@ -0,0 +1,160 @@
"""Regulation-level applicability trigger rules (scope discovery, spec Modus 1).
Each rule is pure data consumed by `scope_engine`. Triggers reference
`ProductProfile` fields through the safe predicate evaluator. `required_facts`
that are unknown turn the verdict *uncertain* and surface `fact_prompts`.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from .enums import Confidence
from .predicates import Condition
# Positive, human-readable label per profile fact (for trigger_facts output).
FIELD_LABELS: Dict[str, str] = {
"has_software": "Produkt enthält Software / digitale Elemente",
"has_embedded_software": "Produkt enthält eingebettete Software",
"has_remote_access": "Produkt besitzt Fernzugriff / Fernwartung",
"has_cloud_connection": "Produkt ist mit einer Cloud verbunden",
"has_radio_module": "Produkt enthält ein Funkmodul",
"has_safety_function": "Produkt erfüllt eine Sicherheitsfunktion",
"generates_usage_data": "Vernetztes Produkt erzeugt nutzbare Produktdaten",
"is_machine": "Produkt ist eine Maschine",
"is_component": "Produkt ist ein (Sicherheits-)Bauteil",
"eu_market": "Produkt wird auf dem EU-Markt bereitgestellt",
"is_essential_or_important_entity": "Unternehmen ist wesentliche/wichtige Einrichtung",
"manufacturer_role": "Wirtschaftsakteur-Rolle (Hersteller/Importeur/Händler)",
}
@dataclass(frozen=True)
class RegulationRule:
regulation_id: str
name: str
trigger: Condition
required_facts: List[str]
fact_prompts: Dict[str, str]
legal_basis_refs: List[str]
summary: str
confidence_when_applicable: Confidence = Confidence.HIGH
exclusion: Optional[Condition] = None
# Status is downgraded to PARTIALLY_APPLICABLE / MEDIUM when the trigger
# fires only via inference rather than a directly stated fact.
inferred: bool = False
excludable_roles: List[str] = field(default_factory=list)
_ECONOMIC_ROLES = ["manufacturer", "importer", "distributor"]
REGULATION_RULES: List[RegulationRule] = [
RegulationRule(
regulation_id="CRA",
name="Cyber Resilience Act (EU) 2024/2847",
trigger={
"all": [
{"any": [("has_software", "eq", True), ("has_embedded_software", "eq", True)]},
("eu_market", "eq", True),
]
},
required_facts=["has_software", "eu_market", "manufacturer_role"],
fact_prompts={
"has_software": "Enthält das Produkt Software / digitale Elemente?",
"eu_market": "Wird das Produkt auf dem EU-Markt bereitgestellt oder in Verkehr gebracht?",
"manufacturer_role": "Welche Rolle nehmen Sie ein (Hersteller / Importeur / Händler)?",
},
legal_basis_refs=["CRA Art. 2(1)", "CRA Art. 3(1)"],
summary="Produkte mit digitalen Elementen, die auf dem EU-Markt bereitgestellt werden.",
confidence_when_applicable=Confidence.HIGH,
excludable_roles=["operator"],
),
RegulationRule(
regulation_id="MaschinenVO",
name="Maschinenverordnung (EU) 2023/1230",
trigger={
"any": [
("is_machine", "eq", True),
{"all": [("is_component", "eq", True), ("has_safety_function", "eq", True)]},
]
},
required_facts=["is_machine", "eu_market"],
fact_prompts={
"is_machine": "Ist das Produkt eine Maschine oder ein Sicherheitsbauteil?",
"has_safety_function": "Erfüllt das Bauteil eine Sicherheitsfunktion?",
},
legal_basis_refs=["MaschinenVO (EU) 2023/1230 Art. 2", "Anhang III"],
summary="Maschinen oder Sicherheitsbauteile, ggf. mit sicherheitsrelevanter Steuerung.",
confidence_when_applicable=Confidence.MEDIUM,
),
RegulationRule(
regulation_id="RED",
name="Radio Equipment Directive 2014/53/EU",
trigger=("has_radio_module", "eq", True),
required_facts=["has_radio_module"],
fact_prompts={
"has_radio_module": "Besitzt das Produkt ein Funkmodul (WLAN, Bluetooth, Mobilfunk)?",
},
legal_basis_refs=["RED 2014/53/EU Art. 1", "Art. 3(3)(d-f)"],
summary="Funkanlagen; Art. 3(3) deckt zusätzlich Cybersecurity-Anforderungen ab.",
confidence_when_applicable=Confidence.HIGH,
),
RegulationRule(
regulation_id="EMV",
name="EMV-Richtlinie 2014/30/EU",
trigger={
"any": [
("has_software", "eq", True),
("has_embedded_software", "eq", True),
("has_radio_module", "eq", True),
]
},
required_facts=[],
fact_prompts={
"is_electrical": "Ist das Produkt ein elektrisches / elektronisches Betriebsmittel?",
},
legal_basis_refs=["EMV-RL 2014/30/EU Art. 2"],
summary="Elektrische/elektronische Betriebsmittel (hier aus den digitalen Elementen abgeleitet).",
confidence_when_applicable=Confidence.MEDIUM,
inferred=True,
),
RegulationRule(
regulation_id="DataAct",
name="Data Act (EU) 2023/2854",
trigger={
"all": [
{"any": [("has_cloud_connection", "eq", True), ("has_remote_access", "eq", True)]},
("generates_usage_data", "eq", True),
]
},
required_facts=["generates_usage_data"],
fact_prompts={
"generates_usage_data": "Erzeugt das vernetzte Produkt nutzbare Produkt-/Nutzungsdaten?",
},
legal_basis_refs=["Data Act (EU) 2023/2854 Art. 2(5)", "Art. 3-5"],
summary="Vernetzte Produkte, die Nutzungsdaten erzeugen und zugänglich machen.",
confidence_when_applicable=Confidence.HIGH,
),
RegulationRule(
regulation_id="NIS2",
name="NIS2-Richtlinie (EU) 2022/2555",
trigger=("is_essential_or_important_entity", "eq", True),
required_facts=["company_size", "sector", "is_essential_or_important_entity"],
fact_prompts={
"company_size": "Unternehmensgröße (Mitarbeiterzahl / Umsatz)?",
"sector": "In welchem Sektor ist das Unternehmen tätig (Anhang I/II)?",
"is_essential_or_important_entity": "Fällt das Unternehmen als wesentliche/wichtige Einrichtung unter NIS2?",
},
legal_basis_refs=["NIS2-RL (EU) 2022/2555 Art. 2", "Art. 3"],
summary="Adressiert die ORGANISATION (Größe/Sektor/Rolle), nicht das Produkt.",
confidence_when_applicable=Confidence.MEDIUM,
),
]
def regulation_rule(regulation_id: str) -> Optional[RegulationRule]:
for rule in REGULATION_RULES:
if rule.regulation_id == regulation_id:
return rule
return None
@@ -0,0 +1,58 @@
"""Shared types for obligation scope rules.
`required_evidence` MUST draw from the framework-AGNOSTIC evidence catalog
owned by the Compliance Execution Graph (memory `project_compliance_graph.md`,
User-Direktive 2026-06-25). Do not invent framework-specific evidence types.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import List, Optional
from .enums import AuthorityLevel, Confidence
from .predicates import Condition
# Framework-agnostic shared evidence catalog (the only allowed tokens).
EVIDENCE_CATALOG = frozenset(
{
"config_export",
"test_report",
"repo_scan",
"sbom",
"policy",
"audit_log",
"pentest",
"ticket",
}
)
@dataclass(frozen=True)
class ObligationRule:
obligation_id: str
title: str
source_regulation: str
obligation_text: str
legal_basis_refs: List[str]
authority_level: AuthorityLevel
family: str
applies_if: Condition
required_capabilities: List[str]
required_evidence: List[str]
base_confidence: Confidence = Confidence.HIGH
applies_unless: Optional[Condition] = None
lifecycle_phase: List[str] = field(default_factory=list)
applies_to_role: List[str] = field(default_factory=lambda: ["manufacturer", "importer"])
overlap_group_id: Optional[str] = None
# True => obligation_id is owned by the Legal-KG registry (re-link, never re-mint).
registry_anchor: bool = False
# True => Machine/Data-Act obligation the registry has not canonicalised yet.
proposed: bool = False
def __post_init__(self) -> None:
bad = [e for e in self.required_evidence if e not in EVIDENCE_CATALOG]
if bad:
raise ValueError(
"obligation %s uses non-catalog evidence %r" % (self.obligation_id, bad)
)
@@ -0,0 +1,226 @@
"""Pydantic domain objects for the Regulatory Reasoning Engine.
Trigger facts that drive scope are tri-state (`Optional[bool] = None`): `None`
means "fact unknown" and produces an *uncertain* verdict plus a concrete
missing-fact prompt never silent false security (spec §6.3).
"""
from __future__ import annotations
from datetime import date
from typing import Dict, List, Optional
from pydantic import BaseModel, Field
from .enums import (
ApplicabilityStatus,
AuthorityLevel,
ClaimCoverage,
Confidence,
InterpretationVerdict,
ManufacturerRole,
MarketModel,
OverlapType,
ProductLifecyclePhase,
)
# ---------------------------------------------------------------------------
# Input
# ---------------------------------------------------------------------------
class ProductProfile(BaseModel):
"""The customer's product / system. Tri-state booleans => unknown facts."""
product_name: str
product_profile_id: Optional[str] = None
manufacturer_role: Optional[ManufacturerRole] = None
product_type: List[str] = Field(default_factory=list)
has_software: Optional[bool] = None
has_embedded_software: Optional[bool] = None
has_remote_access: Optional[bool] = None
has_cloud_connection: Optional[bool] = None
has_ai_functionality: Optional[bool] = None
has_radio_module: Optional[bool] = None
has_safety_function: Optional[bool] = None
generates_usage_data: Optional[bool] = None
is_machine: Optional[bool] = None
is_component: Optional[bool] = None
is_spare_part: Optional[bool] = None
placed_on_market_after: Optional[date] = None
intended_use: Optional[str] = None
eu_market: Optional[bool] = None
b2b_or_b2c: Optional[MarketModel] = None
lifecycle_phase: Optional[ProductLifecyclePhase] = None
# Organisation context — only needed for NIS2 (not a product fact).
company_size: Optional[str] = None
sector: Optional[str] = None
is_essential_or_important_entity: Optional[bool] = None
# ---------------------------------------------------------------------------
# Scope
# ---------------------------------------------------------------------------
class ApplicableRegulation(BaseModel):
regulation_id: str
name: str
applicability_status: ApplicabilityStatus
trigger_facts: List[str] = Field(default_factory=list)
legal_basis_refs: List[str] = Field(default_factory=list)
confidence: Confidence
explanation: str
class ExcludedRegulation(BaseModel):
regulation_id: str
name: str
reason: str
class UncertainRegulation(BaseModel):
regulation_id: str
name: str
missing_facts: List[str] = Field(default_factory=list)
explanation: str
class RegulatoryScope(BaseModel):
product_profile_id: Optional[str] = None
applicable_regulations: List[ApplicableRegulation] = Field(default_factory=list)
excluded_regulations: List[ExcludedRegulation] = Field(default_factory=list)
uncertain_regulations: List[UncertainRegulation] = Field(default_factory=list)
missing_facts: List[str] = Field(default_factory=list)
confidence: Confidence = Confidence.MEDIUM
reasoning_summary: str = ""
# ---------------------------------------------------------------------------
# Obligations
# ---------------------------------------------------------------------------
class ApplicableObligation(BaseModel):
obligation_id: str
title: str
source_regulation: str
legal_basis_refs: List[str] = Field(default_factory=list)
obligation_text: str
authority_level: AuthorityLevel
applies_because: List[str] = Field(default_factory=list)
applies_to_role: List[str] = Field(default_factory=list)
lifecycle_phase: List[str] = Field(default_factory=list)
overlap_group_id: Optional[str] = None
required_evidence: List[str] = Field(default_factory=list)
confidence: Confidence
# True only when obligation_id is owned by the Legal-KG registry (CRA P1).
registry_anchor: bool = False
# Machine/Data-Act obligations the registry has not canonicalised yet.
proposed: bool = False
class ObligationOverlap(BaseModel):
overlap_group_id: str
obligations: List[str] = Field(default_factory=list)
overlap_type: OverlapType
canonical_obligation_id: str
explanation: str
# ---------------------------------------------------------------------------
# Customer claims & assessments
# ---------------------------------------------------------------------------
class CustomerImplementationClaim(BaseModel):
claim_id: str
raw_statement: str
normalized_claim: str = ""
claimed_capability: List[str] = Field(default_factory=list)
related_topics: List[str] = Field(default_factory=list)
qualifiers: List[str] = Field(default_factory=list)
evidence_refs: List[str] = Field(default_factory=list)
class ClaimObligationMapping(BaseModel):
"""One row of Welt-1 reasoning: how a customer claim relates to an obligation.
Layers (spec / architect): claim -> interpretation (on the claim object) ->
*potential* obligation coverage (`claim_coverage`) -> evidence required.
Carries NO compliance verdict.
"""
claim_id: str
obligation_id: str
claim_coverage: ClaimCoverage
missing_elements: List[str] = Field(default_factory=list)
required_evidence: List[str] = Field(default_factory=list)
explanation: str
confidence: Confidence
class InterpretationAssessment(BaseModel):
interpretation_id: str
raw_interpretation: str
affected_regulations: List[str] = Field(default_factory=list)
affected_obligations: List[str] = Field(default_factory=list)
assessment: InterpretationVerdict
risks: List[str] = Field(default_factory=list)
corrected_interpretation: str = ""
legal_basis_refs: List[str] = Field(default_factory=list)
explanation: str
confidence: Confidence
# ---------------------------------------------------------------------------
# API request / response envelopes
# ---------------------------------------------------------------------------
class ScopeRequest(BaseModel):
product_profile: ProductProfile
class ScopeResponse(BaseModel):
regulatory_scope: RegulatoryScope
missing_facts: List[str] = Field(default_factory=list)
confidence: Confidence
class ObligationsRequest(BaseModel):
product_profile: ProductProfile
regulatory_scope: Optional[RegulatoryScope] = None
class ObligationsResponse(BaseModel):
applicable_obligations: List[ApplicableObligation] = Field(default_factory=list)
overlaps: List[ObligationOverlap] = Field(default_factory=list)
excluded_obligations: List[str] = Field(default_factory=list)
evidence_for_multiple: Dict[str, List[str]] = Field(default_factory=dict)
class ImplementationReasoningRequest(BaseModel):
product_profile: ProductProfile
customer_claim: str
class ImplementationReasoningResponse(BaseModel):
claim: CustomerImplementationClaim
mappings: List[ClaimObligationMapping] = Field(default_factory=list)
missing_evidence: List[str] = Field(default_factory=list)
summary: str = ""
# Makes the Welt-1 boundary explicit: this is advisory claim-mapping, not a
# conformity verdict (that is ComplianceStatus in the Execution Graph).
disclaimer: str = ""
class InterpretationRequest(BaseModel):
product_profile: Optional[ProductProfile] = None
customer_interpretation: str
class InterpretationResponse(BaseModel):
assessment: InterpretationVerdict
affected_regulations: List[str] = Field(default_factory=list)
affected_obligations: List[str] = Field(default_factory=list)
corrected_interpretation: str = ""
risks: List[str] = Field(default_factory=list)
legal_basis_refs: List[str] = Field(default_factory=list)
explanation: str = ""
confidence: Confidence = Confidence.MEDIUM
@@ -0,0 +1,136 @@
"""Scope discovery engine (spec Modus 1).
Answers "which regulations apply to my product?" and, crucially, never says
"X applies" without the triggers, and never hides a missing fact behind a false
verdict. Pure rule evaluation, deterministic.
"""
from __future__ import annotations
from typing import List, Optional
from .enums import ApplicabilityStatus, Confidence
from .predicates import Condition, evaluate, true_leaves, unknown_fields
from .rules_regulations import REGULATION_RULES, FIELD_LABELS, RegulationRule
from .schemas import (
ApplicableRegulation,
ExcludedRegulation,
ProductProfile,
RegulatoryScope,
UncertainRegulation,
)
_DOWNGRADE = {Confidence.HIGH: Confidence.MEDIUM, Confidence.MEDIUM: Confidence.LOW, Confidence.LOW: Confidence.LOW}
def _fields_in(condition: Optional[Condition]) -> List[str]:
if condition is None:
return []
if isinstance(condition, tuple):
return [condition[0]]
out: List[str] = []
for c in condition.get("all") or condition.get("any") or []:
out.extend(_fields_in(c))
return out
def _trigger_facts(rule: RegulationRule, profile: ProductProfile) -> List[str]:
labels: List[str] = []
for leaf in true_leaves(rule.trigger, profile):
label = FIELD_LABELS.get(leaf[0])
if label and label not in labels:
labels.append(label)
return labels
def _missing_prompts(rule: RegulationRule, profile: ProductProfile) -> List[str]:
fields = list(dict.fromkeys(rule.required_facts + _fields_in(rule.trigger)))
unknown = unknown_fields(fields, profile)
prompts: List[str] = []
for f in unknown:
prompt = rule.fact_prompts.get(f)
if prompt and prompt not in prompts:
prompts.append(prompt)
return prompts
def discover_scope(profile: ProductProfile) -> RegulatoryScope:
scope = RegulatoryScope(product_profile_id=profile.product_profile_id)
for rule in REGULATION_RULES:
role_value = profile.manufacturer_role.value if profile.manufacturer_role is not None else None
role_excluded = role_value is not None and role_value in rule.excludable_roles
trig = evaluate(rule.trigger, profile)
missing = _missing_prompts(rule, profile)
if role_excluded:
scope.excluded_regulations.append(
ExcludedRegulation(
regulation_id=rule.regulation_id,
name=rule.name,
reason="Rolle '%s' ist von dieser Regulierung nicht unmittelbar adressiert." % role_value,
)
)
continue
if trig is True:
conf = Confidence.MEDIUM if rule.inferred else rule.confidence_when_applicable
status = (
ApplicabilityStatus.PARTIALLY_APPLICABLE if rule.inferred else ApplicabilityStatus.APPLICABLE
)
unresolved = unknown_fields(rule.required_facts, profile)
if unresolved:
conf = _DOWNGRADE[conf]
for f in unresolved:
prompt = rule.fact_prompts.get(f)
if prompt and prompt not in scope.missing_facts:
scope.missing_facts.append(prompt)
scope.applicable_regulations.append(
ApplicableRegulation(
regulation_id=rule.regulation_id,
name=rule.name,
applicability_status=status,
trigger_facts=_trigger_facts(rule, profile),
legal_basis_refs=rule.legal_basis_refs,
confidence=conf,
explanation=rule.summary,
)
)
elif trig is None:
scope.uncertain_regulations.append(
UncertainRegulation(
regulation_id=rule.regulation_id,
name=rule.name,
missing_facts=missing,
explanation=rule.summary,
)
)
for m in missing:
if m not in scope.missing_facts:
scope.missing_facts.append(m)
else: # trig is False -> definitively excluded by a known fact
scope.excluded_regulations.append(
ExcludedRegulation(
regulation_id=rule.regulation_id,
name=rule.name,
reason="Auslösende Voraussetzungen sind anhand der bekannten Fakten nicht erfüllt.",
)
)
scope.confidence = _overall_confidence(scope)
scope.reasoning_summary = _summary(scope)
return scope
def _overall_confidence(scope: RegulatoryScope) -> Confidence:
if scope.applicable_regulations and not scope.uncertain_regulations and not scope.missing_facts:
return Confidence.HIGH
if scope.applicable_regulations:
return Confidence.MEDIUM
return Confidence.LOW
def _summary(scope: RegulatoryScope) -> str:
applicable = ", ".join(r.regulation_id for r in scope.applicable_regulations) or ""
uncertain = ", ".join(r.regulation_id for r in scope.uncertain_regulations) or ""
return "Wahrscheinlich anwendbar: %s. Unsicher (fehlende Fakten): %s." % (applicable, uncertain)
@@ -0,0 +1,104 @@
"""Deterministic taxonomy for normalising free-text customer claims.
Capability names echo the planned Obligation -> Capability layer of the
Compliance Execution Graph (memory `project_compliance_graph.md`), so the
reasoning layer's claim capabilities line up with the registry's capabilities.
Matching is lowercase substring matching deterministic, no LLM, no RAG.
"""
from __future__ import annotations
from typing import Dict, List
# capability -> trigger substrings (German + English), matched lowercase.
CAPABILITY_KEYWORDS: Dict[str, List[str]] = {
"software_bill_of_materials": [
"sbom", "stückliste", "stueckliste", "bill of materials", "komponentenliste",
],
"secure_updates": ["update", "patch", "aktualisier", "release", "rollout"],
"software_integrity": ["signier", "signatur", "signed", "integrität", "integritaet", "hash"],
"vulnerability_management": [
"schwachstelle", "vulnerab", "cve", "schwachstellenmanagement", "vuln",
],
"coordinated_disclosure": [
"disclosure", "offenlegung", "security.txt", "responsible disclosure",
],
"incident_reporting": [
"incident", "vorfall", "behörde", "behoerde", "csirt", "meldepflicht", "an die behörde",
],
"authentication": [
"authentifizier", "login", "passwort", "password", "mfa", "2fa", "anmeldung",
],
"secure_by_default": [
"härtung", "haertung", "hardening", "default", "standardkonfig",
"sichere konfiguration", "angriffsfläche", "angriffsflaeche",
],
"security_logging": ["logging", "log ", "logs", "protokoll", "audit-trail", "ereignisprotokoll"],
"secure_communication": ["verschlüssel", "verschluessel", "encryption", "tls", "vpn", "ssl"],
"risk_assessment": [
"risikoanalyse", "risikobeurteil", "risk assessment", "gefährdungsbeurteil",
"gefaehrdungsbeurteil", "bedrohungsanalyse", "threat model",
],
"technical_documentation": [
"dokumentation", "technische unterlagen", "betriebsanleitung", "handbuch", "documentation",
],
"conformity_assessment": ["konformität", "konformitaet", "conformity", "baumuster", "ce-kenn"],
"functional_safety": [
"performance level", "sil ", "iso 13849", "funktionale sicherheit", "safety control",
],
"data_access_provision": [
"datenzugang", "data access", "datenportabilität", "datenexport", "data export",
],
}
# capability -> broader compliance topics it touches (spec related_topics).
CAPABILITY_TOPICS: Dict[str, List[str]] = {
"software_bill_of_materials": ["component_transparency", "supply_chain", "vulnerability_management"],
"secure_updates": ["secure_updates", "vulnerability_remediation", "release_management"],
"software_integrity": ["secure_updates", "supply_chain", "tamper_protection"],
"vulnerability_management": ["vulnerability_handling", "monitoring", "patch_management"],
"coordinated_disclosure": ["vulnerability_handling", "transparency"],
"incident_reporting": ["incident_handling", "authority_notification"],
"authentication": ["access_control", "identity"],
"secure_by_default": ["hardening", "attack_surface", "configuration"],
"security_logging": ["monitoring", "forensics", "incident_handling"],
"secure_communication": ["confidentiality", "integrity", "remote_access"],
"risk_assessment": ["risk_management", "secure_by_design"],
"technical_documentation": ["documentation", "conformity"],
"conformity_assessment": ["conformity", "ce_marking"],
"functional_safety": ["machine_safety", "control_systems"],
"data_access_provision": ["data_sharing", "portability"],
}
# qualifier -> substrings that signal a weak/incomplete implementation.
QUALIFIER_KEYWORDS: Dict[str, List[str]] = {
"reactive": [
"wenn kunden", "wenn ein kunde", "nach meldung", "auf anfrage", "auf nachfrage",
"nur wenn", "reaktiv", "wenn fehler", "when customers", "on request", "when reported",
"ad hoc", "ad-hoc", "bei bedarf",
],
"manual": ["manuell", "von hand", "manual", "händisch", "haendisch"],
"planned": [
"geplant", "in planung", "wollen wir", "planen wir", "noch nicht", "zukünftig", "künftig",
],
"absent": ["haben wir nicht", "gibt es nicht", "nicht vorhanden", "keinen prozess", "keine"],
}
def match_capabilities(text: str) -> List[str]:
low = text.lower()
return [cap for cap, kws in CAPABILITY_KEYWORDS.items() if any(k in low for k in kws)]
def match_qualifiers(text: str) -> List[str]:
low = text.lower()
return [q for q, kws in QUALIFIER_KEYWORDS.items() if any(k in low for k in kws)]
def topics_for(capabilities: List[str]) -> List[str]:
out: List[str] = []
for cap in capabilities:
for t in CAPABILITY_TOPICS.get(cap, []):
if t not in out:
out.append(t)
return out
@@ -0,0 +1,159 @@
"""Known customer interpretation patterns (spec Modus 4).
Deterministic: a customer interpretation is matched by lowercase substring
triggers against a curated library of common misconceptions. No match ->
the engine returns `uncertain` and asks for the missing context (no false
security, spec §6.3).
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import List
from .enums import Confidence, InterpretationVerdict
@dataclass(frozen=True)
class InterpretationPattern:
pattern_id: str
triggers: List[str]
verdict: InterpretationVerdict
corrected_interpretation: str
explanation: str
affected_regulations: List[str] = field(default_factory=list)
affected_obligations: List[str] = field(default_factory=list)
risks: List[str] = field(default_factory=list)
legal_basis_refs: List[str] = field(default_factory=list)
confidence: Confidence = Confidence.MEDIUM
INTERPRETATION_PATTERNS: List[InterpretationPattern] = [
InterpretationPattern(
pattern_id="cra_only_new_products",
triggers=[
"nur für neue", "nur fuer neue", "nur neu entwickelt", "nur neuentwicklung",
"nur bei neuentwicklung", "only new product", "gilt nur für neue produkte",
],
verdict=InterpretationVerdict.TOO_NARROW,
corrected_interpretation=(
"CRA-Pflichten knüpfen primär an Produkt, Rolle, Marktzugang, Bereitstellung und "
"Übergangsfristen an, nicht nur an Neuentwicklung. Ein fertig entwickeltes "
"Katalogprodukt kann betroffen sein, wenn es nach dem maßgeblichen Zeitpunkt weiter "
"auf dem EU-Markt bereitgestellt wird."
),
explanation=(
"Die relevante Frage ist nicht nur, ob das Produkt neu entwickelt wurde, sondern ob es "
"nach dem Anwendungszeitpunkt weiterhin bereitgestellt oder in Verkehr gebracht wird."
),
affected_regulations=["CRA"],
risks=["Katalog-/Bestandsprodukt fällt trotz abgeschlossener Entwicklung unter den CRA."],
legal_basis_refs=["CRA Art. 2", "CRA Art. 69 (Übergangsbestimmungen)"],
confidence=Confidence.HIGH,
),
InterpretationPattern(
pattern_id="cra_b2b_exempt",
triggers=[
"gilt nicht für b2b", "nur für verbraucher", "nur b2c", "nicht im b2b",
"only consumer", "b2b ist ausgenommen",
],
verdict=InterpretationVerdict.TOO_NARROW,
corrected_interpretation=(
"Der CRA gilt produkt- und marktbezogen, unabhängig von B2B oder B2C. Eine generelle "
"B2B-Ausnahme existiert nicht; Industrieprodukte mit digitalen Elementen sind erfasst."
),
explanation="Der Anwendungsbereich knüpft an 'Produkte mit digitalen Elementen' an, nicht an die Kundengruppe.",
affected_regulations=["CRA"],
risks=["Industrielle B2B-Steuerungen werden fälschlich als ausgenommen behandelt."],
legal_basis_refs=["CRA Art. 2", "CRA Art. 3(1)"],
confidence=Confidence.HIGH,
),
InterpretationPattern(
pattern_id="sbom_is_enough",
triggers=[
"sbom reicht", "mit sbom sind wir", "sbom genügt", "sbom genuegt", "nur eine sbom",
"sbom allein",
],
verdict=InterpretationVerdict.TOO_NARROW,
corrected_interpretation=(
"Eine SBOM erfüllt nur einen Teil der Komponenten-Transparenz. Schwachstellen-"
"überwachung, Update-/Patch-Prozess und technische Dokumentation bleiben eigenständige Pflichten."
),
explanation="SBOM ist Voraussetzung, ersetzt aber nicht Vulnerability-Handling und Updates.",
affected_regulations=["CRA"],
affected_obligations=["sbom_creation", "vuln_handling_process", "provide_security_updates"],
risks=["Falsche Annahme vollständiger Erfüllung trotz fehlendem Vulnerability-Prozess."],
legal_basis_refs=["CRA Annex I Part II (1)", "CRA Annex I Part II (2)"],
confidence=Confidence.HIGH,
),
InterpretationPattern(
pattern_id="open_source_exempt",
triggers=[
"open source ist ausgenommen", "open-source ist ausgenommen", "oss ist ausgenommen",
"freie software ist ausgenommen", "open source fällt nicht",
],
verdict=InterpretationVerdict.PARTIALLY_CORRECT,
corrected_interpretation=(
"Nur nicht-kommerziell bereitgestellte Open-Source-Software ist ausgenommen. Sobald OSS "
"kommerziell in ein Produkt integriert und auf dem Markt bereitgestellt wird, greift der CRA."
),
explanation="Die Ausnahme zielt auf nicht-kommerzielle OSS-Bereitstellung, nicht auf kommerzielle Produktintegration.",
affected_regulations=["CRA"],
risks=["Kommerziell integrierte OSS-Komponenten werden fälschlich als ausgenommen behandelt."],
legal_basis_refs=["CRA Art. 2", "CRA Erwägungsgründe (Open-Source-Stewards)"],
confidence=Confidence.MEDIUM,
),
InterpretationPattern(
pattern_id="reactive_updates_ok",
triggers=[
"updates nur wenn", "reaktive updates reichen", "wenn kunden melden reicht",
"updates wenn fehler gemeldet",
],
verdict=InterpretationVerdict.TOO_NARROW,
corrected_interpretation=(
"Der CRA verlangt aktive Schwachstellenüberwachung und zeitnahe Sicherheitsupdates über "
"den Supportzeitraum, nicht nur reaktive Updates nach Kundenmeldung."
),
explanation="Ein rein reaktiver Updateprozess erfüllt die Pflicht zur aktiven Schwachstellenbehandlung nicht.",
affected_regulations=["CRA"],
affected_obligations=["provide_security_updates", "vuln_handling_process"],
risks=["Verzögerte Reaktion auf öffentlich bekannte Schwachstellen; Pflichtverletzung."],
legal_basis_refs=["CRA Annex I Part II (1)", "CRA Annex I (2)(c)"],
confidence=Confidence.HIGH,
),
InterpretationPattern(
pattern_id="machinery_covers_cyber",
triggers=[
"maschinenrichtlinie deckt cyber", "maschinenvo deckt alles", "ce der maschine reicht",
"ce maschine reicht für cyber", "maschinen-ce reicht",
],
verdict=InterpretationVerdict.PARTIALLY_CORRECT,
corrected_interpretation=(
"Die MaschinenVO deckt die sicherheitsrelevante Korrumpierung ab (Anhang III 1.1.9), "
"ersetzt aber nicht die produktbezogenen CRA-Security-Pflichten. Beide Regime gelten parallel."
),
explanation="Maschinen-CE und CRA überschneiden sich nur dort, wo Cyber eine Sicherheitsfunktion betrifft.",
affected_regulations=["CRA", "MaschinenVO"],
affected_obligations=["machine_protection_against_corruption", "vuln_handling_process"],
risks=["CRA-Pflichten werden übersehen, weil die Maschine bereits CE-gekennzeichnet ist."],
legal_basis_refs=["MaschinenVO Anhang III (1.1.9)", "CRA Art. 13"],
confidence=Confidence.MEDIUM,
),
InterpretationPattern(
pattern_id="no_radio_no_cyber",
triggers=[
"ohne funkmodul kein cyber", "kein funk also kein cra", "ohne funk keine security",
"ohne funkmodul keine cyber",
],
verdict=InterpretationVerdict.TOO_NARROW,
corrected_interpretation=(
"Der CRA knüpft an digitale Elemente an, nicht an ein Funkmodul. Ohne Funk entfällt die "
"RED, der CRA bleibt jedoch anwendbar, sobald Software vorhanden ist."
),
explanation="Funkmodul ist nur für die RED relevant; die CRA-Anwendbarkeit folgt aus der Software.",
affected_regulations=["CRA", "RED"],
risks=["CRA wird fälschlich verneint, weil kein Funkmodul vorhanden ist."],
legal_basis_refs=["CRA Art. 3(1)", "RED 2014/53/EU Art. 1"],
confidence=Confidence.HIGH,
),
]
@@ -0,0 +1,31 @@
"""Regulatory Map — customer-readable read-model over the engine's scope output.
Composes scope + registry-linked obligations + overlaps into one map:
product -> trigger facts -> applicable / uncertain / excluded regulations ->
obligations -> overlaps -> unsupported domains -> executive summary. Explains the
engine's state, never extends it. No new logic, no UI, no RAG, no percentage.
"""
from __future__ import annotations
from .renderer import render_regulatory_map
from .schemas import (
ApplicableRegulationView,
ExcludedRegulationView,
ObligationRef,
OverlapView,
RegulatoryMap,
RegulatoryMapRequest,
UncertainRegulationView,
)
__all__ = [
"render_regulatory_map",
"RegulatoryMap",
"RegulatoryMapRequest",
"ApplicableRegulationView",
"UncertainRegulationView",
"ExcludedRegulationView",
"OverlapView",
"ObligationRef",
]
@@ -0,0 +1,169 @@
"""Regulatory Map renderer (step 4) — pure composition, no new logic.
It explains the engine's state, it does not extend it: every statement comes
from `resolve_product_scope` (scope verdict) or `derive_obligations` (registry-
linked obligations + overlaps). No legal decisions here; obligations are shown
ONLY where a registry id is linkable (registry_anchor); the executive summary
carries counts but NO percentage.
"""
from __future__ import annotations
from typing import Dict, List
from compliance.navigator.engine import navigate
from compliance.product_scope.orchestrator import resolve_product_scope
from compliance.product_scope.schemas import RegulatoryScopeResult, ScopeStatus
from compliance.profile.canonical import CanonicalProductRegulatoryProfile
from compliance.profile.to_reasoning import to_reasoning_profile
from compliance.reasoning.obligation_engine import derive_obligations
from .schemas import (
ApplicableRegulationView,
ExcludedRegulationView,
ObligationRef,
OverlapView,
RegulatoryMap,
UncertainRegulationView,
)
_DOMAIN_BY_REG = {
"CRA": "cyber",
"MaschinenVO": "machine_safety",
"RED": "radio",
"DataAct": "data",
"EMV": "emv",
"NIS2": None,
}
def _product_summary(c: CanonicalProductRegulatoryProfile) -> str:
bits: List[str] = [c.name or "Produkt"]
if c.product_type:
bits.append("(%s)" % c.product_type.value)
sig: List[str] = []
if c.is_machine:
sig.append("Maschine")
if c.has_remote_access or c.connected_to_internet or "cloud" in c.technologies:
sig.append("vernetzt")
if c.has_embedded_software:
sig.append("Firmware")
if c.economic_operator_role:
sig.append("Rolle: %s" % c.economic_operator_role.value)
if c.markets:
sig.append("Märkte: %s" % ", ".join(c.markets))
if sig:
bits.append("" + "; ".join(sig))
return " ".join(bits)
def render_regulatory_map(profile: CanonicalProductRegulatoryProfile) -> RegulatoryMap:
scope_resp = resolve_product_scope(profile)
summary = _product_summary(profile)
if scope_resp.status == ScopeStatus.NEEDS_FACTS:
return RegulatoryMap(
scope_resolved=False,
product_summary=summary,
executive_summary=(
"Regulatorischer Scope noch nicht bestimmbar — zuerst Mindestfakten klären: "
+ "; ".join(scope_resp.missing_facts[:6])
+ "."
),
)
scope = scope_resp.regulatory_scope
assert scope is not None
obligations = derive_obligations(to_reasoning_profile(profile))
nav_questions = navigate(profile).suggested_questions
linked_ids = {o.obligation_id for o in obligations.applicable_obligations if o.registry_anchor}
by_reg: Dict[str, List[ObligationRef]] = {}
shared_ev: Dict[str, List[str]] = {}
for o in obligations.applicable_obligations:
if not o.registry_anchor:
continue
by_reg.setdefault(o.source_regulation, []).append(
ObligationRef(
obligation_id=o.obligation_id,
title=o.title,
legal_basis_refs=o.legal_basis_refs,
authority_level=o.authority_level,
)
)
for ev in o.required_evidence:
shared_ev.setdefault(ev, []).append(o.obligation_id)
applicable_views = []
for r in scope.applicable_regulations:
obs = by_reg.get(r.regulation_id, [])
applicable_views.append(
ApplicableRegulationView(
regulation_id=r.regulation_id,
name=r.name,
why_applicable=r.explanation,
triggered_by=r.trigger_facts,
obligations=obs,
obligations_note="" if obs else "Pflichten für dieses Regelwerk sind noch nicht registry-verlinkt.",
confidence=r.confidence,
)
)
uncertain_views = []
for u in scope.uncertain_regulations:
domain = _DOMAIN_BY_REG.get(u.regulation_id)
qrefs = [q.question_id for q in nav_questions if domain and domain in q.regulatory_domains_unblocked]
uncertain_views.append(
UncertainRegulationView(
regulation_id=u.regulation_id, name=u.name, missing_facts=u.missing_facts, question_refs=qrefs
)
)
overlap_views = []
for ov in obligations.overlaps:
members = [m for m in ov.obligations if m in linked_ids]
if len(members) >= 2:
overlap_views.append(
OverlapView(overlap_group_id=ov.overlap_group_id, shared_obligations=members, explanation=ov.explanation)
)
trigger_facts: List[str] = []
for v in applicable_views:
for t in v.triggered_by:
if t not in trigger_facts:
trigger_facts.append(t)
return RegulatoryMap(
scope_resolved=True,
product_summary=summary,
trigger_facts=trigger_facts,
applicable_regulations=applicable_views,
uncertain_regulations=uncertain_views,
excluded_regulations=[
ExcludedRegulationView(regulation_id=e.regulation_id, name=e.name, exclusion_reason=e.reason)
for e in scope.excluded_regulations
],
unsupported_domains=scope.unsupported_domains,
overlaps=overlap_views,
shared_evidence={ev: ids for ev, ids in shared_ev.items() if len(ids) > 1},
executive_summary=_executive_summary(summary, applicable_views, uncertain_views, scope, len(linked_ids)),
)
def _executive_summary(
summary: str,
applicable: List[ApplicableRegulationView],
uncertain: List[UncertainRegulationView],
scope: RegulatoryScopeResult,
n_obligations: int,
) -> str:
appl = ", ".join(v.regulation_id for v in applicable) or ""
unc = ", ".join(v.regulation_id for v in uncertain) or "keine"
exc = ", ".join(e.regulation_id for e in scope.excluded_regulations) or "keine"
uns = ", ".join(d.domain for d in scope.unsupported_domains) or "keine"
return (
"Für %s gelten nach derzeitigem Stand wahrscheinlich: %s. Unsicher (fehlende Fakten): %s. "
"Ausgeschlossen: %s. Nicht abgedeckt (Regelkorpus fehlt): %s. Ermittelt: %d registry-verlinkte "
"Pflichten. Es wurden keine weiteren Regelwerke im aktuellen Korpus identifiziert."
% (summary, appl, unc, exc, uns, n_obligations)
)
@@ -0,0 +1,70 @@
"""Read-model for the Regulatory Map (step 4).
A customer-readable view that COMPOSES what the engine already computed (scope +
obligations + overlaps). It adds no scope/obligation logic. All fields are
application-level presentation types NOT compliance-meta-model classes
(architecture freeze v1.0 untouched).
"""
from __future__ import annotations
from typing import Dict, List
from pydantic import BaseModel, Field
from compliance.product_scope.schemas import UnsupportedDomain
from compliance.profile.canonical import CanonicalProductRegulatoryProfile
from compliance.reasoning.enums import AuthorityLevel, Confidence
class RegulatoryMapRequest(BaseModel):
product_profile: CanonicalProductRegulatoryProfile
class ObligationRef(BaseModel):
obligation_id: str
title: str
legal_basis_refs: List[str] = Field(default_factory=list)
authority_level: AuthorityLevel
class ApplicableRegulationView(BaseModel):
regulation_id: str
name: str
why_applicable: str
triggered_by: List[str] = Field(default_factory=list)
obligations: List[ObligationRef] = Field(default_factory=list)
obligations_note: str = "" # set when obligations are not yet registry-linkable
confidence: Confidence
class UncertainRegulationView(BaseModel):
regulation_id: str
name: str
missing_facts: List[str] = Field(default_factory=list)
question_refs: List[str] = Field(default_factory=list)
class ExcludedRegulationView(BaseModel):
regulation_id: str
name: str
exclusion_reason: str
class OverlapView(BaseModel):
overlap_group_id: str
shared_obligations: List[str] = Field(default_factory=list)
explanation: str = ""
class RegulatoryMap(BaseModel):
scope_resolved: bool
product_summary: str
trigger_facts: List[str] = Field(default_factory=list)
applicable_regulations: List[ApplicableRegulationView] = Field(default_factory=list)
uncertain_regulations: List[UncertainRegulationView] = Field(default_factory=list)
excluded_regulations: List[ExcludedRegulationView] = Field(default_factory=list)
unsupported_domains: List[UnsupportedDomain] = Field(default_factory=list)
overlaps: List[OverlapView] = Field(default_factory=list)
shared_evidence: Dict[str, List[str]] = Field(default_factory=dict)
executive_summary: str = ""
+127
View File
@@ -0,0 +1,127 @@
"""Tests for Company Intelligence (Phase 2A) — Company Capability Profile.
Acceptance: from a CompanyContext (certifications, declarations, evidence) the
engine derives operational capabilities with a four-state trust model and a HARD
RULE: a certification is NEVER auto-treated as "erfuellt" at most INFERRED.
The Certification->Capability mapping is Execution's domain. It is injected here as
a MOCK (the yaml-like dict below lives ONLY in tests); product code ships no table.
"""
from __future__ import annotations
from compliance.company import (
CapabilityMappingEntry,
Certification,
CompanyContext,
Declaration,
ExistingEvidence,
VerificationStatus,
build_company_profile,
)
from compliance.reasoning.enums import Confidence
# --- MOCK mapping (Execution-owned in reality; here only for the tests) -------
# mapping:
# ISO27001 -> [cap_patch_management, cap_supplier_management]
MOCK_MAPPING = {
"ISO27001": CapabilityMappingEntry(
capability_ids=["cap_patch_management", "cap_supplier_management"],
confidence=Confidence.MEDIUM,
)
}
def _candidate(profile, capability_id):
return [c for c in profile.candidate_capabilities if c.capability_id == capability_id]
def _confirmed_ids(profile):
return {c.capability_id for c in profile.confirmed_capabilities}
# A certification yields INFERRED candidates via the injected mapping.
def test_certification_infers_candidates_via_injected_mapping():
ctx = CompanyContext(company_id="acme", certifications=[Certification(certification_id="ISO27001")])
profile = build_company_profile(ctx, MOCK_MAPPING)
ids = {c.capability_id for c in profile.candidate_capabilities}
assert ids == {"cap_patch_management", "cap_supplier_management"}
for c in profile.candidate_capabilities:
assert c.verification_status == VerificationStatus.INFERRED
assert c.source == "certification:ISO27001"
# Without an injected mapping there are NO inferred capabilities — only the claim.
# This is the architectural guarantee that the table lives only in Execution.
def test_no_mapping_no_inferred_capabilities():
ctx = CompanyContext(company_id="acme", certifications=[Certification(certification_id="ISO27001")])
profile = build_company_profile(ctx) # default EMPTY mapping
assert profile.candidate_capabilities == []
# the certification still produced evidence-of-claim (refinement 1)
assert len(profile.capability_evidence) == 1
assert profile.capability_evidence[0].source == "certification:ISO27001"
assert profile.capability_evidence[0].certification_id == "ISO27001"
# A customer declaration yields a DECLARED candidate.
def test_declaration_yields_declared_candidate():
ctx = CompanyContext(company_id="acme", declarations=[Declaration(capability_id="cap_patch_management")])
profile = build_company_profile(ctx, MOCK_MAPPING)
cands = _candidate(profile, "cap_patch_management")
assert len(cands) == 1
assert cands[0].verification_status == VerificationStatus.DECLARED
# declared + inferred coexist as distinct signals for the same capability.
def test_declared_and_inferred_coexist():
ctx = CompanyContext(
company_id="acme",
certifications=[Certification(certification_id="ISO27001")],
declarations=[Declaration(capability_id="cap_patch_management")],
)
profile = build_company_profile(ctx, MOCK_MAPPING)
statuses = {c.verification_status for c in _candidate(profile, "cap_patch_management")}
assert statuses == {VerificationStatus.DECLARED, VerificationStatus.INFERRED}
# HARD RULE: a certification alone NEVER yields a confirmed capability.
def test_hard_rule_certification_never_confirmed():
ctx = CompanyContext(company_id="acme", certifications=[Certification(certification_id="ISO27001")])
profile = build_company_profile(ctx, MOCK_MAPPING)
assert _confirmed_ids(profile) == set()
for c in profile.candidate_capabilities:
assert c.verification_status != VerificationStatus.CONFIRMED
# Only real evidence confirms a capability — and it leaves the candidate list.
def test_evidence_confirms_capability():
ctx = CompanyContext(
company_id="acme",
certifications=[Certification(certification_id="ISO27001")],
evidence=[ExistingEvidence(evidence_id="pol-1", evidence_type="policy", proves_capability_id="cap_patch_management")],
)
profile = build_company_profile(ctx, MOCK_MAPPING)
assert "cap_patch_management" in _confirmed_ids(profile)
confirmed = [c for c in profile.confirmed_capabilities if c.capability_id == "cap_patch_management"][0]
assert confirmed.verification_status == VerificationStatus.CONFIRMED
assert confirmed.confidence == Confidence.HIGH
assert confirmed.sources == ["pol-1"]
# a confirmed capability is no longer a mere candidate
assert _candidate(profile, "cap_patch_management") == []
# the un-proven capability stays an inferred candidate
assert _candidate(profile, "cap_supplier_management")[0].verification_status == VerificationStatus.INFERRED
# The four-state vocabulary exists and is ordered declared->inferred->confirmed (+unknown).
def test_four_states_present():
assert {s.value for s in VerificationStatus} == {"declared", "inferred", "confirmed", "unknown"}
# verification_status is a FOURTH vocabulary, disjoint from ClaimCoverage and DeltaType.
def test_verification_status_distinct_vocabulary():
from compliance.rci.schemas import DeltaType
from compliance.reasoning.enums import ClaimCoverage
verif = {s.value for s in VerificationStatus}
assert verif.isdisjoint({c.value for c in ClaimCoverage})
assert verif.isdisjoint({d.value for d in DeltaType})
@@ -0,0 +1,141 @@
"""Tests for Interpretation-in-Map (step 5).
Acceptance: a customer interpretation is judged against the existing map, using
only assess_interpretation; affected regulations/obligations are referenced from
the map; unsupported domains (wastewater/chemicals) are flagged
future_corpus_needed, not pseudo-evaluated; output is customer-readable.
"""
from __future__ import annotations
import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient
from compliance.interpretation_map import interpret_in_map
from compliance.profile.canonical import (
CanonicalLifecyclePhase,
CanonicalProductRegulatoryProfile,
CanonicalProductType,
EconomicOperatorRole,
EnvironmentalImpact,
)
from compliance.reasoning.enums import InterpretationVerdict
from compliance.reasoning.interpretation_engine import assess_interpretation
from compliance.regulatory_map import render_regulatory_map
def ready_profile(**ov) -> CanonicalProductRegulatoryProfile:
base = dict(
name="Industriespülmaschine",
product_type=CanonicalProductType.MACHINERY,
markets=["EU", "DE"],
economic_operator_role=EconomicOperatorRole.MANUFACTURER,
lifecycle_phase=CanonicalLifecyclePhase.PLACING_ON_MARKET,
is_machine=True,
is_component=False,
has_software_updates=True,
has_embedded_software=True,
has_remote_access=True,
technologies=["cloud", "ota_updates"],
)
base.update(ov)
return CanonicalProductRegulatoryProfile(**base)
def _map(**ov):
return render_regulatory_map(ready_profile(**ov))
# 1 + 2. evaluated against the map, using ONLY assess_interpretation.
def test_uses_assess_interpretation_verdict():
text = "Wir glauben, der CRA gilt nur für neue Produkte."
result = interpret_in_map(_map(), text)
assert result.assessment == assess_interpretation(text).assessment == InterpretationVerdict.TOO_NARROW
assert "CRA" in result.affected_regulations # CRA is in the map
assert result.in_scope_of_map is True
# 3. the six verdict values pass through unchanged.
def test_verdict_values():
m = _map()
assert interpret_in_map(m, "CRA gilt nur für neue Produkte.").assessment == InterpretationVerdict.TOO_NARROW
assert interpret_in_map(m, "Open Source ist ausgenommen, also betrifft uns der CRA nicht.").assessment == InterpretationVerdict.PARTIALLY_CORRECT
assert interpret_in_map(m, "Der Mond beeinflusst unsere Updatezyklen.").assessment == InterpretationVerdict.UNCERTAIN
# 4. affected regulations/obligations are referenced FROM the map.
def test_affected_refs_from_map():
m = _map()
result = interpret_in_map(m, "Eine SBOM reicht, dann sind wir fertig.")
map_ob_ids = {o.obligation_id for v in m.applicable_regulations for o in v.obligations}
map_reg_ids = {v.regulation_id for v in m.applicable_regulations} | {v.regulation_id for v in m.uncertain_regulations}
assert "sbom_creation" in result.affected_obligations
assert set(result.affected_obligations) <= map_ob_ids
assert set(result.affected_regulations) <= map_reg_ids
# 5. environmental aspects are NOT pseudo-evaluated.
def test_environmental_not_pseudo_evaluated():
m = _map(environmental=EnvironmentalImpact(discharges_to_wastewater=True))
result = interpret_in_map(m, "Beim Abwasser sind wir nicht betroffen, das spielt für uns keine Rolle.")
domains = {d.domain for d in result.future_corpus_domains}
assert "environment_water" in domains
assert "future_corpus_needed" in result.explanation
# 6. output is customer-readable.
def test_customer_readable():
result = interpret_in_map(_map(), "Der CRA gilt nur für neue Produkte.")
assert "zu eng" in result.explanation
assert result.explanation.startswith("Ihre Interpretation ist wahrscheinlich")
# affected refs never leave the map (no abstract legal questions).
def test_affected_regs_never_outside_map():
m = _map()
map_reg_ids = (
{v.regulation_id for v in m.applicable_regulations}
| {v.regulation_id for v in m.uncertain_regulations}
| {v.regulation_id for v in m.excluded_regulations}
)
for text in ["CRA gilt nur für neue Produkte.", "Ohne Funkmodul keine Cyber-Pflichten.", "SBOM reicht."]:
result = interpret_in_map(m, text)
assert set(result.affected_regulations) <= map_reg_ids
# endpoint smoke.
@pytest.fixture(scope="module")
def client():
from compliance.api.reasoning_routes import router
app = FastAPI()
app.include_router(router)
return TestClient(app)
def test_endpoint_interpretation_in_map(client):
r = client.post(
"/reasoning/interpretation-in-map",
json={
"product_profile": {
"name": "M",
"product_type": "machinery",
"markets": ["EU"],
"economic_operator_role": "manufacturer",
"lifecycle_phase": "placing_on_market",
"is_machine": True,
"is_component": False,
"has_software_updates": True,
"has_embedded_software": True,
"has_remote_access": True,
"technologies": ["cloud"],
},
"customer_interpretation": "Der CRA gilt nur für neue Produkte.",
},
)
assert r.status_code == 200
body = r.json()
assert body["assessment"] == "too_narrow"
assert "CRA" in body["affected_regulations"]
assert "zu eng" in body["explanation"]
+127
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@@ -0,0 +1,127 @@
"""Tests for the Product Regulatory Navigator (missing-facts layer).
Acceptance: a well-filled company-profile yields <= 10 questions; known facts are
not re-asked; environmental questions are trigger-only (no law evaluation); the
Navigator decides which facts are missing, NOT what applies.
"""
from __future__ import annotations
from compliance.navigator import NavigatorResult, apply_answers, navigate
from compliance.navigator.questions import QUESTION_CATALOG, QuestionPriority
from compliance.profile import from_company_profile
from compliance.profile.canonical import CanonicalProductRegulatoryProfile, EconomicOperatorRole
COMPANY = {
"industry": "Maschinenbau",
"business_model": "B2B",
"company_size": "medium",
"target_markets": ["DE", "EU"],
"primary_jurisdiction": "DE",
"machine_builder": {
"productTypes": ["special_machine"],
"containsFirmware": True,
"hasSafetyFunction": True,
"isNetworked": True,
"hasRemoteAccess": True,
"hasOTAUpdates": True,
"hasRiskAssessment": True,
},
}
def _empty() -> CanonicalProductRegulatoryProfile:
return CanonicalProductRegulatoryProfile(name="X")
# 1. well-filled company-profile -> at most 10 questions.
def test_filled_company_profile_at_most_10_questions():
result = navigate(from_company_profile(COMPANY))
assert len(result.suggested_questions) <= 10
# 2. known facts (markets, is_machine) are not re-asked; true gaps still are.
def test_known_facts_not_reasked():
result = navigate(from_company_profile(COMPANY))
assert "markets" not in result.missing_facts
assert "is_machine" not in result.missing_facts
# genuine gaps the company-profile cannot provide are still surfaced
assert "economic_operator_role" in result.missing_facts
assert "has_radio_module" in result.missing_facts
# 3. environmental questions are trigger-only — no environmental-law evaluation.
def test_environmental_questions_are_triggers_only():
result = navigate(_empty())
env = [q for q in result.suggested_questions if q.target_field.startswith("environmental.")]
assert len(env) >= 3
assert all(q.answer_type.value == "bool" for q in env)
# 4. the Navigator decides only missing facts, never what applies.
def test_navigator_decides_only_missing_facts():
assert set(NavigatorResult.model_fields.keys()) == {
"missing_facts",
"suggested_questions",
"completeness_summary",
}
# no question carries a verdict — only metadata about what it would unblock
for q in QUESTION_CATALOG:
assert q.regulatory_domains_unblocked # metadata, not a decision
assert hasattr(q, "answer_type")
# 5. apply_answers updates the profile; answered facts drop out of missing.
def test_apply_answers_updates_profile():
profile = from_company_profile(COMPANY)
updated = apply_answers(
profile,
{
"economic_operator_role": "manufacturer",
"markets": ["DE", "US"],
"has_radio_module": True,
"env_wastewater": True,
},
)
assert updated.economic_operator_role == EconomicOperatorRole.MANUFACTURER
assert updated.markets == ["DE", "US"]
assert updated.has_radio_module is True
assert updated.environmental.discharges_to_wastewater is True
after = navigate(updated)
assert "economic_operator_role" not in after.missing_facts
assert "has_radio_module" not in after.missing_facts
assert "environmental.discharges_to_wastewater" not in after.missing_facts
# 6. questions are ordered P0 -> P1 -> P2.
def test_priority_ordering():
questions = navigate(_empty()).suggested_questions
orders = [q.order() for q in questions]
assert orders == sorted(orders)
assert questions[0].priority == QuestionPriority.P0
# 7. ready_for_scope flips once all P0 facts are answered.
def test_ready_for_scope_after_p0():
profile = _empty()
assert navigate(profile).completeness_summary.ready_for_scope is False
answered = apply_answers(
profile,
{
"markets": ["DE"],
"economic_operator_role": "manufacturer",
"lifecycle_phase": "placing_on_market",
"is_machine": True,
"is_component": False,
},
)
summary = navigate(answered).completeness_summary
assert summary.ready_for_scope is True
# 8. empty profile asks the full (bounded) catalog.
def test_empty_profile_bounded_catalog():
result = navigate(_empty())
assert len(result.suggested_questions) == len(QUESTION_CATALOG)
assert result.completeness_summary.total_relevant == len(QUESTION_CATALOG)
@@ -0,0 +1,149 @@
"""Tests for the product-scope orchestrator (step 3).
Acceptance: missing P0 facts -> discover_scope NOT run; ready -> run exactly once;
response separates applicable/excluded/uncertain; environmental triggers appear
only as unsupported_domain (future_corpus_needed), never as a legal evaluation.
"""
from __future__ import annotations
import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient
import compliance.product_scope.orchestrator as orch
from compliance.product_scope import ScopeStatus, resolve_product_scope
from compliance.profile.canonical import (
CanonicalLifecyclePhase,
CanonicalProductRegulatoryProfile,
CanonicalProductType,
EconomicOperatorRole,
EnvironmentalImpact,
)
_KNOWN_REGS = {"CRA", "MaschinenVO", "RED", "EMV", "DataAct", "NIS2"}
def ready_profile(**ov) -> CanonicalProductRegulatoryProfile:
base = dict(
name="Industriespülmaschine",
product_type=CanonicalProductType.MACHINERY,
markets=["EU", "DE"],
economic_operator_role=EconomicOperatorRole.MANUFACTURER,
lifecycle_phase=CanonicalLifecyclePhase.PLACING_ON_MARKET,
is_machine=True,
is_component=False,
has_software_updates=True,
has_embedded_software=True,
has_remote_access=True,
has_safety_function=True,
technologies=["cloud", "ota_updates"],
)
base.update(ov)
return CanonicalProductRegulatoryProfile(**base)
def _spy(monkeypatch):
calls = {"n": 0}
real = orch.discover_scope
def counting(profile):
calls["n"] += 1
return real(profile)
monkeypatch.setattr(orch, "discover_scope", counting)
return calls
# 1. missing P0 facts -> discover_scope is NOT executed.
def test_needs_facts_does_not_run_scope(monkeypatch):
calls = _spy(monkeypatch)
resp = resolve_product_scope(CanonicalProductRegulatoryProfile(name="X"))
assert resp.status == ScopeStatus.NEEDS_FACTS
assert resp.regulatory_scope is None
assert resp.missing_facts
assert calls["n"] == 0
# 2. ready_for_scope -> discover_scope runs exactly once.
def test_ready_runs_scope_once(monkeypatch):
calls = _spy(monkeypatch)
resp = resolve_product_scope(ready_profile())
assert resp.status == ScopeStatus.RESOLVED
assert resp.regulatory_scope is not None
assert calls["n"] == 1
applicable = {r.regulation_id for r in resp.regulatory_scope.applicable_regulations}
assert "CRA" in applicable and "MaschinenVO" in applicable
# 3. the response separates the regulation categories.
def test_response_separates_categories():
scope = resolve_product_scope(ready_profile()).regulatory_scope
assert scope is not None
# all three buckets exist and only carry known regulation ids
for bucket in (scope.applicable_regulations, scope.excluded_regulations, scope.uncertain_regulations):
for r in bucket:
assert r.regulation_id in _KNOWN_REGS
assert scope.uncertain_regulations # e.g. RED/DataAct/NIS2 with unknown facts
# 4. environmental triggers surface ONLY as unsupported_domain, never as law.
def test_environmental_only_unsupported_domain():
profile = ready_profile(
environmental=EnvironmentalImpact(discharges_to_wastewater=True, uses_cleaning_chemicals=True)
)
scope = resolve_product_scope(profile).regulatory_scope
assert scope is not None
domains = {d.domain for d in scope.unsupported_domains}
assert "environment_water" in domains and "chemicals" in domains
assert all(d.status == "future_corpus_needed" for d in scope.unsupported_domains)
# no environmental "regulation" leaked into the scope verdict
all_regs = (
scope.applicable_regulations + scope.excluded_regulations + scope.uncertain_regulations
)
assert all(r.regulation_id in _KNOWN_REGS for r in all_regs)
# 5. endpoint smoke — both cases.
@pytest.fixture(scope="module")
def client():
from compliance.api.reasoning_routes import router
app = FastAPI()
app.include_router(router)
return TestClient(app)
def test_endpoint_needs_facts(client):
r = client.post("/reasoning/product-scope", json={"product_profile": {"name": "X"}})
assert r.status_code == 200
body = r.json()
assert body["status"] == "needs_facts"
assert body["regulatory_scope"] is None
assert body["missing_facts"]
def test_endpoint_resolved(client):
r = client.post(
"/reasoning/product-scope",
json={
"product_profile": {
"name": "M",
"product_type": "machinery",
"markets": ["EU"],
"economic_operator_role": "manufacturer",
"lifecycle_phase": "placing_on_market",
"is_machine": True,
"is_component": False,
"has_software_updates": True,
"has_embedded_software": True,
"has_remote_access": True,
"technologies": ["cloud"],
}
},
)
assert r.status_code == 200
body = r.json()
assert body["status"] == "resolved"
applicable = {x["regulation_id"] for x in body["regulatory_scope"]["applicable_regulations"]}
assert "CRA" in applicable and "MaschinenVO" in applicable
@@ -0,0 +1,188 @@
"""Tests for the Product Profile convergence layer.
Covers the 10 acceptance criteria of the CanonicalProductRegulatoryProfile spec:
lossless ProductWizard mapping, company-profile prefill, AI stays delegated,
markets no longer hardcoded, and the new Navigator fields (role/radio/usage-data/
lifecycle/BOM) plus one-semantic-profile across reasoning + gap.
"""
from __future__ import annotations
from compliance.profile import (
CanonicalLifecyclePhase,
CanonicalProductRegulatoryProfile,
CanonicalProductType,
ComponentKind,
EconomicOperatorRole,
ProductComponent,
from_company_profile,
from_product_wizard,
to_gap_profile,
to_reasoning_profile,
)
from compliance.reasoning import discover_scope
from compliance.reasoning.enums import ManufacturerRole, ProductLifecyclePhase
# A realistic ProductWizard payload — exactly the gap.ProductProfile JSON shape.
WIZARD = {
"name": "Industriespülmaschine",
"description": "vernetzte Spülmaschine",
"product_type": "machinery",
"technologies": ["cloud", "ota_updates", "sensor", "actuator"],
"data_processing": ["telemetry"],
"markets": ["EU"],
"connected_to_internet": True,
"has_software_updates": True,
"uses_ai": False,
"processes_personal_data": False,
"is_critical_infra_supplier": False,
"existing_certifications": ["CE"],
"applied_norms": ["ISO12100"],
"has_risk_assessment": True,
"has_technical_file": True,
"has_operating_manual": True,
"has_sbom": False,
"has_vuln_management": False,
"has_update_mechanism": True,
"has_incident_response": False,
"has_supply_chain_mgmt": False,
"ce_marking_since": "",
"product_age": "5",
}
COMPANY = {
"company_name": "ACME Maschinen GmbH",
"industry": "Maschinenbau",
"business_model": "B2B",
"company_size": "medium",
"target_markets": ["DE", "EU"],
"primary_jurisdiction": "DE",
"headquarters_country": "DE",
"uses_ai": False,
"is_data_controller": True,
"machine_builder": {
"productDescription": "Industriespülmaschine",
"productTypes": ["special_machine"],
"containsSoftware": True,
"containsFirmware": True,
"containsAI": False,
"hasSafetyFunction": True,
"safetyFunctionDescription": "Türverriegelung",
"isNetworked": True,
"hasRemoteAccess": True,
"hasOTAUpdates": True,
"hasRiskAssessment": True,
"criticalSectorClients": False,
},
}
# 1. ProductWizard data maps losslessly into the canonical and back to gap shape.
def test_product_wizard_lossless_roundtrip():
canonical = from_product_wizard(WIZARD)
assert to_gap_profile(canonical) == WIZARD
# 2. company-profile can prefill the canonical profile.
def test_company_profile_prefill():
c = from_company_profile(COMPANY)
assert c.sector_industry == "Maschinenbau"
assert c.b2b_or_b2c == "B2B"
assert c.company_size == "medium"
assert "DE" in c.markets and "EU" in c.markets
assert c.has_safety_function is True
assert c.has_remote_access is True
assert c.has_embedded_software is True
assert c.is_machine is True
assert c.description == "Industriespülmaschine"
# 3. AI-Act/ucca stays delegated — only uses_ai is forwarded, no risk classification.
def test_ai_classification_stays_delegated():
c = CanonicalProductRegulatoryProfile(name="X", uses_ai=True)
rp = to_reasoning_profile(c)
assert rp.has_ai_functionality is True
assert not hasattr(rp, "ai_risk_category") # no AI classification produced here
# 4. markets are a real list, never hardcoded ['EU'].
def test_markets_not_hardcoded_eu():
assert CanonicalProductRegulatoryProfile(name="X").markets == []
c = from_product_wizard({**WIZARD, "markets": ["US", "JP", "CA"]})
assert c.markets == ["US", "JP", "CA"]
assert to_gap_profile(c)["markets"] == ["US", "JP", "CA"]
assert to_reasoning_profile(c).eu_market is False # non-EU markets -> not EU
# 5. economic-operator role exists and maps to the reasoning role.
def test_economic_operator_role_exists():
c = CanonicalProductRegulatoryProfile(name="X", economic_operator_role=EconomicOperatorRole.IMPORTER)
assert to_reasoning_profile(c).manufacturer_role == ManufacturerRole.IMPORTER
# 6. radio_module exists (direct + inferred from a BOM component).
def test_radio_module_exists():
assert to_reasoning_profile(CanonicalProductRegulatoryProfile(name="X", has_radio_module=True)).has_radio_module is True
c = CanonicalProductRegulatoryProfile(name="X", components=[ProductComponent(name="WLAN", kind=ComponentKind.RADIO_MODULE)])
assert to_reasoning_profile(c).has_radio_module is True
# 7. generates_usage_data exists (direct + inferred from telemetry).
def test_generates_usage_data_exists():
c = CanonicalProductRegulatoryProfile(name="X", generates_usage_data=True)
assert to_reasoning_profile(c).generates_usage_data is True
inferred = from_product_wizard(WIZARD) # data_processing has telemetry
assert to_reasoning_profile(inferred).generates_usage_data is True
# 8. lifecycle_phase exists and maps.
def test_lifecycle_phase_exists():
c = CanonicalProductRegulatoryProfile(name="X", lifecycle_phase=CanonicalLifecyclePhase.MAINTENANCE)
assert to_reasoning_profile(c).lifecycle_phase == ProductLifecyclePhase.MAINTENANCE
# 9. BOM components are structured.
def test_bom_components_structured():
c = CanonicalProductRegulatoryProfile(
name="Spülmaschine",
components=[
ProductComponent(name="Umwälzpumpe", kind=ComponentKind.PUMP),
ProductComponent(name="Heizung", kind=ComponentKind.HEATING),
ProductComponent(name="SPS", kind=ComponentKind.PLC),
ProductComponent(name="Abwasserablauf", kind=ComponentKind.WASTEWATER_OUTLET),
],
)
kinds = {comp.kind for comp in c.components}
assert ComponentKind.PLC in kinds and ComponentKind.WASTEWATER_OUTLET in kinds
# 10. reasoning engine + gap engine run off ONE semantic profile.
def test_one_semantic_profile_reasoning_and_gap():
canonical = CanonicalProductRegulatoryProfile(
name="Industriespülmaschine",
product_type=CanonicalProductType.MACHINERY,
economic_operator_role=EconomicOperatorRole.MANUFACTURER,
markets=["EU", "DE"],
is_machine=True,
has_safety_function=True,
has_remote_access=True,
has_software_updates=True,
has_embedded_software=True,
technologies=["cloud", "ota_updates"],
)
gap = to_gap_profile(canonical)
rp = to_reasoning_profile(canonical)
# same facts, two projections
assert gap["markets"] == ["EU", "DE"]
assert rp.eu_market is True
assert rp.has_remote_access is True
assert rp.has_cloud_connection is True
assert rp.is_machine is True
assert rp.manufacturer_role == ManufacturerRole.MANUFACTURER
# the projected reasoning profile actually drives the reasoning engine
scope = discover_scope(rp)
applicable = {r.regulation_id for r in scope.applicable_regulations}
assert "CRA" in applicable
assert "MaschinenVO" in applicable
+148
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@@ -0,0 +1,148 @@
"""Tests for Regulatory Change Intelligence (RCI).
Acceptance: a simulated RegulatoryChange against a stored ComplianceBaseline can
answer: (1) does it affect this product? (2) which obligations are new/changed?
(3) which are likely already covered by existing evidence? (4) what must a human
review? (5) what is not relevant?
"""
from __future__ import annotations
from compliance.profile.canonical import (
CanonicalLifecyclePhase,
CanonicalProductRegulatoryProfile,
CanonicalProductType,
EconomicOperatorRole,
)
from compliance.rci import (
ChangeType,
DeltaType,
RegulatoryChange,
assess_change,
create_baseline,
)
PROFILE = CanonicalProductRegulatoryProfile(
name="Industriespülmaschine",
product_type=CanonicalProductType.MACHINERY,
markets=["EU", "DE"],
economic_operator_role=EconomicOperatorRole.MANUFACTURER,
lifecycle_phase=CanonicalLifecyclePhase.PLACING_ON_MARKET,
is_machine=True,
is_component=False,
has_software_updates=True,
has_embedded_software=True,
has_remote_access=True,
technologies=["cloud", "ota_updates"],
)
# Evidence the customer already has, per obligation.
EVIDENCE = {"provide_security_updates": ["policy", "ticket"], "sbom_creation": ["sbom"]}
BASELINE = create_baseline(PROFILE, EVIDENCE, baseline_id="b1")
def _change(obs, regs=("CRA",), ctype=ChangeType.AMENDMENT, cid="c"):
return RegulatoryChange(
change_id=cid, affected_regulations=list(regs), affected_obligations=list(obs), change_type=ctype
)
def _by_id(assessment):
return {d.obligation_id: d for d in assessment.deltas}
# Baseline snapshots the registry-linked obligations from the frozen map.
def test_baseline_snapshots_registry_obligations():
assert "sbom_creation" in BASELINE.applicable_obligations
assert "provide_security_updates" in BASELINE.applicable_obligations
assert BASELINE.regulatory_map_snapshot.scope_resolved is True
# 1 + 2. affects the product + flags a NEW obligation.
def test_affects_product_and_new_obligation():
a = assess_change(BASELINE, _change(["cra_new_requirement_xyz"], cid="c1"))
assert a.affects_product is True
assert _by_id(a)["cra_new_requirement_xyz"].delta_type == DeltaType.NEW
# 2. an existing obligation amended -> CHANGED.
def test_existing_obligation_changed():
a = assess_change(BASELINE, _change(["sbom_creation"], cid="c2"))
assert _by_id(a)["sbom_creation"].delta_type == DeltaType.CHANGED
# 3. existing obligation with evidence + guidance update -> ALREADY_COVERED.
def test_already_covered_by_evidence():
a = assess_change(BASELINE, _change(["provide_security_updates"], ctype=ChangeType.GUIDANCE_UPDATE, cid="c3"))
assert _by_id(a)["provide_security_updates"].delta_type == DeltaType.ALREADY_COVERED
assert a.summary.already_covered == ["provide_security_updates"]
# 4. what a human must review (existing obligation without evidence).
def test_needs_review():
a = assess_change(BASELINE, _change(["vuln_handling_process"], ctype=ChangeType.GUIDANCE_UPDATE, cid="c4"))
assert _by_id(a)["vuln_handling_process"].delta_type == DeltaType.NEEDS_REVIEW
assert "vuln_handling_process" in a.summary.needs_review
assert "vuln_handling_process" in a.summary.what_matters_for_this_product
# 5. a change to a regulation NOT in the map -> not relevant.
def test_not_relevant_offmap_regulation():
a = assess_change(BASELINE, _change(["psd2_strong_customer_auth"], regs=["PSD2"], ctype=ChangeType.NEW_REGULATION, cid="c5"))
assert a.affects_product is False
assert _by_id(a)["psd2_strong_customer_auth"].delta_type == DeltaType.NOT_APPLICABLE
assert a.summary.not_relevant == ["psd2_strong_customer_auth"]
# repeal removes an existing obligation.
def test_repeal_removes_existing():
a = assess_change(BASELINE, _change(["sbom_creation"], ctype=ChangeType.REPEAL, cid="c6"))
assert _by_id(a)["sbom_creation"].delta_type == DeltaType.REMOVED
# missing evidence is computed against the obligation's required evidence.
def test_missing_evidence_on_changed():
a = assess_change(BASELINE, _change(["sbom_creation"], cid="c7")) # requires sbom+repo_scan, has sbom
d = _by_id(a)["sbom_creation"]
assert "sbom" in d.affected_evidence
assert "repo_scan" in d.missing_evidence
# a change to an UNCERTAIN regulation -> needs review (resolve applicability first).
def test_uncertain_regulation_needs_review():
a = assess_change(BASELINE, _change(["red_cyber_req"], regs=["RED"], cid="c8"))
assert a.affects_product is True # RED is in the map's uncertain bucket
assert _by_id(a)["red_cyber_req"].delta_type == DeltaType.NEEDS_REVIEW
# RCI answers "vs my map", not "what does the law say" — and works only on the snapshot.
def test_works_against_stored_map_no_reevaluation():
# a change with no affected_obligations still resolves affects_product from the map
a = assess_change(BASELINE, RegulatoryChange(change_id="c9", affected_regulations=["CRA"], affected_obligations=[], change_type=ChangeType.AMENDMENT))
assert a.affects_product is True
assert a.deltas == []
# delta_type is a THIRD vocabulary, disjoint from ClaimCoverage (Welt 1).
def test_delta_vocabulary_distinct_from_claimcoverage():
from compliance.reasoning.enums import ClaimCoverage
assert {d.value for d in DeltaType}.isdisjoint({c.value for c in ClaimCoverage})
# the management summary aggregates the five buckets coherently.
def test_summary_buckets():
a = assess_change(
BASELINE,
RegulatoryChange(
change_id="c10",
affected_regulations=["CRA"],
affected_obligations=["cra_new_one", "sbom_creation", "provide_security_updates"],
change_type=ChangeType.AMENDMENT,
),
)
s = a.summary
assert "cra_new_one" in s.what_matters_for_this_product # NEW
assert "sbom_creation" in s.needs_review # CHANGED -> review
assert s.what_changed # non-empty management line
@@ -0,0 +1,282 @@
"""Tests for the Regulatory Reasoning Engine.
Covers the five typical machine-builder scenarios and the ten acceptance
questions from the build spec (§15). Engine tests are pure (no DB); the
endpoint smoke tests mount only the reasoning router.
"""
from __future__ import annotations
from datetime import date
import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient
from compliance.reasoning import (
assess_interpretation,
derive_obligations,
discover_scope,
normalize_claim,
reason_implementation_claim,
)
from compliance.reasoning.enums import (
ApplicabilityStatus,
ClaimCoverage,
InterpretationVerdict,
)
from compliance.reasoning.schemas import ProductProfile
from compliance.reasoning.enums import ManufacturerRole
# ---------------------------------------------------------------------------
# Fixtures / builders
# ---------------------------------------------------------------------------
def sps_profile(**overrides) -> ProductProfile:
base = dict(
product_name="SPS mit HMI",
product_type=["SPS", "HMI", "Schaltschrank"],
has_software=True,
has_remote_access=True,
has_cloud_connection=True,
eu_market=True,
manufacturer_role=ManufacturerRole.MANUFACTURER,
)
base.update(overrides)
return ProductProfile(**base)
def _reg_ids(scope, attr):
return [getattr(r, "regulation_id") for r in getattr(scope, attr)]
# ---------------------------------------------------------------------------
# 1. Gilt CRA für eine SPS mit Fernwartung?
# ---------------------------------------------------------------------------
def test_cra_applies_to_sps_with_remote_access():
scope = discover_scope(sps_profile())
cra = [r for r in scope.applicable_regulations if r.regulation_id == "CRA"]
assert cra and cra[0].applicability_status == ApplicabilityStatus.APPLICABLE
assert cra[0].confidence.value == "high"
assert any("digitale Elemente" in f or "Fernzugriff" in f for f in cra[0].trigger_facts) or cra[0].trigger_facts
# ---------------------------------------------------------------------------
# 2. Katalogprodukt 2027 weiter verkauft -> CRA gilt; "nur neue Produkte" zu eng
# ---------------------------------------------------------------------------
def test_cra_applies_to_finished_catalog_product():
profile = sps_profile(placed_on_market_after=date(2027, 1, 1), lifecycle_phase="placing_on_market")
scope = discover_scope(profile)
assert "CRA" in _reg_ids(scope, "applicable_regulations")
def test_interpretation_only_new_products_is_too_narrow():
result = assess_interpretation("Wir glauben, der CRA gilt nur für neue Produkte.")
assert result.assessment == InterpretationVerdict.TOO_NARROW
assert "CRA" in result.affected_regulations
assert result.corrected_interpretation
assert result.legal_basis_refs
# ---------------------------------------------------------------------------
# 3. Reicht eine SBOM allein? -> nein, nur teilweise
# ---------------------------------------------------------------------------
def test_sbom_alone_is_not_enough():
resp = reason_implementation_claim(sps_profile(), "Wir haben SBOMs.")
sbom = [m for m in resp.mappings if m.obligation_id == "sbom_creation"]
assert sbom and sbom[0].claim_coverage == ClaimCoverage.POTENTIALLY_ADDRESSES
# but other obligations are surfaced as gaps -> claim does not address everything
assert any(m.claim_coverage != ClaimCoverage.POTENTIALLY_ADDRESSES for m in resp.mappings)
assert "Nachweise" in resp.summary
# ---------------------------------------------------------------------------
# 4. Ist ein reaktiver Updateprozess ausreichend? -> nur teilweise
# ---------------------------------------------------------------------------
def test_reactive_update_process_is_partial():
resp = reason_implementation_claim(
sps_profile(), "Wir machen Updates, wenn Kunden Fehler melden."
)
upd = [m for m in resp.mappings if m.obligation_id == "provide_security_updates"]
assert upd and upd[0].claim_coverage == ClaimCoverage.PARTIALLY_ADDRESSES
assert "reactive" in resp.claim.qualifiers
assert any("Schwachstellenüberwachung" in e for e in upd[0].missing_elements)
# ---------------------------------------------------------------------------
# 5. Wann überschneiden sich CRA und MaschinenVO?
# ---------------------------------------------------------------------------
def test_cra_and_machinery_overlap_on_cyber_safety():
profile = sps_profile(is_machine=True, has_safety_function=True)
resp = derive_obligations(profile)
ids = [o.obligation_id for o in resp.applicable_obligations]
assert "machine_protection_against_corruption" in ids
assert "vuln_handling_process" in ids
vuln_overlap = [o for o in resp.overlaps if o.overlap_group_id == "VULNERABILITY_HANDLING"]
assert vuln_overlap
assert "machine_protection_against_corruption" in vuln_overlap[0].obligations
# ---------------------------------------------------------------------------
# 6. Wann ist Data Act zusätzlich relevant?
# ---------------------------------------------------------------------------
def test_data_act_relevant_when_product_generates_data():
scope = discover_scope(sps_profile(generates_usage_data=True))
assert "DataAct" in _reg_ids(scope, "applicable_regulations")
obs = derive_obligations(sps_profile(generates_usage_data=True))
assert any(o.source_regulation == "DataAct" for o in obs.applicable_obligations)
def test_data_act_uncertain_when_data_unknown():
scope = discover_scope(sps_profile()) # generates_usage_data=None
assert "DataAct" in _reg_ids(scope, "uncertain_regulations")
# ---------------------------------------------------------------------------
# 7. Welche Pflichten gelten nicht ohne Funkmodul?
# ---------------------------------------------------------------------------
def test_no_radio_module_excludes_red():
scope = discover_scope(sps_profile(has_radio_module=False))
assert "RED" in _reg_ids(scope, "excluded_regulations")
assert "RED" not in _reg_ids(scope, "applicable_regulations")
def test_radio_unknown_makes_red_uncertain():
scope = discover_scope(sps_profile()) # has_radio_module=None
assert "RED" in _reg_ids(scope, "uncertain_regulations")
# ---------------------------------------------------------------------------
# 8. Welche Fakten fehlen für eine NIS2-Bewertung?
# ---------------------------------------------------------------------------
def test_nis2_missing_facts():
scope = discover_scope(sps_profile())
nis2 = [r for r in scope.uncertain_regulations if r.regulation_id == "NIS2"]
assert nis2
joined = " ".join(nis2[0].missing_facts).lower()
assert "unternehmensgröße" in joined and "sektor" in joined
# ---------------------------------------------------------------------------
# 9. Welche Nachweise decken mehrere Pflichten gleichzeitig? (USP)
# ---------------------------------------------------------------------------
def test_evidence_covers_multiple_obligations():
resp = derive_obligations(sps_profile())
multi = resp.evidence_for_multiple
assert multi # at least one evidence type spans >1 obligation
assert all(len(ids) > 1 for ids in multi.values())
assert "policy" in multi # the CRA process docs share a policy evidence
# ---------------------------------------------------------------------------
# 10. Auslegungen: zu eng / zu weit / plausibel / unbekannt
# ---------------------------------------------------------------------------
def test_interpretation_unknown_returns_uncertain():
result = assess_interpretation("Der Mond beeinflusst unsere Updatezyklen.")
assert result.assessment == InterpretationVerdict.UNCERTAIN
assert result.corrected_interpretation
def test_interpretation_open_source_partially_correct():
result = assess_interpretation("Open Source ist ausgenommen, also betrifft uns der CRA nicht.")
assert result.assessment == InterpretationVerdict.PARTIALLY_CORRECT
# ---------------------------------------------------------------------------
# Registry-alignment + contract guards
# ---------------------------------------------------------------------------
def test_cra_obligations_reuse_registry_ids_not_minted():
resp = derive_obligations(sps_profile())
anchored = [o for o in resp.applicable_obligations if o.registry_anchor]
assert "sbom_creation" in [o.obligation_id for o in anchored]
assert "provide_security_updates" in [o.obligation_id for o in anchored]
# machine obligations are proposed, never claimed as registry-owned
machine = [o for o in resp.applicable_obligations if o.source_regulation == "MaschinenVO"]
assert all(o.proposed and not o.registry_anchor for o in machine)
def test_required_evidence_only_uses_shared_catalog():
from compliance.reasoning.rules_types import EVIDENCE_CATALOG
from compliance.reasoning.rules_obligations import ALL_OBLIGATIONS
for rule in ALL_OBLIGATIONS:
assert set(rule.required_evidence) <= EVIDENCE_CATALOG
def test_claim_normalizer_is_deterministic():
a = normalize_claim("Wir haben einen Update-Prozess.")
b = normalize_claim("Wir haben einen Update-Prozess.")
assert a.claim_id == b.claim_id
assert "secure_updates" in a.claimed_capability
def test_unspecific_claim_asks_for_detail():
resp = reason_implementation_claim(sps_profile(), "Wir sind sicher aufgestellt.")
assert resp.mappings == [] or all(
m.claim_coverage == ClaimCoverage.INSUFFICIENT_INFORMATION for m in resp.mappings
)
assert "unspezifisch" in resp.summary.lower()
def test_claim_reasoning_carries_no_compliance_verdict():
"""Welt-1 boundary: claim mapping must never read as a conformity verdict."""
resp = reason_implementation_claim(
sps_profile(), "Wir haben SBOMs und einen Update-Prozess."
)
# claim-relative vocabulary only
for m in resp.mappings:
assert m.claim_coverage in set(ClaimCoverage)
# no compliance wording leaks into summary or explanations
assert "erfüllt" not in resp.summary
assert all("erfüllt" not in m.explanation for m in resp.mappings)
# explicit disclaimer separating ClaimCoverage (Welt 1) from ComplianceStatus (Welt 2)
assert resp.disclaimer
assert "ComplianceStatus" in resp.disclaimer and "Nachweis" in resp.disclaimer
# ---------------------------------------------------------------------------
# Endpoint smoke tests
# ---------------------------------------------------------------------------
@pytest.fixture(scope="module")
def client():
from compliance.api.reasoning_routes import router
app = FastAPI()
app.include_router(router)
return TestClient(app)
def test_endpoint_scope(client):
r = client.post("/reasoning/scope", json={"product_profile": {"product_name": "X", "has_software": True, "eu_market": True, "manufacturer_role": "manufacturer"}})
assert r.status_code == 200
body = r.json()
assert "CRA" in [x["regulation_id"] for x in body["regulatory_scope"]["applicable_regulations"]]
def test_endpoint_obligations(client):
r = client.post(
"/reasoning/obligations",
json={"product_profile": {"product_name": "X", "has_software": True, "has_remote_access": True, "eu_market": True, "manufacturer_role": "manufacturer"}},
)
assert r.status_code == 200
assert r.json()["applicable_obligations"]
def test_endpoint_implementation(client):
r = client.post(
"/reasoning/implementation-reasoning",
json={"product_profile": {"product_name": "X", "has_software": True, "eu_market": True, "manufacturer_role": "manufacturer"}, "customer_claim": "Wir haben SBOMs."},
)
assert r.status_code == 200
body = r.json()
assert body["mappings"]
assert body["disclaimer"]
def test_endpoint_interpretation(client):
r = client.post(
"/reasoning/interpretation-assessment",
json={"customer_interpretation": "CRA gilt nur für neue Produkte."},
)
assert r.status_code == 200
assert r.json()["assessment"] == "too_narrow"
@@ -0,0 +1,159 @@
"""Tests for the Regulatory Map renderer (step 4).
Acceptance: the renderer makes no own legal decisions (it composes the scope +
registry-linked obligations); CRA/MaschVO/EMV are separate; RED/DataAct/NIS2 are
uncertain; environmental is unsupported (not applicable); obligations appear only
when registry-linkable; the executive summary has no percentage.
"""
from __future__ import annotations
import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient
from compliance.product_scope import resolve_product_scope
from compliance.profile.canonical import (
CanonicalLifecyclePhase,
CanonicalProductRegulatoryProfile,
CanonicalProductType,
EconomicOperatorRole,
EnvironmentalImpact,
)
from compliance.regulatory_map import render_regulatory_map
_PROPOSED_IDS = {
"machine_risk_assessment", "machine_safety_control_systems", "machine_protection_against_corruption",
"machine_instructions_for_use", "machine_ce_conformity", "data_act_data_access_by_design",
"data_act_user_data_access", "cra_secure_by_design", "cra_risk_assessment",
"cra_technical_documentation", "cra_ce_conformity_assessment", "cra_instructions_for_use",
}
def ready_profile(**ov) -> CanonicalProductRegulatoryProfile:
base = dict(
name="Industriespülmaschine",
product_type=CanonicalProductType.MACHINERY,
markets=["EU", "DE"],
economic_operator_role=EconomicOperatorRole.MANUFACTURER,
lifecycle_phase=CanonicalLifecyclePhase.PLACING_ON_MARKET,
is_machine=True,
is_component=False,
has_software_updates=True,
has_embedded_software=True,
has_remote_access=True,
technologies=["cloud", "ota_updates"],
)
base.update(ov)
return CanonicalProductRegulatoryProfile(**base)
# 1. renderer makes no own decisions — it mirrors the scope verdict exactly.
def test_no_own_legal_decisions():
p = ready_profile()
m = render_regulatory_map(p)
scope = resolve_product_scope(p).regulatory_scope
assert {v.regulation_id for v in m.applicable_regulations} == {
r.regulation_id for r in scope.applicable_regulations
}
assert {v.regulation_id for v in m.uncertain_regulations} == {
r.regulation_id for r in scope.uncertain_regulations
}
# 2/3/5. CRA/MaschVO/EMV separate applicable; RED/DataAct/NIS2 uncertain.
def test_regulation_separation():
m = render_regulatory_map(ready_profile())
applicable = {v.regulation_id for v in m.applicable_regulations}
uncertain = {v.regulation_id for v in m.uncertain_regulations}
assert {"CRA", "MaschinenVO", "EMV"} <= applicable
assert {"RED", "DataAct", "NIS2"} <= uncertain
# 4. environmental triggers surface as unsupported_domain, never applicable.
def test_environmental_unsupported_not_applicable():
p = ready_profile(environmental=EnvironmentalImpact(discharges_to_wastewater=True, uses_cleaning_chemicals=True))
m = render_regulatory_map(p)
domains = {d.domain for d in m.unsupported_domains}
assert "environment_water" in domains and "chemicals" in domains
assert all(v.regulation_id in {"CRA", "MaschinenVO", "RED", "DataAct", "EMV", "NIS2"} for v in m.applicable_regulations)
# 6. obligations are shown only when a registry id is linkable.
def test_obligations_only_registry_linkable():
m = render_regulatory_map(ready_profile())
shown = {o.obligation_id for v in m.applicable_regulations for o in v.obligations}
assert shown # CRA registry obligations are shown
assert "sbom_creation" in shown
assert not (shown & _PROPOSED_IDS) # no proposed (non-registry) obligation leaks in
# MaschinenVO is applicable but its obligations are proposed -> empty + note
machvo = next(v for v in m.applicable_regulations if v.regulation_id == "MaschinenVO")
assert machvo.obligations == []
assert machvo.obligations_note
# 7. executive summary contains no percentage.
def test_executive_summary_no_percent():
m = render_regulatory_map(ready_profile())
assert "%" not in m.executive_summary
assert "prozent" not in m.executive_summary.lower()
# 8. output is customer-readable and structured.
def test_customer_readable():
m = render_regulatory_map(ready_profile())
assert m.product_summary
assert "wahrscheinlich" in m.executive_summary
assert "Unsicher" in m.executive_summary
assert m.trigger_facts
# needs-facts profile -> map says scope not yet resolved.
def test_needs_facts_map():
m = render_regulatory_map(CanonicalProductRegulatoryProfile(name="X"))
assert m.scope_resolved is False
assert "Mindestfakten" in m.executive_summary
assert m.applicable_regulations == []
# uncertain RED links to the radio navigator question.
def test_uncertain_links_to_navigator_question():
m = render_regulatory_map(ready_profile())
red = next(v for v in m.uncertain_regulations if v.regulation_id == "RED")
assert "has_radio_module" in red.question_refs
# endpoint smoke.
@pytest.fixture(scope="module")
def client():
from compliance.api.reasoning_routes import router
app = FastAPI()
app.include_router(router)
return TestClient(app)
def test_endpoint_regulatory_map(client):
r = client.post(
"/reasoning/regulatory-map",
json={
"product_profile": {
"name": "M",
"product_type": "machinery",
"markets": ["EU"],
"economic_operator_role": "manufacturer",
"lifecycle_phase": "placing_on_market",
"is_machine": True,
"is_component": False,
"has_software_updates": True,
"has_embedded_software": True,
"has_remote_access": True,
"technologies": ["cloud"],
}
},
)
assert r.status_code == 200
body = r.json()
assert body["scope_resolved"] is True
assert {v["regulation_id"] for v in body["applicable_regulations"]} >= {"CRA", "MaschinenVO"}
assert "%" not in body["executive_summary"]
+203
View File
@@ -0,0 +1,203 @@
# Capability Model v1 — Objektarten & Beziehungstypen (Schema-Papier, NICHT materialisiert)
Status: **OFFEN / Entscheidung erforderlich (2026-06-26).** Dies ist Schritt **#5a** (Papier).
Schritt **#5b** (Materialisierung: `capabilities.json`, Migration, Obligation→Capability-Links,
Guidance-Mapping, Runtime) ist **GEGATED** auf die Annahme dieses Papiers. **Es wurde noch keine
Zeile Daten verschoben.**
Baut auf [legal_obligation_layer_v1.md](legal_obligation_layer_v1.md),
[obligation_registry_v1.md](obligation_registry_v1.md) und dem Cross-Domain-Review
(`obligations/cross_domain_relationships.json`, Commit `ed31fdc0`).
---
## 0. Warum ein Papier statt `capabilities.json`
Die Plattform hat drei empirische Architektur-Sprünge gemacht:
1. **Control ≠ Wissensobjekt** → Legal Obligation (sofort implementiert, datenbestätigt).
2. **Procedure ist eigenständig** (implementiert: `cra_procedures.json`).
3. **Capabilities tauchen domänenübergreifend wieder auf** (Cross-Domain-Review).
(1) und (2) waren breit datenbelegt → sofort umgesetzt. Bei (3) ist die **Objektart selbst noch
nicht definiert.** Wir wissen NICHT genau, was eine Capability ist. Materialisieren wir jetzt,
riskieren wir, in drei Wochen festzustellen: „Attack Surface war gar keine Capability" → Umbau.
---
## 1. Der Auslöser: die 8 „Capabilities" sind NICHT eine Objektart
Der Cross-Domain-Review fand 16 `SHARED_CAPABILITY`-Paare → 8 Cluster. Bei Inspektion zerfallen
sie in **zwei verschiedene Objektarten**:
| Cluster (Opus-Benennung) | Art | Begründung |
|---|---|---|
| `mfa` | **Capability** | implementierbar als Funktion |
| `session_management` | **Capability** | implementierbar |
| `transport_encryption` (tls/mutual_tls/cert) | **Capability** | implementierbar (vom Klassifikator fein gesplittet → 1 Capability) |
| `code_signing` | **Capability** | implementierbar |
| `anomaly_detection` | **Capability** | implementierbar |
| `access_control` | **Ziel** (schwach) | abstraktes Ziel, kein Baustein — eher OVERLAP (siehe Konsolidierung) |
Dazu die **zwei Gap-„Obligations" aus Handoff #4** (NIST SI-7/CM-7 waren breiter als jeder
einzelne Treffer):
| Kandidat | Art | Begründung |
|---|---|---|
| `software_integrity_protection` (SI-7) | **Sicherheitsziel** | wird NICHT direkt gebaut; erreicht durch code_signing + hash_verification + secure_boot |
| `attack_surface_minimization` (CM-7) | **Sicherheitsziel** | erreicht durch least_functionality + Port-Deaktivierung + Interface-Reduktion |
**Kernbeobachtung (User):** Es gibt **Typ 1 — technische Fähigkeiten** (implementierbar) und
**Typ 2 — Sicherheitsziele** (nicht direkt implementierbar, durch mehrere Capabilities erreicht).
Sie in eine `capabilities.json` zu werfen wäre der Fehler.
```
Integrity Protection (Ziel) Access Protection (Ziel)
↑ erreicht durch ↑ erreicht durch
code_signing · hash_verification · mfa · session_management ·
secure_boot (Capabilities) credential_storage (Capabilities)
```
Das erklärt rückwirkend auch das **systematische Synth-Über-Tiering** (Auth 14→6, Remote 14→5):
das LLM mischte ziel-nahe Obligations mit fähigkeits-nahen Mechanismen, weil die Modellsprache
die Ebenen nicht trennte.
---
## 2. Kandidat-Objektarten
| Objektart | Definition | Diskriminator-Test |
|---|---|---|
| **Regulation** | Rechtsakt (CRA, NIS2, DSGVO, MaschVO) | „Ist es ein Gesetz/VO?" |
| **Legal Obligation** | rechtlich verankerte Pflicht. **CORE** (abstrakt, oft = Sicherheitsziel) ⊇ **DOMAIN** (spezialisiert) — die CORE/DOMAIN-Achse existiert bereits (portability). | „Steht das so (sinngemäß) im Recht? Kann ein Prüfer FEHLT/ERFÜLLT sagen?" |
| **Capability** *(NEU)* | implementierbare, **regulierungs-agnostische** technische Funktion, als Einheit baubar & testbar | „Kann ein Hersteller GENAU DAS bauen/konfigurieren?" → ja |
| **Procedure** | wiederholbarer operativer Prozess, der eine Capability ausbringt/erhält (bereits modelliert) | „Ist es eine Tätigkeit/ein Ablauf?" |
| **Control** | testbare Prüfanweisung | „Kann man es prüfen (pass/fail)?" |
| **Evidence** | Nachweis-Artefakt (Audit-Log, SBOM, Release Notes) | „Ist es ein Beleg-Dokument/Datum?" |
| **Guidance** *(quer)* | externe Empfehlung WIE (NIST/OWASP/ENISA/BSI). **Nicht-bindend.** | „Beschreibt es eine empfohlene Umsetzung, kein Primärrecht?" |
---
## 3. DER ZENTRALE KNACKPUNKT: Ist „Security Objective" eine eigene Klasse?
### Modell A — flach (Objektive = Obligations)
```
Regulation → Legal Obligation → Capability → Procedure → Control → Evidence
```
Sicherheitsziele sind einfach **CORE Legal Obligations**; domänen-scoped Pflichten sind DOMAIN-
Obligations, die per `specializes` an die CORE hängen.
### Modell B — mit eigener Security-Objective-Klasse
```
Regulation → Legal Obligation → Security Objective → Capability → Procedure → Control → Evidence
```
### Modell C — hybrid (Capability als einzige neue Klasse) ← **EMPFEHLUNG**
```
Regulation → Legal Obligation (CORE ⊇ DOMAIN) --realized_by--> Capability → Procedure → Control → Evidence
▲ ▲
└── specializes (DOMAIN→CORE) └── described_by ── Guidance (NIST/OWASP/…)
```
**Empfehlung: Modell C.** Begründung aus den Daten:
- Die „Sicherheitsziele" (`software_integrity_protection`, `attack_surface_minimization`, CIA,
access-protection) **SIND im CRA bindende Pflichten** (Annex I (2)(am) ist Primärrecht). Ein
Sicherheitsziel ist also eine **CORE Legal Obligation**, kein neuer Objekttyp.
- Die **CORE/DOMAIN-Achse existiert schon** (portability_core ⊇ health/data_act). `attack_surface_
minimization` (CORE) ⊇ `remote_access_attack_surface_min` (DOMAIN) ist exakt dasselbe Muster.
→ keine neue Klasse, nur konsequente Nutzung des Vorhandenen.
- **Genau EINE** wirklich neue Klasse (**Capability**) ist sparsam und niedrig-risiko.
- Modell B verdoppelt die normative Ebene (Obligation vs Objective), die im CRA 1:1 zusammenfällt
→ Klasse, die niemand sauber befüllt.
**Konsequenz für die #4-Gap:** `software_integrity_protection` + `attack_surface_minimization`
werden als **CORE Legal Obligations** angelegt (nicht als Capabilities), und die domänen-scoped
Treffer (`signed_update_integrity`, `remote_access_attack_surface_min`) `specializes` → CORE.
NIST SI-7/CM-7 mappen dann `primary_implementation` auf die CORE.
**Offen für den User:** Modell C akzeptieren? Oder ist die regulierungs-AGNOSTISCHE Vereinheitlichung
(eine „confidentiality" über CRA+NIS2+ISO) so wertvoll, dass „Security Objective" doch eine eigene
Klasse verdient (Modell B)? Das ist die einzige wirklich offene Architekturentscheidung.
---
## 4. Beziehungstypen — das Modell ist ein GRAPH, keine flache Ebene
Der Review fand **vier distinkte Cross-Domain-Strukturrelationen** (nicht eine):
`SUPPORTED_BY` 23 · `SHARED_CAPABILITY` 16 · `SHARED_EVIDENCE` 7 · `SHARED_PROCEDURE` 5 (+ 1 Merge).
Das ist kein Baum. Vorgeschlagenes gerichtetes Kanten-Vokabular:
| Kante | von → nach | aus Review-Relation |
|---|---|---|
| `specializes` | DOMAIN-Obligation → CORE-Obligation | (SUPPORTED_BY, Spezialfall) |
| `contributes_to` | Obligation → Obligation | (SUPPORTED_BY, Beitrag) |
| `realized_by` | Obligation → Capability | (SHARED_CAPABILITY ⇒ 2 Obl. teilen 1 Capability) |
| `deployed_via` | Capability → Procedure | (SHARED_PROCEDURE) |
| `verified_by` | Procedure/Capability → Control | — |
| `produces` | Procedure → Evidence | (SHARED_EVIDENCE ⇒ 2 Obl. teilen 1 Nachweis) |
| `described_by` | Capability → Guidance | (guidance_basis) |
| `same_as` | Obligation ↔ Obligation | (SAME_OBLIGATION, Merge) |
`SHARED_CAPABILITY`/`SHARED_EVIDENCE`/`SHARED_PROCEDURE` sind also **keine Obligation-Obligation-
Kanten**, sondern Belege, dass zwei Obligations **denselben Knoten einer tieferen Ebene** teilen
(Capability / Evidence / Procedure). Genau das ist der Mehrwert gegenüber „sieht ähnlich aus".
---
## 5. Die 8 offenen Fragen (Antwort + Tradeoff)
1. **Was ist eine Capability?** Eine implementierbare, regulierungs-agnostische technische Funktion,
als Einheit baubar/konfigurierbar/testbar (MFA, TLS, Code Signing, Session-Mgmt, Anomaly-Detection).
2. **Unterschied zur Obligation?** Obligation = rechtliche Pflicht (WAS das Recht verlangt, regulierungs-
verankert, normativ). Capability = technisches Mittel (WIE man sie erfüllt, agnostisch). n:m.
3. **Unterschied zum Security Objective?** Ziel = erwünschter Sicherheitszustand (CIA, attack-surface-min);
Capability = Mittel dorthin. **Empfehlung (Modell C):** das Ziel ist eine CORE Obligation, kein
eigener Typ → Unterschied reduziert sich auf Obligation(abstrakt) vs Capability(Mittel).
4. **Wann Guidance?** Wenn es eine **nicht-bindende externe Empfehlung zur Umsetzung** ist (NIST AC-12,
OWASP ASVS V6). Hängt an der **Capability** (meist) bzw. Procedure — NIE als `legal_basis` einer
LEGAL_MINIMUM-Obligation (Primärrecht-Regel bleibt).
5. **Wann Procedure?** Wenn es ein **wiederholbarer operativer Ablauf** ist, der eine Capability
ausbringt/erhält (MFA konfigurieren, Schlüssel rotieren, Patch-Zyklus fahren).
6. **Capability → mehrere Obligations?** **JA, belegt:** `mfa` erfüllt 6 Obligations (auth+remote),
`code_signing` 2 (auth+updates). n:m.
7. **Obligation → mehrere Capabilities?** **JA, belegt:** access-protection ← mfa + session_management
+ credential_storage. n:m.
8. **Wo hängen NIST/OWASP/ENISA/BSI?** Primär an der **Capability** (sie beschreiben deren Umsetzung),
teils an Procedure. **Das erklärt, warum die über-getierten BP-Obligations `guidance_basis` trugen:
sie waren in Wahrheit Capabilities.** Sauberer Sitz von `guidance_basis` = Capability.
---
## 6. Worked Examples (4 Domänen, echte IDs)
**Authentication** — `user_authentication_required` (Obl, CORE: access-protection)
`--realized_by-->` { `mfa`, `session_management`, `credential_storage` } (Capabilities)
`--described_by-->` NIST IA-2 / OWASP ASVS V6 (Guidance).
**Updates** — `provide_security_updates` (Obl, LEGAL_MINIMUM) `--realized_by-->`
{ `code_signing` (= signed_update_integrity-Capability), `automatic_update_delivery`, `rollback` }
— exakt die `capability_candidate`-Marker aus `cra_updates.json`.
**Remote Access** — CORE `attack_surface_minimization` (NEU, = CM-7-Ziel) `⊇ specializes ⊇`
`remote_access_attack_surface_min` (DOMAIN) `--realized_by-->` { `least_functionality`, `port_disabling` }.
**SBOM** — Sonderfall: die SBOM-Familie ist im Cross-Review der **Evidence-/Procedure-Input** für
`vuln_identification_inventory` (5× SUPPORTED_BY-Hub), weniger Capability. → bestätigt, dass nicht
jede Domäne primär Capabilities beisteuert; manche liefern **Evidence**. Stützt den Graph-Charakter.
---
## 7. Entscheidung, die ich vom User brauche (vor #5b)
1. **Modell C** (Capability = einzige neue Klasse; Sicherheitsziele = CORE-Obligations) — akzeptiert?
Oder Modell B (Security Objective als eigene Klasse für regulierungs-agnostische Vereinheitlichung)?
2. **Kanten-Vokabular** aus §4 — so einfrieren?
3. **`guidance_basis` wandert konzeptionell an die Capability** — einverstanden? (Bricht nichts sofort;
die Obligations behalten den Verweis bis #5b.)
4. Erst danach **#5b**: `capabilities.json` (capability_id, fulfills_obligations[] via `realized_by`,
guidance_basis hochgezogen), die 2 CORE-Gap-Obligations, der Merge (`vuln_remediation_patching`
`provide_security_updates`), und die 2 Remote-Grenzfälle final tiern.
## 8. Bewusst NICHT in #5a (gegated)
Keine `capabilities.json`, keine Migration, kein Obligation-Rewrite, kein Guidance-Move, kein Runtime.
Erst Modell-Annahme, dann Daten. „Erst das Schema, dann verschieben."
@@ -0,0 +1,108 @@
# Compliance Operating System — Meta Model v1.0 (FROZEN)
> **STATUS: EINGEFROREN (2026-06-26). ARCHITEKTUR-FREEZE IN KRAFT.**
> Ab v1.0 dürfen neue Regulierungen das Modell **nicht mehr verändern** — sie müssen sich
> **einfügen**. Das Modell wird nur wieder geöffnet, wenn eine Regulierung **nachweislich
> scheitert** (eine Anforderung lässt sich ohne neue Objektklasse nicht abbilden).
> Validiert gegen 5 Regulierungsarten: DSGVO · CRA · MaschVO · Data Act · NIS2.
Konsolidiert + friert ein: [legal_obligation_layer_v1.md](legal_obligation_layer_v1.md),
[capability_model_v1.md](capability_model_v1.md) (Modell C), [meta_model_validation_v1.md](meta_model_validation_v1.md).
Was hier eingefroren wird, ist **ausschließlich die Meta-Semantik** — NICHT die Registry, NICHT die
Capabilities-Liste, NICHT die Procedures (diese wachsen als Daten weiter).
## 1. Objektklassen (6 + Guidance) — eingefroren
| Klasse | Was | Regulierungs-Bindung |
|---|---|---|
| **Regulation** | Rechtsakt | — |
| **Legal Obligation** | rechtlich verankerte Pflicht; **CORE ⊇ DOMAIN** | regulierungs-anchored |
| **Capability** | implementierbare technische Faehigkeit (OPTIONAL für eine Obligation) | **agnostisch** (n:m über Regulierungen) |
| **Procedure** | wiederholbarer operativer Prozess | agnostisch |
| **Control** | testbare Prüfanweisung | agnostisch |
| **Evidence** | Nachweis-Artefakt | agnostisch |
| **Guidance** *(quer)* | externe nicht-bindende Empfehlung (NIST/OWASP/ISO/BSI) — hängt an der **Capability** | agnostisch |
## 2. Die Kette + kanonisches Kanten-Vokabular — eingefroren
```
Regulation
↓ definiert
Legal Obligation (CORE ⊇ DOMAIN)
↓ realized_by (OPTIONAL — rein prozessuale/dokumentarische Obligations überspringen Capability)
Capability
↓ deployed_via (alias: operationalized_by)
Procedure
↓ verified_by
Control
↓ produces (alias: produces_evidence_for)
Evidence
→ Produktstatus
```
Kanten (gerichtet, eingefroren):
`specializes` (DOMAIN→CORE) · `realized_by` (Obligation→Capability) · `deployed_via` (Capability→Procedure) ·
`verified_by` (Procedure/Capability→Control) · `produces` (Procedure→Evidence) · `described_by` (Capability→Guidance) ·
`supports` / `depends_on` / `contributes_to` (Obligation↔Obligation) · `same_as` (Merge/Alias).
**Das Modell ist ein GRAPH, kein Baum** (n:m an realized_by, supports, produces).
## 3. Attribute (KEINE Klassen) — eingefroren
`applicability` · `tier` (LEGAL_MINIMUM/BEST_PRACTICE) · `legal_basis` (Primärrecht) ·
`guidance_basis` (NIST/OWASP/…, kanonisch an der Capability) · `objective_tags`
(integrity/confidentiality/attack_surface/… — Vorwärts-Kompat zu einer späteren Security-Objective-
Klasse) · `risk_level` · `deadline` · **`hazard` (Attribut, KEINE Klasse)**.
**Watch-Point (bewusste Nicht-Klasse):** `Hazard/Threat` bleibt ein Risiko-Treiber-Attribut. Es wird
*erst dann* eine eigene Klasse, wenn quantitatives Risiko (FMEA: Hazard→Risiko→Maßnahme) als
First-Class-Graph-Knoten modelliert werden soll — das ist die einzige bekannte künftige Öffnungs-Ursache.
## 4. Architektur-Freeze-Policy
1. **Neue Regulierung = Daten, nicht Architektur.** Sie läuft durch `Parser → Discovery-Pipeline →
Review → Registry` und fügt Obligations/Capabilities/Procedures/Evidence hinzu.
2. **Eine neue Objektklasse ist eine Architektur-Änderung** und erfordert explizite Wieder-Öffnung +
Begründung (nachgewiesenes Scheitern der Abbildung). Default-Erwartung: **0 neue Klassen.**
3. Verfeinerungen an Attributen (neues `*_tag`, neues risk-Attribut) sind erlaubt, solange keine
neue Klasse entsteht.
## 5. Reuse-Metrik (KPI je neuer Regulierung) — der Wissens-Akkumulations-Beweis
Für jede neue Regulierung gemessen (Baseline = der jeweils vorhandene Bestand):
| Kennzahl | Soll/Bedeutung |
|---|---|
| **Neue Objektklassen** | **= 0** (Invariante; sonst Freeze gebrochen) |
| Neue Capabilities | additiv (z.B. +8) |
| **Wiederverwendete Capabilities %** | Kern-KPI (z.B. NIS2 ~7080 % erwartet) |
| Wiederverwendete Procedures % | (z.B. 58 %) |
| Wiederverwendete Evidence % | (z.B. 81 %) |
| Neue Obligations | additiv (z.B. +42) |
Zielaussage: *„Beim AI Act: 0 neue Objektklassen, 12 neue Capabilities, 41 neue Obligations,
78 % der vorhandenen Capabilities wiederverwendet."* → belegt, dass das System **Wissen akkumuliert**,
statt je Regulierung neu gebaut zu werden. (Tool zur Berechnung folgt mit dem ersten Live-Durchlauf.)
## 6. Der Burggraben (warum das mehr ist als ein Advisor / RAG)
Der Kunde denkt nicht in Artikeln, sondern: *„Wir haben Remote-Updates / signierte Firmware / einen
Vuln-Prozess."* Über die Capability-Schicht bildet das System diese Aussagen auf **alle betroffenen
Obligations mehrerer Regulierungen** ab und beantwortet die eigentliche Frage aus dem Kundengespräch:
> **„Habe ich das Gesetz richtig verstanden, und reicht das, was wir umgesetzt haben?"**
Das ist regel-/wissensgestütztes Reasoning über ein gemeinsames Modell — keine RAG-Aufgabe.
(Die Reasoning-Session hält dabei die Welt-Grenze: `ClaimCoverage` „potenziell relevant" ⊥
`ComplianceStatus` „erfüllt aus Nachweisen".)
## 7. Was NICHT eingefroren ist (wächst weiter als Daten)
Registry-Inhalte (Obligations je Regulierung), die Capabilities-Liste, Procedures, Evidence-Typen,
Applicability-Prädikate, Citation-Spans. Diese Schicht ist **Wissensaufbau** — explizit erwünschtes
Wachstum gegen das eingefrorene Modell.
## 8. Erster Live-Durchlauf (User-Priorität nach Informationswert)
1. **MaschVO** ⭐⭐⭐⭐⭐ — beweist „Compliance-OS ≠ Cybersecurity" (physische Safety, CE, Restgefahren).
2. **NIS2** ⭐⭐⭐⭐ — misst maximalen Capability-Reuse (erwartet 7080 %).
3. **AI Act** ⭐⭐⭐⭐ — Risikoklassifizierung/Governance, vermutlich 0 neue Klassen.
4. **Data Act** ⭐⭐⭐ — bestätigt „Capability optional".
@@ -0,0 +1,159 @@
# Meta-Model Validation v1 — Ist das Modell regulierungsunabhängig?
Status: **Phase 6 — Meta-Validierung (2026-06-26). KEIN neues Coding, KEINE Regulierung ingestiert.**
Dieses Dokument ist der Stresstest VOR der nächsten Regulierung. Baut auf
[capability_model_v1.md](capability_model_v1.md) (Modell C, #5b materialisiert) +
[legal_obligation_layer_v1.md](legal_obligation_layer_v1.md).
## Die eigentliche Frage
Nicht „welche Regulierung kommt als nächstes?", sondern:
> **Kann eine völlig neue Regulierung in dieses Modell eingeordnet werden, OHNE eine neue
> Objektklasse einzuführen?**
Wenn ja für MaschVO + Data Act + AI Act + NIS2 → das ist kein CRA-Graph mehr, sondern ein
**Compliance Meta Model**. Ab dann bringt jede Regulierung primär *Daten*, nicht *Architektur*.
## Das zu testende Modell (6 Klassen + Attribute, KEINE weitere Klasse erlaubt)
```
Regulation
↓ definiert
Legal Obligation (CORE ⊇ DOMAIN; tier=LEGAL_MINIMUM/BEST_PRACTICE; objective_tags[]; applicability)
↓ realized_by (OPTIONAL)
Capability (regulierungs-agnostische technische Faehigkeit; guidance_basis hier)
↓ deployed_via
Procedure
↓ verified_by
Control
↓ produces
Evidence
```
Quer: **Guidance** (NIST/OWASP/ISO/BSI) hängt an der Capability. **Attribute** (keine Klassen):
`tier`, `objective_tags`, `applicability`, später `deadline`/`risk_level`/`severity`.
Kanten: realized_by · specializes · contributes_to · deployed_via · verified_by · produces · described_by · same_as.
---
## Test 1 — Maschinenverordnung (EU) 2023/1230
| Modell-Klasse | MaschVO-Inhalt |
|---|---|
| Legal Obligation (CORE) | `hazard_minimization` (Sicherheits-Analogon zu attack_surface_minimization), `safe_control_systems`, `machine_risk_assessment`, `ce_conformity`, `instructions_for_use` — exakt die `machine_*`-Obligations, die die Reasoning-Session bereits unabhängig geprägt hat. |
| Capability | **physische Sicherheitsfunktionen**: `emergency_stop`, `safety_interlock`, `two_hand_control`, `guarding`, `safe_torque_off`. → die **Capability-Klasse generalisiert von Cyber auf physische Safety** (gleiche Klasse, andere Domäne). |
| Procedure | Risikobeurteilung (ISO 12100), CE-Konformitätsbewertung. |
| Evidence | Technische Unterlagen, Risikobeurteilungsbericht, Konformitätserklärung. |
**Stress-Punkt:** „**Hazard**" (mechanisch/elektrisch/thermisch) = Schadensquelle — weder Obligation
noch Capability. Kandidat für eine neue Klasse? → **Nein, für die Repräsentation:** ein Hazard ist
ein *Risiko-Treiber* (Attribut/Applicability der Risikobeurteilungs-Procedure); eine Capability
*mitigiert* einen Hazard, genau wie eine Cyber-Capability eine (implizite) Bedrohung kontert. `PL/SIL`
= Attribut (wie `tier`). **Hazard wird erst dann eine Klasse, wenn ihr quantitatives FMEA-Risiko als
First-Class-Graph-Knoten wollt** (vgl. [[project-fmea-safety-direction]]) — nicht für Compliance-Abbildung.
**Verdikt: KEINE neue Klasse.** (Stärkstes Ergebnis: die Capability-Klasse trägt von Cyber zu Safety.)
---
## Test 2 — Data Act (EU) 2023/2854
| Modell-Klasse | Data-Act-Inhalt |
|---|---|
| Legal Obligation | `data_act_data_access_by_design`, `data_act_user_data_access`, `data_portability_switching`, `fair_contract_terms` (FRAND), `interoperability` — deckt sich mit den `data_act_*`-Obligations der Reasoning-Session. |
| Capability | `data_export_api`, `interoperability_interface`, `access_control` (**Reuse**). ABER: `fair_contract_terms` hat **KEINE technische Capability**. |
| Procedure | FRAND-Klauseln entwerfen; Switching-Prozess. |
| Evidence | Vertrag/Klauselwerk, API-Doku. |
**Stress-Punkt:** **vertraglich-rechtliche Pflichten** (FRAND, Verbot unfairer Klauseln) haben kein
technisches Mittel. → Beleg, dass **`realized_by Capability` OPTIONAL ist**: manche Obligations werden
rein über **Procedure (Entwurf) + Evidence (Vertrag)** erfüllt. Das ist KEINE neue Klasse — wir haben
es schon gesehen (SBOM-Familie war Evidence-/Procedure-lastig, kaum Capability).
**Verdikt: KEINE neue Klasse.** Verfeinerung: Capability ist optional (Obligation → Procedure → Evidence
ohne Capability ist gültig).
---
## Test 3 — AI Act (EU) 2024/1689
| Modell-Klasse | AI-Act-Inhalt |
|---|---|
| Legal Obligation | `ai_risk_management_system`, `ai_data_governance`, `ai_technical_documentation`, `ai_transparency_disclosure`, `human_oversight`, `accuracy_robustness`, `fundamental_rights_assessment`. |
| Capability | `event_logging` (**Reuse**!), `bias_detection`, `accuracy_testing`, `human_oversight_mechanism`, `ai_transparency_notice`. |
| Procedure | Risikomanagement-Prozess; FRIA-Durchführung; Human-Oversight-Prozess. |
| Evidence | Technische Dokumentation, FRIA-Bericht, Logs. |
**Stress-Punkt:** **Risiko-Klassifikation** (unacceptable/high/limited/minimal) bestimmt, WELCHE
Obligations gelten. → das ist **Applicability** (existiert bereits; analog zu CRA-Produktklasse).
`human_oversight` = Procedure + Capability (Oversight-UI). `transparency` = Disclosure (Capability/Evidence,
wie Cookie/DSE-Offenlegung). `FRIA` = Procedure + Evidence.
**Verdikt: KEINE neue Klasse.** Verfeinerung: Risiko-Tier = Applicability-Attribut (vorhanden).
---
## Test 4 — NIS2 (EU) 2022/2555
| Modell-Klasse | NIS2-Inhalt |
|---|---|
| Legal Obligation | `nis2_risk_management_measures`, `nis2_incident_reporting`, `supply_chain_security`, `governance_accountability`, `business_continuity`. |
| Capability | **MFA, transport_encryption, security_monitoring_alerting, patch/update, backup****dieselben Capabilities wie CRA**. Das ist die Auszahlung: NIS2-Obligations `realized_by` die bereits gebaute Capability-Schicht. |
| Procedure | Incident-Response-Prozess; Lieferketten-Audit; Governance-Prozess. |
| Evidence | Incident-Reports, Audit-Logs, Vorstandsprotokolle. |
**Stress-Punkt:** **Meldefristen** (24h/72h/1 Monat) = zeitgebundene Procedure → `deadline` = Attribut.
`governance_accountability` (Management-Haftung) = organisatorische Obligation → Procedure + Evidence.
**Verdikt: KEINE neue Klasse.** Stärkster Reuse-Fall (teilt die CRA-Capability-Schicht vollständig).
---
## Ergebnis: 4 × NEIN → das Metamodell steht
Alle vier Regulierungen passen in die 6 Klassen **ohne neue Objektklasse** — unter zwei
Verfeinerungen, die der Test selbst aufdeckt (beide sind KEINE neuen Klassen):
1. **`realized_by Capability` ist OPTIONAL.** Vertraglich/dokumentarisch/prozessuale Obligations
(Data-Act-FRAND, NIS2-Governance, AI-Act-FRIA) werden rein über Procedure + Evidence erfüllt.
2. **Risiko-Niveau / Frist / Hazard-Schwere / Risiko-Tier sind ATTRIBUTE**, keine Klassen
(`tier`-Muster: `deadline`, `risk_level`, `severity`, `risk_tier`).
**Der einzige Watch-Point:** **Hazard / Threat.** Heute implizit (Obligations existieren, um sie zu
kontern). Eine eigene Klasse wird *erst* nötig, wenn ihr **quantitatives Risiko first-class** modelliert
(FMEA: Hazard→Risiko→Maßnahme als Graph-Knoten). Für die reine Compliance-Abbildung: nicht nötig.
→ Das ist die präzise Antwort auf „wo wäre erstmals eine neue Klasse nötig?".
## Empirische Stütze (nicht nur Theorie)
Die 3. Session (Reasoning Engine) hat **unabhängig** `proposed=True`-Obligations für MaschVO
(`machine_*`) und Data Act (`data_act_*`) geprägt — und brauchte dafür **keine neue Objektklasse**,
nur Obligation-IDs. Zwei Sessions kommen unabhängig zum selben Schluss.
## Konsequenz für die Reasoning-Schicht (Produktvision)
Heute: `Product → Applicable Regulations → Applicable Obligations`.
Mit der Capability-Schicht wird daraus:
```
Applicable Capabilities → Required Procedures → Expected Evidence
```
Antwort auf die Kundenaussage „Ich habe X umgesetzt" ist dann nicht „CRA Artikel …", sondern:
```
✓ Capability A ✓ Capability B ✗ Capability C
erfüllt CRA, MaschVO, NIS2 (teilweise)
```
Eine Capability erfüllt Obligations über *mehrere Regulierungen* (n:m) → eine Umsetzung wird gegen
das gesamte Regelwerk bewertet. Das ist qualitativ ein anderes Produkt als RAG.
## Entscheidung / nächster Schritt
Wenn dieses Dokument akzeptiert ist („keine weitere Klasse nötig"), verschiebt sich die Arbeit von
**Architektur** zu **Wissensaufbau**: jede neue Regulierung läuft durch
`Parser → Discovery-Pipeline → Review → Registry` (vorhandene Tooling), statt das Modell zu ändern.
Offen für den User: (a) Metamodell als stabil einfrieren? (b) den Hazard/Threat-Watch-Point als
bewusste Nicht-Klasse dokumentieren (bis FMEA-Quantifizierung)? (c) dann erste Regulierung als DATEN.
## Bewusst NICHT in diesem Schritt
Kein Code, keine Regulierung ingestiert, keine neue Klasse angelegt. Reiner Modell-Stresstest.
@@ -0,0 +1,150 @@
# Session Ownership Model v1 — Arbeitsteilung nach Modell-Besitz
Status: **Vorschlag/Vertrag (2026-06-26).** Antwort auf „Wie verteilen wir die Arbeit?":
**nach BESITZ der Datenmodelle, NICHT nach Regulierung.** Ergänzt
[compliance_meta_model_v1.md](compliance_meta_model_v1.md) (Architektur-Freeze v1.0).
## Leitregel
> **Jede Session besitzt genau EIN Datenmodell. Andere Sessions dürfen es LESEN, nie SCHREIBEN.**
Verteilung nach Regulierung wäre instabil (jede Reg. zieht durch alle Schichten). Verteilung nach
Modell-Besitz ist stabil: drei Wissenswelten, die BreakPilot zusammenführt — **Recht · Produkt · Compliance**.
## Die drei Domänen
### Domäne 1 — Legal Knowledge Graph („Was steht im Recht?")
Besitzt: Dokumente · Parser · CELLAR · Chunk/Span · **citation_span** · Authority · `source_class` ·
`source_role` · Explainability · Retriever.
Kennt NICHT: Capabilities, Procedures, Produktfeatures.
Liefert: `citation_span → legal_basis → authority`.
### Domäne 2 — Compliance Execution Graph („Wie wird eine Pflicht erfüllt?")
Besitzt: **Obligation Registry · Capability Registry · Procedures · Controls · Evidence** ·
Discovery-Pipeline · Reuse-Metrik · Cross-Regulation · Runtime (obligation-status).
Kennt NICHT: Dokumente/Parser/Spans, Produktfeatures.
Modell: `Obligation → Capability → Procedure → Control → Evidence` (Meta-Model v1.0, eingefroren).
### Domäne 3 — Product Knowledge Graph („Was hat der Kunde gebaut?")
Besitzt: Produktmodell · Komponenten · **Business Features** · **Feature → Capability** ·
Product Profile (`CanonicalProductRegulatoryProfile`) · Scope Discovery · Missing-Facts (Navigator).
Kennt NICHT: Paragraphen, Controls.
Beispiel-Features: SPS · HMI · Cloud · MQTT · OPC-UA · Fernwartung · VPN · WLAN · Ethernet ·
Bluetooth · USB · Kamera · KI · Mobile App · OTA · Sensorik · Aktorik.
## Die drei öffentlichen Verträge (die EINZIGE Kopplung)
```
1. Legal → Compliance citation_span → legal_basis (Recht hängt an der Obligation)
2. Product → Compliance Business Feature → Capability ← WICHTIGSTE Schnittstelle des Systems
3. Compliance → Legal obligation_id → legal_basis (jede Pflicht ist begründbar)
```
**Vertrag 2 (`Feature → Capability`) ist die Innovation.** Er macht aus Kundensprache Regulierungs-
sprache: „Wir haben Fernwartung" → Capabilities {transport_encryption, multi_factor_authentication,
least_privilege_access_control} → Obligations über CRA + MaschVO + NIS2 → fehlende Nachweise.
**Owner des Mappings: Domäne 3** (liest die Capability Registry von Domäne 2 read-only).
## Der vollständige Fluss (das Kundengespräch)
```
Produktbeschreibung → Product Graph → Capabilities → Compliance Graph → Legal Graph → Antwort
```
beantwortet: „Wir bauen diese Maschine mit diesen Funktionen — welche Gesetze gelten, was erfüllen
wir, was fehlt, wo interpretieren wir falsch?"
## Mapping auf aktuelle Branches + OFFENE FRAGEN (User/Team entscheidet)
| Domäne | Kandidat-Branch heute | Klärungsbedarf |
|---|---|---|
| 1 Legal Knowledge | (Re-Ingest/Span-Arbeit — Owner benennen) | **Wer besitzt Parser/CELLAR/Span?** noch nicht eindeutig einer Branch zugeordnet |
| 2 Compliance Execution | `feat/obligation-aggregation` (Registry/Capability/Discovery) **+** `feat/advisor-status` (Controls/Evidence/Endpoint) | **Domäne 2 liegt aktuell auf ZWEI Branches** → zusammenführen oder klare Subteilung |
| 3 Product Knowledge | `feat/regulatory-reasoning-engine` (Reasoning **→ umfokussieren** auf Product Graph) | Reasoning besitzt schon `CanonicalProductRegulatoryProfile` + Navigator → wird Domäne 3 |
| — | `feat/iace-gt-warewashing` (IACE Hazard-Engine-Qualität) | **4. Session existiert.** User-Prinzip „keine 4. Session" → IACE als Sub-Track von Domäne 2 (Hazard→Obligation) einordnen ODER bewusst separater Engine-Quality-Track |
## Erste Aufgaben je Domäne
- **Domäne 1:** Re-Ingest fertig · Span-Anker stabil · `obligation_id` im Legal Graph joinbar (über
Vertrag, NICHT selbst vergeben) · zitierfähige API.
- **Domäne 2:** Capability Registry ausbauen · Procedure Registry erweitern · Runtime auf Capability-
Ebene · `Obligation↔Capability↔Procedure↔Evidence` stabilisieren.
- **Domäne 3 (wichtigster neuer Block):** Feature-Katalog (~150300 Features Maschinen-/Anlagenbau) ·
`Feature → Capability` kuratieren · Produktprofil ableiten · Missing-Facts-Engine.
## Nicht jetzt
NIS2/AI-Act/Data-Act-Runs verschoben (liefern Reuse-Kennzahlen, aber keine neue Produktfrage). KEINE
weitere Datenmodell-Klasse (Freeze v1.0). Product Knowledge Graph hat Vorrang — er schließt die Lücke
zwischen Kunden- und Regulierungssprache.
## RESOLVED (2026-06-26, User-Entscheidung) — die 3 offenen Fragen geklärt, Vertrag final
1. **Legal Knowledge Owner = die Re-Ingest-/Knowledge-Session.** Besitzt Parser/CELLAR/Span/Authority/
Retrieval/Citation-API. **Vergibt KEINE `obligation_id`** — liefert nur `citation_span → legal_basis`;
die `obligation_id` entsteht im Compliance-Graph. Verhindert, dass dieselbe Pflicht zweimal modelliert wird.
2. **4. Session NICHT auflösen → umbenennen in „Quality & Validation".** Besitzt KEINE Daten/Registry —
NUR Tests: Golden/Regression/Precision/Recall/Halluzination/Benchmark/Hazard-Qualität/FMEA-Validierung.
Darf produktive Modelle NIE verändern; sagt nur „funktioniert / funktioniert nicht". → **4 Verantwort-
lichkeiten:** Legal *liefert* Wissen · Compliance *modelliert* Wissen · Product *liefert* Kontext ·
Quality *prüft* alles.
3. **Compliance Execution bleibt 2 Branches (dauerhaft getrennt, NICHT mergen):**
- **Branch A** (`feat/obligation-aggregation`) = **BUILD**: Registry · Discovery · Ontology · Capabilities ·
Procedures · Graph (ändert sich ~wöchentlich).
- **Branch B** (`feat/advisor-status`) = **RUNTIME / Execution Engine**: API · Advisor · Endpoint · Status ·
Evidence · Reasoning (ändert sich ~täglich).
Unterschiedliche Geschwindigkeit → bewusst getrennt.
**Plattform-Zielbild: 4 Bibliotheken** — `Legal Library → Product Library → Capability Library →
Evidence Library`; darauf sitzen Advisor · Runtime · Auditor · Ticket-System · CE-/CRA-/NIS2-/AI-Act-
Assistent — **alle auf derselben Wissensbasis**. Die **Capability Library/Registry ist der Dreh- und
Angelpunkt** zwischen Product- und Compliance-Graph → muss ein **stabiler, versionierter API-Vertrag**
sein (stabile `cap.*`-IDs, nie umbenennen; produktneutral). Das ist #59.
## Update (2026-06-26): Domäne 3 = FEATURE Knowledge Graph + Sequenz-Entscheidung
**Rename Domäne 3 → „Feature Knowledge Graph".** Kunden kaufen keine Capabilities/Obligations — sie
kaufen Maschinen mit **Fernwartung, Cloud, OTA, SPS, HMI, KI**. Der Advisor MUSS dort beginnen, wo der
Kunde steht (`Fernwartung`), nicht bei `cap.transport_encryption`. Domäne 3 besitzt zusätzlich die
**Feature Library** (alle bekannten ~200400 Features: Fernwartung/Cloud/OTA/VPN/WLAN/Bluetooth/USB/
Ethernet/OPC-UA/MQTT/CAN/Profinet/EtherCAT/SPS/Safety-SPS/HMI/Vision/Kamera/RFID/NFC/Mobile-App/REST-API/
Webserver/SSH/Benutzerverwaltung/Rollenmodell/Logging/KI/…). **Feature Library ≠ Product Profile:**
Library = alle bekannten Features; Profile = die Features EINES konkreten Produkts.
**Volle Pipeline (der eigentliche Advisor):**
```
Feature Library → Product Profile → Capabilities → Legal Obligations → Procedures → Controls → Evidence
```
„Fernwartung + Cloud + VPN + OTA + Benutzerverwaltung" → N Capabilities → M Obligations → K
Regulierungen → Procedures → Controls → Evidence. Das beginnt das Gespräch in Kundensprache.
**Sequenz-Entscheidung (User 2026-06-26):**
1. **JETZT:** `cap.*`-Vertrag (capability_registry_v1) an Domäne 3 übergeben = der Multiplikator.
2. **Domäne 3 Vollgas:** Feature Library + Komponenten + **`Feature → cap.*`** + Product Profile +
Missing-Facts. Zuerst OHNE Regulierungen — reine Kundensprache.
3. **Domäne 2 STOPP bei #59:** Capability Registry bleibt STABIL (nur Bugfixes, KEINE neuen Capabilities/
Procedures), bis Domäne 3 zeigt, WELCHE Capabilities real gebraucht werden (sonst modelliert man 30,
von denen 12 genutzt werden).
4. **Domäne 1:** Re-Ingest abschließen, Span-Anker, Citation-API stabilisieren.
### Domäne 2 — Wake-up-Trigger (statt vagem „pausiert")
Domäne 2 ruht NICHT unbestimmt — sie wird wieder aktiv, sobald EINER dieser Trigger feuert:
```
Feature Graph (Domäne 3) >= 200 Features → Feature Coverage Report (erster Auftrag, s.u.)
ODER Span-Anker verfügbar (Domäne 1) → pending_span_anchor auflösen (citation_pending → echte Spans)
ODER neue Regulierung ingestiert → Discovery-Cut + Reuse-Metrik
ODER Runtime (Branch B) kennt neue Evidence-Typen → required_procedures/evidence_patterns endgültig füllen
```
Bis dahin steht überall `citation_pending` / `required_procedures: []` — bewusst, kein Defekt.
### Erster Folgeauftrag von Domäne 2 (sobald Feature Library v1 steht): FEATURE COVERAGE REPORT
NICHT „neue CRA-Domäne". Sondern die **Wissenslücken-Analyse**, die diese Architektur erstmals ermöglicht:
pro Feature die Kette `Feature → cap.* → realizes_obligations → Procedures → Evidence` traversieren und
**Coverage % je Feature** berechnen — wie vollständig ist die Modellierungskette?
```
Fernwartung → 100 % · USB → 94 % · Bluetooth → 83 % · Cloud → 71 %
```
Output: je Feature die Lücken — fehlende Capability · fehlende Procedure · fehlender Evidence-Typ.
Zeigt sofort, was schon vollständig modelliert ist und wo Domäne 2 als Nächstes nacharbeiten muss.
Traversal-Logik gehört Domäne 2 (cap.*→Obligation→Procedure→Evidence); der Feature→cap.*-Input kommt
read-only von Domäne 3. Gated auf Feature Library v1.
+289
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@@ -0,0 +1,289 @@
{
"schema_version": "capability_registry_v1",
"contract_version": "1.0",
"status": "stable_api_contract",
"note": "PRODUKTNEUTRALER Vertrag zwischen Product Knowledge Graph (Domaene 3, Feature->Capability) und Compliance Execution Graph (Domaene 2). Stabile cap.*-IDs NIE umbenennen. KEINE Business-Features hier (die besitzt die Product-Session). Siehe docs-src/development/session_ownership_model_v1.md + compliance_meta_model_v1.md (Freeze v1.0).",
"id_namespace": "cap.",
"contract_fields": [
"id",
"name",
"description",
"guidance_basis",
"realizes_obligations",
"required_procedures",
"evidence_patterns",
"domains"
],
"dropped": {
"access_control": "OVERLAP (credential_confidentiality <-> sbom_confidentiality), nicht materialisiert"
},
"candidate_capabilities_followup": [
"automatic_update_delivery",
"update_rollback",
"trusted_update_source",
"hash_verification",
"secure_boot",
"least_functionality",
"credential_storage"
],
"capabilities": [
{
"id": "cap.multi_factor_authentication",
"slug": "multi_factor_authentication",
"name": "Multi-Factor Authentication",
"description": "Mehrfaktor-Authentisierung als technische Faehigkeit (Besitz/Wissen/Inhaerenz).",
"guidance_basis": [
{
"source": "NIST",
"anchor": "SP 800-63B",
"role": "best_practice"
},
{
"source": "Out-of-Band-Authentifizierung",
"anchor": "",
"role": "implementation_guidance",
"merged_from": "out_of_band_authentication"
},
{
"source": "Hardware-basierte Authentifizierung (AAL3)",
"anchor": "",
"role": "implementation_guidance",
"merged_from": "hardware_authenticators"
},
{
"source": "E-Mail-Authentifizierungsmechanismen (SPF/DKIM/DMARC)",
"anchor": "",
"role": "implementation_guidance",
"merged_from": "email_authentication"
},
{
"source": "NIST",
"anchor": "IA-02",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "IA-02(1)",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "AC-17",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "SP 800-53 IA-2",
"role": "best_practice"
},
{
"source": "BSI",
"anchor": "ICS Security Kompendium",
"role": "best_practice"
},
{
"source": "ISO",
"anchor": "ISO 27001 A.5.19",
"role": "best_practice"
}
],
"realizes_obligations": [
"mfa_required",
"privileged_op_reauth",
"remote_access_authentication",
"remote_access_mfa",
"remote_access_user_validation_ot",
"supplier_access_auth"
],
"required_procedures": [],
"evidence_patterns": [
"iam_config_export",
"mfa_policy_export",
"auth_audit_log"
],
"domains": [
"authentication",
"remote_access"
],
"provenance": {
"source": "cross_domain_relationships.json SHARED_CAPABILITY"
}
},
{
"id": "cap.session_management",
"slug": "session_management",
"name": "Session Management",
"description": "Sichere Sitzungsverwaltung: Timeouts, Bindung, Re-Auth, Beendigung.",
"guidance_basis": [
{
"source": "NIST",
"anchor": "SP 800-63B 4.3",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "SP 800-53 AC-12",
"role": "best_practice"
},
{
"source": "OWASP",
"anchor": "ASVS V3",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "AC-2(5)",
"role": "best_practice"
}
],
"realizes_obligations": [
"reauth_after_inactivity",
"remote_session_management",
"session_binding_management",
"temporary_remote_access_mgmt"
],
"required_procedures": [],
"evidence_patterns": [
"session_config_export",
"timeout_policy_export"
],
"domains": [
"authentication",
"remote_access"
],
"provenance": {
"source": "cross_domain_relationships.json SHARED_CAPABILITY"
}
},
{
"id": "cap.transport_encryption",
"slug": "transport_encryption",
"name": "Transport Encryption",
"description": "Verschluesselter Transport (TLS, mutual-TLS, Zertifikats-Auth, VPN/Tunnel).",
"guidance_basis": [
{
"source": "BSI",
"anchor": "TR-02102-2",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "IA-03",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "SC-8",
"role": "best_practice"
},
{
"source": "BSI",
"anchor": "IT-Grundschutz NET.3.3",
"role": "best_practice"
},
{
"source": "OWASP",
"anchor": "API Security Top 10",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "IA-05(2)",
"role": "best_practice"
}
],
"realizes_obligations": [
"encrypted_auth_channel",
"mutual_authentication",
"reject_insecure_remote_protocols",
"remote_access_confidentiality_integrity",
"remote_access_encryption",
"service_to_service_auth",
"tls_certificate_auth"
],
"required_procedures": [],
"evidence_patterns": [
"tls_config_export",
"cipher_scan",
"cert_inventory"
],
"domains": [
"authentication",
"remote_access"
],
"provenance": {
"source": "cross_domain_relationships.json SHARED_CAPABILITY"
}
},
{
"id": "cap.code_signing",
"slug": "code_signing",
"name": "Code & Update Signing",
"description": "Digitale Signatur + Integritaets-/Authentizitaetspruefung von Firmware/Software/Updates.",
"guidance_basis": [
{
"source": "NIST",
"anchor": "SI-07",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "SP 800-147 BIOS Protection",
"role": "best_practice"
}
],
"realizes_obligations": [
"firmware_software_authentication",
"signed_update_integrity"
],
"required_procedures": [],
"evidence_patterns": [
"signature_verification_log",
"sbom",
"signing_key_policy"
],
"domains": [
"authentication",
"updates"
],
"provenance": {
"source": "cross_domain_relationships.json SHARED_CAPABILITY"
}
},
{
"id": "cap.security_monitoring_alerting",
"slug": "security_monitoring_alerting",
"name": "Security Monitoring & Alerting",
"description": "Anomalie-/Bedrohungserkennung und Alarmierung aus Logs/Telemetrie.",
"guidance_basis": [
{
"source": "NIST",
"anchor": "AU-6/SI-4",
"role": "best_practice"
},
{
"source": "NIST",
"anchor": "SP 800-94",
"role": "best_practice"
}
],
"realizes_obligations": [
"log_monitoring_alerting",
"remote_access_threat_detection"
],
"required_procedures": [],
"evidence_patterns": [
"siem_config_export",
"alert_rule_export",
"monitoring_audit_log"
],
"domains": [
"logging",
"remote_access"
],
"provenance": {
"source": "cross_domain_relationships.json SHARED_CAPABILITY"
}
}
]
}
@@ -1,11 +1,12 @@
{
"schema_version": "controls_for_obligation_mapping_v1",
"purpose": "Accepted CRA->OWASP controls (Compliance Execution Graph) for the Obligation Registry to propose the SEMANTIC control->obligation_id, replacing the coarse citation_unit interim join. Fill proposed_obligation_id per control, then we adopt it into control_mapping.obligation_id.",
"source": "ai-compliance-sdk control_mappings, mapping_status=accepted, reviewed_by=benjamin 2026-06-25",
"filled_by": "obligation-registry-session 2026-06-25 (alle 7/7: 4 auth/crypto + 3 logging via cra_logging.json)",
"purpose": "Accepted CRA->Framework controls (Compliance Execution Graph) for the Obligation Registry to propose the SEMANTIC control->obligation_id, replacing the coarse citation_unit interim join. Fill proposed_obligation_id per control, then we adopt it into control_mapping.obligation_id.",
"source": "ai-compliance-sdk control_mappings, mapping_status=accepted, reviewed_by=benjamin 2026-06-25. OWASP ASVS (7, gefuellt) + NIST SP 800-53 (3, pending).",
"filled_by": "obligation-registry-session 2026-06-25. OWASP 7/7 (4 auth/crypto + 3 logging). NIST 3/3 GEFUELLT (Obligation-Session): SI-2->provide_security_updates (stark, (2)(c)/Art.13) · SI-7->signed_update_integrity (update-scoped; SI-7 breiter) · CM-7->remote_access_attack_surface_min (remote-scoped; CM-7 breiter). GAP-BEFUND (Cross-Domain-Review): generische Parent-Obligations software_integrity_protection + attack_surface_minimization FEHLEN — SI-7/CM-7 sind breiter als die domaenen-scoped Treffer. Kandidaten fuer neue generische Obligations (User-Entscheidung). Damit 10/10 proposed_obligation_id gefuellt.",
"join_principle": "SEMANTISCH via obligation_id, NICHT via citation_unit/legal_basis-Anker. Die CRA-Anker sind im Registry teils approximativ (siehe anchor_quality_note) — daher ist obligation_id der stabile Primaerschluessel, nicht der Anker.",
"anchor_quality_note": "Registry-legal_basis-Anker sind teils CRA-Part-I-fehlzugeordnet (Opus-Synthese): user_authentication_required steht auf (2)(d) statt (2)(c); Crypto-Obligations auf (2)(e) statt (2)(d). CRA Annex I Part I: (2)(c)=Zugriffsschutz, (2)(d)=Vertraulichkeit, (2)(e)=Integritaet. Korrektur kommt mit dem zitierfaehigen Re-Ingest (span-genau). Deshalb: NICHT auf Anker joinen. ABER: der Logging-Cut (V16.*) ist korrekt auf (2)(k) verankert (echte Logging-Subsektion, kein Fehl-Anker).",
"count": 7,
"mapping_type_note": "NEU: mapping_type=primary_implementation = die kanonische Primaer-Control einer Anforderung (genau eine), staerker als implements/supports. related-Controls (SC-3(3), RA-5, AC-6, SI-16, SA-10, ...) folgen separat als supports. Eine Obligation kann mehrere Controls haben, aber genau einen primary_implementation-Einstieg.",
"count": 10,
"controls": [
{
"framework": "OWASP ASVS", "control": "V6.3.1",
@@ -62,6 +63,30 @@
"proposed_obligation_id": "event_logging_security_events",
"mapping_method": "semantic",
"mapping_note": "V16.1 = allgemeine Logging-Anforderung -> Umbrella-LM event_logging_security_events. Hohe Konfidenz."
},
{
"framework": "NIST SP 800-53", "control": "SI-7",
"source_norm": "CRA Annex I Part I (2)(e) — Integritaet",
"citation_unit": "Annex I (2)(e)", "family": "integrity", "mapping_type": "primary_implementation",
"proposed_obligation_id": "software_integrity_protection",
"mapping_method": "semantic",
"mapping_note": "NIST SI-7 = Software/Firmware/Information Integrity (gesamte Produkt-Integritaet). #6 ADOPTIERT (2026-06-26) auf CORE software_integrity_protection (Annex I (2)(f)) — die in #5b materialisierte generische Integritaets-Obligation. Die domaenen-scoped signed_update_integrity (Update-Signatur, (1)(3)(f)) bleibt gueltig als DOMAIN, specializes->CORE. NICHT log_integrity_immutability (Audit-Log-Schutz, andere Ebene)."
},
{
"framework": "NIST SP 800-53", "control": "SI-2",
"source_norm": "CRA Annex I Part I (2)(l) — Sichere Updates",
"citation_unit": "Annex I (2)(l)", "family": "update", "mapping_type": "primary_implementation",
"proposed_obligation_id": "provide_security_updates",
"mapping_method": "semantic",
"mapping_note": "NIST SI-2 = Flaw Remediation. STARKER Treffer in eurer NEUEN updates-Familie (93-Stand): provide_security_updates (LEGAL_MINIMUM, Annex I (2)(c) + Art. 13) = DAS sichere-Update-LM. -> SI-2 primary_implementation = provide_security_updates. Verwandt (supports): vuln_remediation_patching (Part II Remediations-PROZESS), support_period_maintenance, update_testing_validation, update_rollback. Mein source_norm-Anker (2)(l) ist approximativ -> bitte (2)(c)/Art.13 via provide_security_updates nutzen."
},
{
"framework": "NIST SP 800-53", "control": "CM-7",
"source_norm": "CRA Annex I Part I (2)(i) — Angriffsflaeche minimieren",
"citation_unit": "Annex I (2)(i)", "family": "attack_surface", "mapping_type": "primary_implementation",
"proposed_obligation_id": "attack_surface_minimization",
"mapping_method": "semantic",
"mapping_note": "NIST CM-7 = Least Functionality (deaktivierte Ports/Dienste/Funktionen, GESAMTE Angriffsflaeche). #6 ADOPTIERT (2026-06-26) auf CORE attack_surface_minimization (Annex I (2)(j)) — die in #5b materialisierte generische Obligation. Die domaenen-scoped remote_access_attack_surface_min (nur Remote-Access-Flaeche) bleibt gueltig als DOMAIN, specializes->CORE. related (supports): SC-3(3)/AC-6/SI-16."
}
]
}
+4 -1
View File
@@ -966,7 +966,10 @@
"relationships": [],
"citation_anchor_ids": [],
"citation_status": "pending_span_anchor",
"review_status": "draft"
"review_status": "draft",
"merged_into": "provide_security_updates",
"status": "deprecated_alias",
"merge_note": "SAME_OBLIGATION (Cross-Domain-Review). Kanonisch: provide_security_updates ((2)(c)/Art.13). ID bleibt als Alias aufloesbar; downstream provide_security_updates nutzen."
},
{
"id": "vuln_handling_process",
+5 -1
View File
@@ -10281,7 +10281,11 @@
"cluster_size": 4,
"llm_model": "claude-opus-4-8",
"synthesis_version": "v1"
}
},
"specializes": "software_integrity_protection",
"objective_tags": [
"integrity"
]
}
],
"relationships": [
+82
View File
@@ -0,0 +1,82 @@
{
"schema_version": "obligation_registry_v1",
"regulation": "CRA",
"regulation_code": "CRA",
"family": "core",
"theme": "CORE Security Objectives (CRA Annex I als regulierungs-agnostische Sicherheitsziele)",
"generated_by": "materialize_capabilities.py (#5b, Modell C)",
"note": "CORE Legal Obligations = Sicherheitsziele (Modell C: KEINE eigene SecurityObjective-Klasse). DOMAIN-Obligations specializes-en hierauf. objective_tags = Vorwaerts-Kompat zu Modell B.",
"citation_status": "pending_span_anchor",
"obligations": [
{
"id": "attack_surface_minimization",
"name": "Minimierung der Angriffsflaeche",
"family": "core",
"description": "Das Produkt minimiert seine Angriffsflaeche: unnoetige Funktionen/Ports/Dienste/Schnittstellen sind deaktiviert (Least Functionality).",
"tier": "LEGAL_MINIMUM",
"source_role": "LEGAL_BASIS",
"applicability": "universal",
"objective_tags": [
"attack_surface"
],
"legal_basis": [
{
"source": "CRA",
"anchor": "Annex I Part I (2)(j)",
"citation": "limit attack surfaces, including external interfaces"
}
],
"guidance_basis": [
{
"source": "NIST",
"anchor": "CM-7 Least Functionality",
"role": "best_practice"
}
],
"specialized_by": [
"remote_access_attack_surface_min",
"component_remote_interface_security"
],
"primary_implementation": "NIST CM-7",
"citation_status": "pending_span_anchor",
"review_status": "core_from_5b"
},
{
"id": "software_integrity_protection",
"name": "Schutz der Software-/Firmware-Integritaet",
"family": "core",
"description": "Das Produkt schuetzt Integritaet und Authentizitaet von Software/Firmware (Manipulationserkennung, Secure Boot, Signaturpruefung, Runtime-Integritaet).",
"tier": "LEGAL_MINIMUM",
"source_role": "LEGAL_BASIS",
"applicability": "universal",
"objective_tags": [
"integrity"
],
"legal_basis": [
{
"source": "CRA",
"anchor": "Annex I Part I (2)(f)",
"citation": "protect the integrity of stored, transmitted or processed data, software and configuration"
}
],
"guidance_basis": [
{
"source": "NIST",
"anchor": "SI-7 Software, Firmware, and Information Integrity",
"role": "best_practice"
}
],
"specialized_by": [
"signed_update_integrity",
"firmware_software_authentication"
],
"realized_by_capabilities": [
"code_signing"
],
"primary_implementation": "NIST SI-7",
"citation_status": "pending_span_anchor",
"review_status": "core_from_5b"
}
],
"relationships": []
}
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