d1383227b29efeabb5f8cab533bfd8dd677601e8
586 Commits
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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> |
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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>
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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> |
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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> |
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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> |
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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>
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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> |
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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>
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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>
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e1b270c36e |
Add obligation discovery pipeline tooling
Sichert die validierte Obligation Discovery Pipeline aus /tmp als dauerhaftes, committetes Tooling (scripts/obligation_discovery/) — der eigentliche Vermögenswert. Stufen: precluster (Embedding-Cache + Mikro-Cluster) → meta_cluster (Review Units, Skalierungs-Fix) → synthesize_obligations (Opus, Key aus ENV, Streaming, harte Tier-Regel, Provenance) → validate_registry → merge_review_diff. Reine Helfer in _core.py, 16 Unit-Tests. Doku docs-src/development/obligation_discovery_pipeline_v1.md mit Meilensteinen (SBOM/Vuln reproduziert, Auth 4408→170 Review Units→54→kuriert 29) und der Architekturregel: Runtime deterministisch, Discovery LLM-gestützt. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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c1ea9458a7 |
Add met_count and recall_limited_obligations to shadow telemetry
Reichert die Obligation-Shadow-Telemetrie um zwei Felder an für die Cross-Firmen- Auswertung: met_count (abgedeckte Obligations) + recall_limited_obligations (welche Obligations recall-limitiert sind) — erlaubt die Konzentrations-Analyse über Firmen. 7-Firmen-Shadow: 136 Control-Findings → 29 Obligation-Findings (4,7×); recall_limited nur 6/29, konzentriert auf third_country/safeguards in 2/7 Firmen → LLM-Fix bounded. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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0631a98bdd |
Mark recall-limited obligations in DSE shadow telemetry
Trennt im Shadow drei Kategorien statt eines pauschalen FAILED: - echte Lücke (failed_by_current_checker) - redundanter Control-FP (kollabiert per OR zu MET) - Prüfer-Reichweitenproblem (recall_limited) obligation_taxonomy.py: decision_method_required=LLM für recipients_disclosed, third_country_transfer_disclosed, safeguards_disclosed, safeguards_accessible (versioniertes Registry-Artefakt bis DB-Tabelle, v1-Spec). Empirisch: TeamViewer 0/22 kw+emb trotz erfüllter Pflicht (cos 0.49-0.57) → CONTENT/LLM-Klasse, kein Schwellen-Fix. compute_obligation_shadow segregiert FAILED/PARTIAL über requires_llm(): teamviewer 5 Findings → 2 echte + 3 recall_limited. 9 neue Unit-Tests (41 gesamt grün). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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c3542f7dfe |
feat(dse): obligation shadow telemetry
Verdrahtet die Obligation Aggregation Engine als Layer 4 (SHADOW) in v3_engine: erzeugt aus den results zusätzlich Obligation-Ergebnisse AUSSCHLIESSLICH für die Telemetrie. Greift NICHT in results ein — nutzer-sichtbare Findings unverändert. - _obligation_shadow.py: fetch_obligation_markers (legal_obligations + applicability) + compute_obligation_shadow (pure): legacy_control_findings, obligation_shadow_results, collapse_factor, na_count, met_failed_delta, top_collapsed_obligations - met-Signal = Legacy-passed (kein zusätzlicher Prüfer-Call/Key) E2E (3 Firmen, echte Engine): 57 Control-Findings → 14 Obligation-Findings (4,1×); Redundanz kollabiert wo Evidenz existiert, echte Lücken bleiben FAILED. 6 Unit-Tests grün. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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7ec29999a2 |
feat(obligation): obligation applicability predicates
Minimaler Applicability-Hook für die Obligation Aggregation Engine: entscheidet aus dem Dokumenttext, ob eine bedingte Obligation anwendbar ist (True/False/None). - has_third_country_transfer · uses_legitimate_interest · direct_marketing (+ Alias legitimate_interest_or_public_task) - unbekanntes Prädikat → None → Aufrufer behält Default=anwendbar (fail-safe, nie stille NA) - profiling/employment/telecom/health/data_act folgen als nächste Charge Re-Benchmark (Opus-GT, 3 Firmen): Prädikate erkennen Transfer/berecht.Interesse/ Direktwerbung korrekt → keine falsche NA; NA-Flip-Probe bestätigt FEHLT→NA ohne Transfer. 14 Unit-Tests grün. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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402a42d30d |
feat(obligation): obligation-level aggregation engine
Erste Ausführung des Legal Obligation Layer v1: aggregiert Bewertungen auf Kriterium-/Control-Ebene zu Findings auf Obligation-Ebene (Regulation → Legal Obligation → Control → Criterion). - regulierungs-agnostisch (obligation_id/tier/met/legal_basis/conditional) - fail-safe: LM applicable=false→NA · keine erfüllt→FAILED · alle→MET · Teil→PARTIAL; BP/OPT covered→MET sonst OPEN (nie FAILED); LM unbewertbar→UNDETERMINED (Legacy behalten) - Redundanz-Kollaps per OR pro legal_basis-Anforderung → kein künstliches PARTIAL - Applicability als Hook (Prädikat-Engine folgt separat) Shadow-Benchmark (Opus-GT, 3 Firmen): 38 Control-Findings → 13 Obligation-Findings (2,9×); ~23 redundante Falsch-Positive strukturell korrigiert, echte Lücken erhalten, PARTIAL=0. 16/16 Unit-Tests grün. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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067118b12d |
fix(cascade): give OVH/gpt-oss reasoning headroom so Tier-2 isn't silently dead
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gpt-oss-120b is a reasoning model: it spends output tokens on chain-of-thought before the answer. deep_check called _call_ovh with max_tokens=400, which length-capped it mid-reasoning -> content=null -> the OVH tier returned nothing and the cascade always skipped Tier-2. Floor the OVH budget to >=2000, fall back to reasoning_content when content is null, and raise the client timeout to 90s for the slower reasoning path. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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5ff08a240b |
feat(dse): tiered 3-state evaluator + Layer-3 wiring (compliance_tier)
Getierte Auswertung mit compliance_tier-Gating (nur LEGAL_MINIMUM bestimmt ERFÜLLT/TEILWEISE/FEHLT; BEST_PRACTICE/OPTIONAL → Empfehlungen). Deterministisch- first: EMBEDDING-Präsenz + gecachter Haiku nur für Sufficiency → reproduzierbar (löst die gemessene Judge-Varianz). Layer-3 in v3_engine gated auf tiered_criteria, fail-safe (UNBESTIMMT → Legacy). Offene Kalibrierung: Präsenz-Schwelle (Schritt 2). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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3e3644f83d |
feat(checkers): platform router + Haiku sufficiency tier; cookie is first consumer
Generalise "Embedding finds, Claude decides" into the shared Pruefer-Library: - router.route_and_check dispatches control -> sensor_classification -> Checker. - build_spec reads sensor_classification (CONTENT/LLM -> judge=haiku, the validated sufficiency tier; the Qwen-first cascade is disproven for sufficiency). - LLMChecker gains a Haiku-direct tier (reuses the validated deep_check prompt). - Cookie Layer-3 now routes through route_and_check instead of bespoke code, so cookie is the first real router consumer -- proves the architecture end-to-end. Reproduces the validated result via the shared path: FN 159->14, recall 0.13->0.92, precision 0.89 (vs bespoke 12/0.93/0.90 -- within Haiku noise). Tests: 10/10 (router dispatch + build_spec + haiku tier + cookie rewire). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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e809d0bc1c |
feat(cookie): Layer-3 sufficiency-judge — Haiku re-judges embedding/boost rescues
The embedding/boost auto-rescue is intentionally optimistic (finds the topic, not fulfilment) -> 159 FN over-rescues vs Opus-GT (recall 0.13). Layer-3 re-judges exactly the rescued passes with the validated Haiku judge (cohort cookie_sufficiency_v1 P0.89/R0.91) -- NOT the Qwen-first cascade (local is disproven as a sufficiency judge) -- and un-passes them when the obligation is not concretely met. Gated to the full check (not skip_llm). Measured (5-firm Opus-GT, engine+L3): FN 159->12, recall 0.13->0.93, precision 0.96->0.90 (276 rescues corrected). "Embedding finds, Claude decides." Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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869e7aeb1e |
fix(cookie): gate non-COOKIE_POLICY controls out of the cookie-policy scan
The cookie agent loaded 100 controls, 11 of which have no COOKIE_POLICY in applicable_artifacts -- Security/TOM/Audit (PROCESS) or Banner-behaviour (BEHAVIOR) controls that produce nonsense findings against a cookie policy (e.g. "TOMs not documented"). Add a cookie classification gate (analogous to the DSE gate, keyed on COOKIE_POLICY, without the needs_review carve-out since the artifact signal is decisive and the set is inventory-verified). Controls are routed out, not deleted. Effect vs Opus-GT: FP 16->11, FN 179->159; the remaining FN=159 over-rescue is a separate (judge/criteria) question, not routing. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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38a347a82a |
feat(platform): live-wire AGB v2 + DSE v3 + Architektur-Tab (#29)
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AGB v2 (decision_method routing, 71%FP->~0) + DSE v3 (4-layer, recovered from container) + Architektur-Tab into /sdk/agent live path. Incl CI robustness (detect-changes.sh + PR-head checkout) + security (hardcoded Qdrant key removed, gitleaks allowlist). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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76d1dc5e00 |
fix(db): dedupe doc_check_controls 3x + unique constraint
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compliance.doc_check_controls war auf prod historisch trippliziert (Dump-Artefakt ohne PK/Unique: jede (doc_type, control_id)-Zeile 3x, 5622 statt 1874 ueber alle 8 doc_types). Die Migration dedupt idempotent (kleinste ctid behalten) und setzt UNIQUE(doc_type, control_id), damit sich die Triplikation nicht wiederholen kann. Auf prod bereits direkt angewandt und in _migration_history registriert (read-only verifiziert: 1874, alle doc_types total=distinct, Constraint aktiv); dieser Commit codifiziert die Migration in der Deploy-Kette, damit ein Restore aus einem aelteren Dump sie automatisch re-appliziert. [migration-approved] Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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43e02f794a |
feat(cra): SBOM- + DAST-Findings aus dem Scanner-MCP konsumieren
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Sharangs compliance-scanner-agent exponiert SBOM (sbom_vuln_report) + DAST (list_dast_findings) als eigene MCP-Tools (nicht via list_findings). Neuer fetch_all_findings(repo_id) zieht list_findings + SBOM + DAST in EINER MCP-Session und normalisiert ins Finding-Schema: - SBOM: ein Finding pro verwundbarem Paket (nicht pro CVE), cwe=CWE-1395 -> deterministisch CRA-AI-22 (robust gegen Paketnamen wie "sqlite"). - DAST: cwe/endpoint/vuln_type uebernommen -> Mapping via cwe/keywords. assess-from-scanner nutzt fetch_all_findings + liefert source.breakdown (code/sbom/dast). DAST hat im MCP keinen repo_id-Filter -> dast_repo_scoped:false (deployment-weit, transparent geflaggt). Echte MCP-Daten: Kitchenasty 58 code + 35 sbom + 81 dast -> 174 gemappt (Coverage 94,3%, alle 35 SBOM -> CRA-AI-22). Enthaelt zusaetzlich das Qdrant->Prod-Kopierскript (#42, verbatim macmini->prod). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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8f21650d74 |
feat(sdk): Kunden-Dokumente + CRA-Meldewesen, Screening aus Frontend genommen
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- /sdk/dokumente: Kundensicht nur auf veroeffentlichte Rechtsdokumente (Ansehen + Download); Proxy mit Allow-List nur /public — Templates/Drafts/ Generator bleiben unerreichbar. - /sdk/cra-meldewesen: CRA Art. 14 Meldewesen (24h/72h/14d-Kaskade) mit Fristen-Tracking + ENISA-SRP-Export-Entwurf (kein Live-API). Backend: cra_meldewesen (pure, getestet) + cra_incident_store (schema-neutral ueber compliance_cra_documents) + /api/v1/cra/incidents (additiv, contract-safe). - Screening (Self-Scan) aus dem Frontend genommen: Flow-Stepper-Eintrag ausgeblendet (visibleWhen), Dashboard-Kachel + Import-Button entfernt. Repo-Scanning laeuft extern im Compliance-Scanner; Backend-Router bleibt vorerst gemountet (Contract-Stabilitaet). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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72093e5501 |
fix(cra): Scanner-Findings vollstaendig mappen + assess-from-scanner-Latenz senken
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Punkt 2 (Coverage): semgrep/gdpr-Findings ohne CWE blieben unmapped (~21%). Der Mapper nutzt jetzt den scanner rule_id + gezielte Keywords (gdpr -> Datenminimierung CRA-AI-17, path-traversal/prototype-pollution -> CRA-AI-20, nginx-header/Docker-Hardening -> CRA-AI-1/4, insecure-websocket -> CRA-AI-15). Reale Scanner-Daten: unmapped 19/92 -> 0/92 (Coverage 100%). Punkt 3 (Latenz): enrich_findings_with_breadth lief ~6 Aggregat-Queries je (use_case,sub_topic)-Paar, nutzte aber nur die Liste. Jetzt EINE batched Query (breadth_controls_batch) fuer alle Paare + Prozess-Cache (TTL 1800s). macmini: cold 0,23s / warm 0,000s. Prod-Root-Cause: atom_classification ohne (use_case,sub_topic)-Index nach DB-Swap -> Index dem DB-Owner empfohlen. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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fda94afd5f |
fix(cra): prod hang-guard /readiness machinery + robuster Datenblatt-JSON-Parse
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#1 _machinery_obligations: SET statement_timeout=4s + run_in_threadpool — auf prod hing die maschinen-Query ~30s (langsame/unindizierte DB nach DB-Swap) und blockierte den async-Worker. Jetzt: bei Langsamkeit graceful 'keine Maschinen-Pflichten' statt Hang. (Fehlender prod-Index = Controls/DB-Session.) #2 parse_grenzen_json: tolerant ggue. ```json-Fences / Prosa-umschlossenem JSON (gehostete Modelle wie OVH ignorieren z.T. response_format) → Datenblatt- Extraktion liefert auch ueber den OVH-Fallback Felder. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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9e2655bfef |
fix(cra): IACE-Create id-Wrapper + MaschinenVO eigene Sektion
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1) createProject las proj.id, der Create-Response ist aber {project:{id}} →
'Projekt anlegen' war kaputt. Jetzt proj.project?.id. E2E verifiziert
(create→put limits_form→get→delete = 200).
2) MaschinenVO-Sicherheitspflichten wurden in die CRA-Cyber-Buckets
(Code/Prozess/Doku) gemischt → fehl-kategorisiert (Maschinen-Safety ≠
CRA-Annex-I-Cyber). Jetzt eigene Response-Liste machinery_guideline +
eigener Frontend-Abschnitt 'Maschinensicherheit (MaschinenVO 2023/1230)';
geklebtes 'MaschVO'-Badge entfaellt damit.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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fae826e1f7 |
fix(cra): 35B-Datenblatt-Extraktion — Thinking-Mode aus (think=false)
qwen3.5:35b-a3b ist ein Thinking-Modell → generierte erst Reasoning, riss das 90s-Timeout → leere Extraktion. llm_cascade additiv um think-Param erweitert (Cache-Key kennt think); Datenblatt-Extraktor setzt think=False → sauberes JSON in ~1s. Default fuer alle anderen Cascade-Nutzer unveraendert. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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b217429d39 |
feat(cra): Datenblatt-Extraktion auf lokales 35B + llm_status-Fix
llm_cascade additiv modell-faehig (optionaler model-Param, Cache-Key kennt model_hint → keine Kollision; Default unveraendert für alle anderen Nutzer). Datenblatt-Extraktor nutzt jetzt qwen3.5:35b-a3b (CRA_DATASHEET_MODEL, gleiches Modell wie der Compliance Advisor) für bessere semantische Zuordnung. Plus llm_status (ok|empty|unavailable) + Logging statt stillem except; Frontend zeigt bei 'unavailable' einen Hinweis statt leerer Felder (wichtig auf prod ohne lokales Ollama → Cascade-Fallback bzw. Hinweis). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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cfdc5fe277 |
feat(cra): Datenblatt→Grenzen-Extraktor (hybrid, lokales 35B)
Hybrid-Extraktion Datenblatt → IACE Grenzen (ISO 12100): deterministischer Detektor (Schnittstellen/Einheiten per Regex) + lokales 35B via llm_cascade (Qwen-lokal-first) fuer die semantische Zuordnung auf die echten LimitsFormData- Keys. Nichts erfinden: Feld nicht im Text → leer + Quellen-Zitat je Feld. Essenzielle ISO-12100-Felder, die leer bleiben → gezielte Rückfragen (foreseeable_misuses, person_groups, qualification, temporal_limits …). Endpoint POST /api/v1/cra/extract-datasheet. 13 Tests gruen (reine Teile). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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62fafaaec5 |
feat(cra): MaschinenVO-Gefährdungs-Ableitung + Cyber-Safety-Brücke
3-Tier-MaschinenVO-Verdict (direkt / sicherheitsrelevant / nicht relevant) aus Personengefährdungs-Signal: eine Komponente ist keine Maschine, aber wenn ihre Funktion bei Fehler ODER Manipulation Personen gefaehrden kann (Bewegung, Laser/ Auge, Kraft, Temperatur, elektrisch), ist sie sicherheitsrelevant — Pflicht trifft den Maschinenbauer, Zulieferer liefert Nachweise, und ein Cyber-Angriff kann die Sicherheitsfunktion aushebeln (Cyber-Safety-Bruecke). OWIS-mit-Laser landet so korrekt als 'sicherheitsrelevante Komponente'. Engine + /readiness additiv; Frontend: Gefährdungs-Frage + -Typen, MaschinenVO-Ergebnisblock. Presets aktualisiert (OWIS: Laser+Bewegung, Zwick: Bewegung). 22 Tests gruen. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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472b0cfd2b |
fix(db): canonical_controls PK + FKs wiederherstellen (prod DB-Swap-Verlust)
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Migration 157: ADD PRIMARY KEY canonical_controls(id) + FK atom_classification + FK control_suppressions, jeweils nur falls fehlend (No-Op auf macmini, fixt prod). Verifiziert: 314.811 distinct ids, 0 NULL, 0 Orphans. DB-Owner-Freigabe. [migration-approved] Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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3afb0e7f4d |
feat(cra): neutrale Eingangstür-Verdict-Engine (zwingend/ratsam/nicht betroffen)
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Reine, deterministische Verdict-Schicht ueber der bestehenden Annex-III/IV- Klassifikation (kein vierter Klassifizierer): trennt Rechtspflicht von Markt- Druck. Kern: das Inverkehrbringen (ab 11.12.2027), nicht der Entwicklungs- zeitpunkt, entscheidet — Bestandsprodukte, die nach der Frist weiter verkauft werden, fallen unter CRA. Producer-Typen (component/end_device/machine_ integrator/software_app) steuern Default-Annahmen (Anlagenbauer: Vernetzung/OTA vorausgesetzt) + Verdict-Betonung (Komponente => Markt-Druck). Plus Evidence- Checkliste (SBOM/VDP/Patch/Lifecycle/Threat-Model/Logging/Auth/Incident) + Reifegrad. /readiness additiv erweitert (verdict/maturity/digital_elements/ producer_type). 15 Tests gruen. Beispiele: OWIS PS90+, ZwickRoell roboTest. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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8086b8be03 |
fix(migration): control_suppressions ohne FK auf canonical_controls
prod-canonical_controls (aus dem DB-Swap) hat weder PK noch Unique auf id → FK InvalidForeignKey. control_uuid bleibt UUID (logische Referenz), wie die bereits FK-lose atom_classification auf prod. [migration-approved] Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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7aabfbe5b5 |
feat(controls): Mandanten-Suppression — per-tenant Applicability-Override
Geteilte Schicht für alle Surfaces (Workspace-Anwälte, Cyber-Risiko-Projekt,
Admin): ein Mandant markiert ein Control als "nicht anwendbar" → in seinen
Use-Case-Ansichten (und künftig Repo-Scans) ausgeblendet.
- Migration 156: compliance.control_suppressions (PK tenant_id+control_uuid),
reversibel (active + reverted_*), auditierbar (actor/reason/created_at).
[migration-approved]
- Service control_suppression: suppress/revert/list_suppressions +
suppressed_control_uuids (geteilter Filter).
- Routes: GET/POST /v1/controls/suppressions + POST .../{uuid}/revert (X-Tenant-ID).
- controls_for_use_case: optionaler X-Tenant-ID + include_suppressed; suppressed
per Default versteckt (nie gelöscht), suppressed_count, suppressed-Flag pro
Control. Agenten/CRA ohne Tenant unberührt.
- Tests: Request-Validierung + import-safety (E2E-Zyklus gegen macmini bewiesen).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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9e9d780902 |
feat(cra): Management-Fortschritts-Ansicht (Ticket-Status-Readback)
Liest den Lebenszyklus jedes Befunds (status + tracker_issue_url) aus dem Scanner zurück und rollt ihn zu einem Management-Bild auf: % erledigt, 4-Phasen (offen/in Arbeit/erledigt/ausgeschlossen), offenes Restrisiko nach Schweregrad, Fortschritt je CRA-Anforderung und eine Aufgaben-/Ticket-Tabelle mit Jira-Link. Neuer Endpoint GET/POST /api/v1/cra/progress (dünn → Service cra_progress, rein deterministisch, kein /assess-Schema-Drift). Frontend: ProgressView in Ebene 1 (CRACyberView), live je Scanner-Repo, sonst Demo-Status. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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7a4f086151 |
feat(cra): Maßnahmen-Provenienz + Lizenzklasse je Normquelle
Jede Normreferenz einer Maßnahme wird lizenzklassifiziert (eu_law / public_domain / open / paid_reference) — paid-reference-Normen werden nur als Verweis geführt, nie im Text gespeichert (idea/expression). Kuratierte Maßnahmen tragen Tier 'core', KI-/Fallback-Maßnahmen 'review' (indikativ). Frontend zeigt Quellen-Badges + "indikativ"-Kennzeichnung. Methodik in docs-src/development/mapping-methodology.md (Szenario C, Due-Diligence). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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6c619ecc42 |
feat(cra): kuratierte Maßnahmen-Bibliothek — alle 40 CRA-Anforderungen belegt
- data/measures_curated.json: 24 deduplizierte, standard-gestützte Maßnahmen (9 bestehende M540-548 + 15 neue M600-614), Volltext + norm_refs + multi-reg covers. Deckt alle 40 CRA-AI-x (vorher nur 17). - cra_annex_i_data lädt die Bibliothek defensiv: MEASURES=Superset, MEASURE_DETAILS (Volltext), mapped_measures aus covers abgeleitet. Fallback = hartkodierte 9. - Mapper: open_measures tragen jetzt name+description+norm_refs (echte Volltexte). - useCRA: merge nutzt Backend-Volltexte statt Demo-Lookup. - Tests: Coverage (40/40) + Volltext im Assessment. Quelle: extern handkuratiert/recherchiert, hier dedupliziert + gemappt. Maschinen- VO/NIS2/IEC-Maßnahmen folgen, sobald deren Spine existiert. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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4c206aa332 |
feat(cra): scanner-repo→IACE-Projekt-Mapping persistieren (Pull-Flow) [migration-approved]
Ersetzt die ephemere Dropdown-Auswahl durch DB-Persistenz pro IACE-Projekt:
- Migration 156: compliance_cra_scanner_repo_map (tenant_id, iace_project_id PK,
scanner_repo_id). Additiv + idempotent.
- GET/PUT /v1/cra/scanner-repo-map/{iace_project_id} (Upsert/Clear).
- useCRA lädt das gespeicherte Repo beim Laden + persistiert bei Auswahl.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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0a6e57ac02 |
feat(use-case-controls): Adressat-Achse — out-of-scope advisory + additiver GOV-Tag
2-Pass-Haiku-Klassifikation (konservativ + Re-Confirm jeder Nicht-unternehmen- Einstufung) der Review-Tier-Atome: wer muss die Pflicht erfuellen? - Migration 155: atom_classification.addressee (unternehmen/oeffentliche_stelle/ aufsichtsbefugnis/staat_eu/dritter/meta), additiv, kein CHECK. [migration-approved] - Service: addressee + applicable + is_gov pro Control; include_out_of_scope-Param (Default false -> out-of-scope advisory ausgeblendet, NIE geloescht); out_of_scope_count. Pure Helper addressee_applicable/addressee_is_gov (+ Tests). - Route: optionaler include_out_of_scope-Query (contract-safe, additiv). - Frontend: GOV-Chip (additiv) + "kein Kunden-Pruefaspekt"-Chip + 1-Klick-Toggle zum Einblenden der out-of-scope-Atome. Daten: 40.859 Adressat-Tags auf macmini geladen (81% applicable, 19% advisory, 3.146 GOV). Konservativ: NULL/Unklar = applicable. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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90def4d857 |
feat(cra): Flow-2 UI — Scanner-Repo wählen → echtes Assessment
- GET /v1/cra/scanner-repos: distinct repo_ids (+counts) vom Scanner-MCP für den Picker. - useCRA: scannerRepo-State; bei Auswahl POST /assess-from-scanner (echte Findings), sonst by-iace/Demo wie bisher. - ScannerRepoPicker im CRA/Cyber-Tab; leere Auswahl = Demo, Repo gewählt = echte Befunde. Mapping repo_id↔Projekt aktuell UI-seitig (ephemeral); DB-Persistenz pro Projekt folgt. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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926dc02a09 |
feat(use-case-controls): relevant als Stufe statt Hard-Filter + Provenance
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Der harte relevant=true-Filter versteckte ~25% des Korpus (40.926 Atome), ~70% davon echte Pflichten (500er-Validierung). relevant wird zur Stufe: - Service: tier-Param (core=Default schuetzt Agent/CRA; all=alles inkl. review), ORDER BY relevant DESC; pro Control relevant/tier/source_type (own_library bei license_rule=3, sonst derived) + source_regulation/article; core_count/review_count. Pure Helper tier_label + source_type (+ Tests). - Route: optionaler tier-Query (default core) — contract-safe (additiv). - Frontend: Coverage-Drill-down /sdk/coverage/[useCase] — Kern-Pflichten vs. "zur fachlichen Pruefung", je mit Herkunfts-Badge; Uebersicht zeigt Delta. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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e140477c0b |
feat(cra): Pull-Flow — Findings vom Scanner-MCP ziehen + assessen
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(2) Wir als MCP-Client zum compliance-scanner-agent: - scanner_mcp_client.fetch_findings(): streamablehttp_client + ClientSession → list_findings, parst JSON-Text zu Finding-Dicts. Config via SCANNER_MCP_URL/ SCANNER_MCP_TOKEN (unset = leer → UI behält Demo). Transport lazy-importiert. - POST /v1/cra/assess-from-scanner: rohe Scanner-Dicts → toleranter Mapper (behält scan_type/cvss_score/file_path) → assess + Breadth. - Tests: parse_findings_text + no-config-Pfad. Live-Verdrahtung der UI folgt, sobald ihr Endpoint+Token stehen (dann nur Env setzen + useCRA auf /assess-from-scanner zeigen). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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e7c3cd7cee |
fix(mcp): DNS-Rebinding-Schutz aus (server-to-server+Bearer) + MCP-Dienst expose-only
- FastMCP transport_security: enable_dns_rebinding_protection nur an, wenn MCP_ALLOWED_HOSTS gesetzt; sonst aus (sonst HTTP 421 "Invalid Host header" bei Aufrufen über nginx/Container-Name). Bearer bleibt die Zugriffskontrolle. - bp-compliance-mcp: Host-Port-Mapping entfernt (8099 war von bp-core-health belegt) → expose-only im breakpilot-network, Routing via nginx (Folgeschritt). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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414496c31a |
feat(mcp): HTTP+Bearer CRA-MCP-Server für den Repo-Scanner + Finding-Adapter
Register-Flow für compliance-scanner-agent (anderes Team, Rust): deren MCP-Client (McpServerConfig) erwartet Streamable HTTP + Bearer — unser MCP war stdio/ohne Auth. - server.py auf FastMCP umgestellt: Tools cra_assess_findings + cra_list_requirements, Dual-Transport (stdio default; Streamable HTTP wenn MCP_PORT gesetzt), Bearer-Gate via CRA_MCP_TOKEN. - ScannerFinding.from_dict tolerant für ihr Finding-Schema (_id/fingerprint, scan_type→category, cvss_score→cvss, file_path→location, severity info→low). - Eigenständiger docker-compose-Dienst bp-compliance-mcp (Port 8099, pure/kein DB, isoliert von der Haupt-API) + Hetzner-amd64-Override. - Tests: test_cra_scanner_adapter, test_mcp_server (Bearer-Gate + Tool-Registry). Pull-Flow (wir holen ihre Findings über ihren MCP) + öffentliches nginx-Routing folgen separat (brauchen ihren Endpoint/Token). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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731076835d |
fix(cra): Konformitätspfad-Kacheln korrekt benennen + Gating nach CRA Art. 32
(a) Labels: Module korrekt zugeordnet — Modul A = Selbstbewertung, Modul B+C =
benannte Stelle, EUCC = eigenes Zertifikat (nicht Modul H), "harmonisierte
Norm" ist kein Modul sondern Konformitätsvermutung. Für den CRA noch KEINE
harmonisierte Norm veröffentlicht → Kachel als "noch nicht verfügbar"
(erwartet ~2027), nicht wählbar, mit Hinweis. (page/path/documents-Labels.)
(b) Gating: wichtige Klasse II + kritische Produkte dürfen NICHT selbst bewerten;
harmonisierte Norm allein genügt dort nicht → ALLOWED_PATHS IMPORTANT_II/
CRITICAL = {eucc, notified_body}; DEFAULT_FOR II = notified_body. _PATH_HINT
entsprechend. Regressionstest test_cra_conformity_paths.py.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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6a6c2dbcaa |
feat(controls): atom-grain liefert source_article + Registry-Tests im CI-Pfad
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- _ATOM_LIST_SQL via LATERAL: zusaetzlich cpl.source_article (Gesetzes-Artikel) im atom-grain Response. Spalte control_parent_links.source_article verifiziert (macmini + prod). - Registry-Mapper-Test (neue Domaenen) nach compliance/tests/ verschoben — CI faehrt compliance/tests/, nicht tests/; schliesst die CI-Luecke der 6-neue-Use-Cases-Erweiterung. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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828230746e |
feat(cra): Befund-Detail aufgeräumt + Rechts-Anker (source_article) sichtbar
Frontend (CRA/Cyber-Tab): - Erklär-Zwischensätze je Ebene (Befund -> CRA-Anforderung -> Best-Practice- Standard -> Maßnahmen) + "So liest du einen Befund"-Legende. - Kuratierte M-Maßnahmen und atom-grain "Regulatorische Breite" in EINE Sektion "Maßnahmen (wählbar)" zusammengeführt (statt zwei konkurrierender Listen). - Standalone "Empfohlene Maßnahmen (Sollzustand)" entfernt (jetzt je Befund). Backend: - Atom-Controls-Query liefert jetzt cpl.source_article (Artikel/Anhang/Erwägungs- grund-Anker) zusätzlich zu source_regulation; via LATERAL-Join. - enrich_findings_with_breadth trägt source_article in regulatory_breadth. - Daten waren schon ingestiert (682/691 CRA-Atome haben source_article) — wurden nur nicht selektiert/angezeigt. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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e5ea2ee7dd |
test(controls): corpus_overview tier-label test im CI-Pfad (compliance/tests/)
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Schliesst die CI-Luecke (Tests lagen in tests/, CI faehrt compliance/tests/) und flaggt backend in detect-changes, damit der zuvor uebersprungene Backend-Build (43 Use Cases, /corpus, + Migration 153 der CRA-Session) auf Prod nachgezogen wird. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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00f304fed9 |
feat(controls): 5 neue Use Cases + Machinery-Fix + Korpus-/Lizenz-Übersicht
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- Registry: arbeitsrecht, gesellschaftsrecht, insolvenzrecht, csrd, bafin_it + Mapper-Regeln für zuvor ungemappte Quell-Gesetze, Machinery-Guide 2006/42 -> maschinen. Jetzt 43 Use Cases (Achse 1 / license 1+2 vollständig). - corpus_overview Service + GET /v1/controls/corpus: Quell-Dokumente mit Lizenz-Tier + atom-Count + Use-Case + kuratiertem Lizenz-Katalog. - list_use_cases trägt atom_classification-Counts (atom_total/atom_relevant). - Frontend /sdk/coverage: Use-Case-Übersicht + Korpus-Dokumente + Lizenz-Katalog. - Tests: registry-Mappings (neue Domänen), corpus tier-labels, coverage-helpers. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |