184 Commits

Author	SHA1	Message	Date
Benjamin Admin	313982c6f1	feat(profile+report): P17 — 4 Polish-Items ... A) Cookie-Policy-Architecture-Block Fallback auf DSE-Text wenn cookie via P15 deduped wurde. Erkennt jetzt auch single-doc Sites (Safetykon-Pattern). B) Konkrete-Aufgaben-Liste: Per-Doc-Cap (3) entfernt + globaler Cap 10→20. Safetykon zeigt jetzt 7 statt 4 Aufgaben. C) business_type-Klassifizierer: B2B-Service-Cluster aus P14 als Boost. Bei 2+ Service-Indikatoren (CE-Zertifizierung/Compliance/Auditierung) wird b2b_score angehoben. Safetykon: "B2C consulting" → "B2B (consulting)". D) Vendor-Extract Fallback auf DSE-Text wenn cookie deduped + keine CMP- Payloads. LLM extrahiert dann Vendors aus dem DSE-Text. Safetykon: 0 → 1 Vendor (Google Analytics aus dem DSE-Text erkannt). Smoke-Test Safetykon: alle 4 Polish-Items wirken, kein Regression. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 12:22:05 +02:00
Benjamin Admin	479ce2225b	feat(profile): P14+P15+P16 — B2B-Heuristik + Doc-URL-Dedup + Homepage-Profile ... P14 — _detect_no_direct_sales erweitert um 3 Cluster: A) OEM-Konfigurator (BMW/Audi/Mercedes/VW/Porsche-Markennamen + Vertragshaendler-Pattern) B) B2B-Dienstleister (CE-Zertifizierung, Compliance-Beratung, Schulungen, Auditierung, TISAX, ISO-Normen, Arbeitssicherheit, ...) C) NGO/Verein/Public (Spendenkonto, Vereinsregister, gemeinnuetzig, ...) Schwelle: pos >= 2 pro Cluster UND pos > neg. Bisher: nur OEM. P15 — Doc-URL-Dedup im Worker: wenn mehrere Doc-Types DASSELBE Dokument referenzieren (Safetykon-Pattern: User gibt /datenschutz fuer dse, cookie UND widerruf), wird nur dem primaeren Doc-Type (Priority: dse > impressum > cookie > widerruf > agb > nutzungsbedingungen) der Text gegeben. Andere landen als "Nicht separat vorhanden — wird im Dokument 'X' mit-geprueft." Eliminiert die 8+8 systematischen widerruf/cookie False Positives. P16 — Profile-Detection auch Homepage-Text: Homepage-HTML wird mit kurzem Fetch (8s timeout) gezogen, getrippt und zum profile_input gemerged. Vor- her wirkte P14 nur wenn B2B-Indikatoren im DSE/Impressum-Pflichttext standen — bei Safetykon stehen sie nur im Homepage-Menue. Plus Bonus: TDM-Override-Submit-Button wird deaktiviert wenn Reason < 10 Zeichen — verhindert dass User wie heute in den Bug rein klickt. Smoke-Test Safetykon (B2B Compliance-Dienstleister): dse geprueft (kein err) impressum geprueft (kein err) cookie "Nicht separat vorhanden — wird in DSE mit-geprueft" agb "Nicht anwendbar — kein Direkt-Kaufvertrag" widerruf "Nicht anwendbar — kein Direkt-Kaufvertrag" nutzungsbedingungen "Nicht anwendbar — kein Direkt-Kaufvertrag" Vorher: 16 False Positives. Jetzt: 0. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 11:46:58 +02:00
Benjamin Admin	b87c27d104	fix(llm-verify): P13 — Default-Modell auf qwen3:30b-a3b (statt qwen3.5:35b-a3b) ... Bug: qwen3.5:35b-a3b liefert mit format='json' + Batch-Prompt leere Strings zurueck ('LLM batch: empty response from model'). Im echten Compliance-Check lief der LLM-Verifier deshalb wirkungslos — False-Positive-Findings wie 'Vorstand nicht erkannt' (BMW: Klammer- Liste) wurden nicht overturned. Fix: Default auf qwen3:30b-a3b umgestellt. Verifiziert mit BMW- Impressum-Text: representative_person wird mit Evidence 'Milan Nedeljkovic, Vorsitzender' overturned=True markiert. OLLAMA_VERIFY_MODEL Env-Var bleibt als Override-Moeglichkeit. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 09:11:01 +02:00
Benjamin Admin	28a078ccb4	feat(compliance-check): P10 — Cookie-Policy-Architecture-Detection ... Neuer Service cookie_policy_architecture.detect_architecture(...) prueft vier Diagnose-Punkte der Cookie-Policy einer Website: 1. Layer-Trennung: single (BMW-Pattern: Banner + Info in EINER URL) | separate (Best Practice: getrennte Layer) 2. Versionierung: "Stand vom DD.MM.JJJJ" / "Version X.Y" / ... 3. Dynamic content: CMP-Capture auf Doc-URL oder Marker-Texte 4. Vendor-Count im Text: Indikator ob Liste statisch drinsteht Risiko-Ampel: - gruen: separate + versioned + statisch - gelb : single+unversioned (BMW) ODER separate+unversioned - rot : weder noch (Pflicht-Info fehlt) Wire-in im Compliance-Check-Worker: nach Exec-Summary-Block wird der Architecture-Block gerendert (build_architecture_html) mit konkreter Empfehlung. Bei BMW-Pattern: "Snapshot der dynamischen Vendor-Tabelle als versioniertes PDF im Archiv." Hintergrund: BMW hat eine HTML-Seite die GLEICHZEITIG Banner-Re-Trigger und Cookie-Richtlinie ist. Mindestanforderung nach §25 TDDDG + Art. 13 DSGVO erfuellt, aber bei einer Aufsichtsbehoerden-Pruefung kann nicht belegt werden welche Vendor-Liste an einem bestimmten Stichtag aktiv war. Das ist kein Verstoss aber best-practice-Luecke. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 01:01:48 +02:00
Benjamin Admin	0d37822b7c	fix(impressum): P9 — 7 False-Positive-Fixes in Pflichtangaben-Checks ... #1 Name des Anbieters: \b Word-Boundary verhindert "ag" in "samstag", plus "aktiengesellschaft" als Volltreffer. #2 Vertretungsberechtigte: Klammer-Liste-Pattern erkennt jetzt BMW- Format "Vorstand (Milan Nedeljkovic, Jochen Goller, ...)" plus "Vorsitzender des Aufsichtsrats: Name". #3 V.i.S.d.P.: war schon INFO, OK. #4 OS-Plattform/VSBG: bei no_direct_sales=True (OEM-Pattern) jetzt als "Nicht anwendbar" skipped statt 0/1 fail. Profile fliesst neu durch check_document_completeness -> runner. #5 Zustaendige Kammer: IHK + Handwerkskammer + Tieraerztekammer in Pattern aufgenommen + severity LOW -> INFO (konditional). #6 Stammkapital: war schon INFO, OK. #7 Link-Disclaimer: neue Check-Eigenschaft "invert"=True. Anti-Pattern ist passed wenn NICHT gefunden, fail wenn gefunden. Vorher feuerte das Finding immer, jetzt nur wenn ein illegaler Disclaimer im Text ist. Plus: L2-INFO-Checks (z.B. profession_chamber) zaehlen nicht mehr in correctness-pct und erzeugen keine DSI-DETAIL-Findings. Konsistent mit P8-Modell: INFO = "selbst pruefen", nicht "fail". Verifiziert mit BMW-Impressum-Text — alle 7 Faelle korrekt klassifiziert: name=passed, representative_person=passed, profession_chamber=INFO, illegal_disclaimer=passed (kein Disclaimer im Text), dispute_resolution=skipped (no_direct_sales), editorial_visdp=INFO, share_capital=INFO. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 00:52:03 +02:00
Benjamin Admin	575644c9c5	feat(audit): P8 — MC-Severity raus, Email nur harte Findings, MC-Audit als Checkliste ... Email-Hardening (mc_scorecard.top_fails): Neue _is_hard_finding-Heuristik filtert konditionale MCs ohne Negativ-Beleg aus den Top-Auffaelligkeiten. matched_text leer + Label enthaelt "falls/sofern/wenn/soweit/ggf." -> raus, landet nur noch im MC-Audit als "selbst pruefen". DATA-2066-A05 (kostenfreie Abschaltung Standortdaten) ist das prototypische Beispiel. MC-Audit-Frontend (audit/[checkId]/page.tsx): Severity-Spalte (CRITICAL/HIGH/MEDIUM/LOW) entfernt — der MC-Audit ist eine Checkliste, keine Severity-Drohung. Stattdessen: - Spalte "Prioritaet" mit 3-Tier aus regulation-Mapping: Gesetz (DSGVO/ePrivacy/TDDDG/...) / Behoerden-Leitlinie (EDPB/DSK/EuGH/...) / Best-Practice (ISO/NIST/BSI) - 3-Status: erfuellt (✓) / nicht erfuellt (✗) / selbst pruefen (?) / nicht anwendbar (—). rowReviewStatus() leitet "selbst pruefen" aus matched_text-leer + konditionalem Label ab. - Filter umgebaut auf 5 Stati statt 4 - Default-Filter "Nicht erfuellt" (vorher "Nur Fail") Bonus: f.payload.risk_label TS-Cast im FindingsTab clean gemacht (unknown -> string). Effekt: - Email an die GF zeigt nur noch echte Belege ("DSB fehlt", "Gebuehr fuer Widerruf") - MC-Audit ist eine sachliche Pruefliste fuer den Compliance-Officer Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 00:30:04 +02:00
Benjamin Admin	6c223c7c9b	feat(compliance-check): exec-summary + voll-audit + TDM-respect + cookie-KB-extended + saving-scan-funnel ... P1 — Exec-Summary oben im Email-Report (4 KPIs + 2 CTAs, dunkler Gradient) P3 — no_direct_sales-Flag fuer OEM-Konfigurator-Sites; AGB/Widerruf/AGB als "NICHT ANWENDBAR" (grau) statt "NICHT GEFUNDEN" (rot) P5 — Voll-Audit Unification: alle Findings (MC + Pflichtangaben + Vendor + Redundanz) in /data/compliance_audits.db.unified_findings; neuer /api/compliance/agent/findings/<id> Endpoint + FindingsTab im Audit-UI mit Filter + CSV-Export P7 — Crawl-Hardening: TDM-Reservation-Check (robots.txt / ai.txt / Header / Meta) vor jedem Run mit 24h-Cache; HeadlessChrome-UA (Firma noch nicht gegruendet — Switch via BREAKPILOT_BRANDED_UA env); per-Domain Rate-Limit 1 req/s + max 2 concurrent P2 — Cookie-Knowledge-DB additiv erweitert (35 -> 74 Cookies): Adobe, Meta, Microsoft, LinkedIn, TikTok, HubSpot, Marketo, Salesforce, Hotjar, FullStory, Mouseflow, Intercom, Drift, Zendesk, Cloudflare, Stripe, OneTrust/Cookiebot/Usercentrics, Matomo, Pinterest, Snapchat, X/Twitter, YouTube, Vimeo, Klaviyo, Mailchimp, Mixpanel, Segment, Amplitude, Optimizely, Datadog; Wire-in in cookie_function_classifier liefert compliance_risk-Label (kritisch/hoch/mittel/gering) pro Vendor A — k-Anonymitaets-Helper (benchmark_k_anonymity) fuer P6-Vorbereitung B — Cross-Tenant-Domain-Assertion im /findings-Endpoint (expected_domain Query-Param -> 403 bei Mismatch) C — Saving-Scan-Funnel: /api/compliance/agent/saving-scan/start mit Validierung + 24h-Rate-Limit pro Domain + Lead-Persistenz in saving_scan_leads + Auto-Discovery via _run_compliance_check; 6 Tests D — Risk-Badge im Email-Vendor-Row Rechtliche Leitplanken (Memory feedback_oem_data_legal.md): nur eigene Knapp-Bewertungen + Source-Pointer, keine 1:1-Kopien fremder CMP-Texte. TDM-Opt-Out-Respect nach § 44b UrhG. KEINE Schema-Aenderungen — alles in Sidecar-SQLite. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 23:48:34 +02:00
Benjamin Admin	662327e8b4	feat(compliance-check): MC-Classification + Embedding + Vendor-Redundanz + Action-Recipes + Borlabs-Features ... Massiv-Update auf Basis BMW-Test-Iterationen (v1→v9): Core Compliance-Check - Sonnet check_type Klassifikation: text/process/review fuer alle 1874 MCs in compliance.doc_check_controls (script + Sidecar /data/mc_classification.db). rag_document_checker filtert auf check_type='text' fuer doc_check. Plus fits_doc_type-Audit (v2) + ui_only-Audit fuer DSA/E-Commerce-MCs in falscher doc_type-Schublade. - scope_requires-Filter: biometric/ai_decision/child_targeting MCs werden per business_profile gefiltert (FRT skipped fuer BMW etc.). - Embedding-Match (BGE-M3) als Phase-3 nach Regex-Match: Per-doc_type-Threshold-Override (impressum 0.50, dse/cookie 0.60), Short-Field-Rescue (15-Wort-Chunks) fuer Pflichtfelder im Impressum. Title+check_question als Embedding-Input fuer mehr Kontext. - Cookie-Text-Routing: consent-tester gibt cmp_cookie_text aus dem CMP-Reconstruct zurueck, Backend bevorzugt das gegen DOM-Extraction wenn richer (BMW 1824 vs 600 Worte). Vendor-Redundanz + EU-Alternativen + Cost-Saving - vendor_redundancy.analyze() — funktionale Kategorisierung der CMP-Vendors, Detektion von Mehrfach-Anbietern pro Kategorie, EU-Alternative-Lookup (Matomo, IONOS, HERE, Friendly Captcha, Smart AdServer, ...). - vendor_cost_estimator: Tier-Inferenz aus Cookie-Footprint (Cookie-Anzahl + Premium-Feature-Cookies + Third-Party-Quote → starter/professional/ enterprise/premier). - Self-Service-Werbung (Google/Meta/Pinterest/...) = 0 Lizenz-Kosten (nur Media-Spend, separat). DSP-Plattformen behalten enge Range. - Tier-aware Saving-Range: bei Enterprise/Premier nutzen wir den oberen 40-100%-Band der Listpreise, nicht starter→premier. - Multi-Function-Tools (Matomo Pro, SAP CX, IONOS Cloud, Userlike, Smart AdServer, HERE Maps, Vimeo Pro, LamaPoll) — ein Tool ersetzt mehrere Kategorien gleichzeitig. Cookie-Wissens-DB + Funktionale Klassifikation - cookie_knowledge_db: 50 kuratierte Top-Cookies (Google/Meta/Adobe/MS/...) mit vendor, exact_purpose, data_collected, IAB-TCF-IDs, reid_risk, schrems_ii_status, EuGH-Urteile, EU-Alternative. - cookie_function_classifier: pro Cookie funktionale Rolle (tracking_id, ad_pixel, session_id, ab_test, csrf, ...) + blocking_impact. Country-Inferenz aus Rechtsform - cookie_link_validator: Country-Field wird aus Vendor-Name abgeleitet (A/S=DK, GmbH=DE, Inc=US, B.V.=NL, ...) plus Vendor-Lookup-Table. Reduziert false-positive no_country-Flags bei eindeutig-EU-Vendors (Adform DK, Pinterest IE). Action-Recipes + Doc-Anchor-Locator - finding_action_recipes: pro Finding-Typ (no_cookies_listed, no_country, broken_opt_out, "Auftragsverarbeiter erwaehnen", "Art. 22 Profiling", ...) eine strukturierte Anweisung mit what/why/fix_text/where/example. Zum 1:1-Einfuegen in Kunden-Dokumente. - doc_anchor_locator: Embedding-basiert (BGE-M3 cosine) — sucht den passenden Absatz im existierenden Kundendokument fuer jeden Finding. Per-Run Thread-Local-Cache. Fallback: keyword-Match. - Email-Rendering integriert Recipe + Anchor pro Doc-Pruefungs-Fail + Vendor-Flag-Liste mit aufklappbarer Action-Liste. - Score-Erklaerung pro Vendor-Zeile (3/5-Untertitel + Tooltip). Migration-Pipeline (Compliance-Check -> Customer Banner/Documents) - migration_to_banner.py: Vendor-Liste -> CookieBannerConfig mit 4 Kategorien + Review-Flags. - migration_to_document.py: Vendor-Liste -> Cookie-Policy + VVT-Register + Privacy-Policy-Pre-Fills. - agent_migration_routes: 3 Preview-Endpoints (banner-preview, document-preview, summary). Persistierung der cmp_vendors in /data/compliance_audits.db check_payloads-Tabelle. Borlabs-Parity Cookie-Banner-Features - Consent-Historie im Banner: window.bpShowConsentHistory() + localStorage. - Content-Blocker: cookie-banner-content-blocker.ts — YouTube/Maps/Video Placeholder bis Einwilligung. - Google Consent Mode v2 erweitert: wait_for_update + region=EEA/CH/GB. - Consent-Log Export (CSV/JSON) per einwilligungen_export_routes. Bug-Fixes - canonical_control_routes: _jsonish-Helper fuer string-typed jsonb, similar-controls-Endpoint mit _has_embedding_col()-Cache (kein 500 mehr). - Control-Library Frontend: defensive .map-Coercer in 2 Detail-Views. - Embedding-Service-Batching (32er Batches statt 165 in einem Call). - KeyError 'control_id' in MC-Result-Aggregation (defensive .get). - Master-Controls-Klick-Through von /sdk/master-controls auf /sdk/control-library?control=<id> mit URL-Param-Auto-Open. - Dockerfile: /data pre-chowned auf appuser (Audit-DB-Schreibrecht). - Cookie-Text-Routing-Bug (cmp_reconstructed > DOM-extraction). - doc_type-aware MC-Filter (statt all-text-MCs). - Master-Contract-Dedup (60 BMW-Internal-Eintraege = 1 Adobe-Vertrag). - A3-v2-Audit hat 24 UI-Sprache-MCs als 'process' reklassifiziert. Tests - test_migration_mappers.py (9 Tests) - test_migration_endpoints.py (4 Tests) Skripte (one-shot) - classify_mc_check_type.py (v1) + _v2 (PK=control_id,doc_type) - audit_mc_doctype_fit.py (v1 fits) + _v2 (ui_only + scope_requires) BMW-Run-Bilanz v1 (broken) -> v9 (alle Fixes): DSE 7,5% -> 81-83% Impressum 4% -> 100% (6 echte MCs alle erfuellt) Cookie 0% -> 79-83% (CMP-Text-Routing + Embedding) Plus: 10 Konsolidierungs-Kategorien, geschaetzte Saving 200k-3M / Jahr Plus: Action-Recipes + Doc-Anchors fuer jeden Fail Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 18:30:08 +02:00
Benjamin Admin	df7d83134b	feat(agent): migrate compliance-check results to banner + documents (M1-M5) ... After a compliance-check run finishes, the user can now apply the extracted vendor inventory directly to their own: - CookieBanner config (admin /sdk/einwilligungen) - Cookie-Policy / VVT-Register / Privacy-Policy templates (admin /sdk/document-generator) Backend: - migration_to_banner.py: vendor list -> CookieBannerConfig with ESSENTIAL/PERFORMANCE/PERSONALIZATION/EXTERNAL_MEDIA buckets + review flags (broken opt-out URLs, missing expiry, no cookies listed) - migration_to_document.py: vendor list -> pre-fills for 3 doc templates, recipient-type aware (INTERNAL/GROUP/PROCESSOR/CONTROLLER) - agent_migration_routes.py: GET /banner-preview, /document-preview, /summary keyed on check_id - compliance_audit_log: new check_payloads table persists cmp_vendors + extracted_profile so the preview survives an app restart - tests: 9 mapper units + 4 endpoint integration tests Frontend: - MigrationPanel.tsx: modal showing banner-config diff + document pre-fills, plus links into the existing editors - ComplianceCheckTab.tsx: replaces standalone audit link with the panel; net -3 lines, stays at the 500-cap Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-17 14:06:28 +02:00
Benjamin Admin	6ed30dae5b	feat(agent): MC scorecard + audit drill-down + tenant trend (A1-A6) ... Now that all 1874 MCs run per check (Task #30 cap removal), the report was about to drown in noise. This commit adds the full aggregation / persistence / drill-down stack so each MC is actionable, not just counted. A1 mc_scorecard.py (new): build_scorecard(checks) -> per-regulation PASS/FAIL/SKIP + severity top_fails(checks, n) -> N most severe failed MCs full_audit_records(...) -> flat rows ready for sidecar SQLite A2 Email rendering: agent_doc_check_scorecard.py (new) builds an HTML scorecard table (regulation × passed/failed/HIGH/MEDIUM/score) shown at the top of the email. agent_doc_check_report._render_document now collapses the 500-MC L2 forest into 'X/Y bestanden (Z Fail)' summary plus a top-10 fails block per doc — old verbose render is gone. A3 compliance_audit_log.py (new) — sidecar SQLite at /data/compliance_audits.db (separate from compliance Postgres schema to comply with the no-new-migrations rule in CLAUDE.md): check_runs(check_id, ts, tenant_id, site_name, base_domain, doc_count, scorecard json, vvt_summary json) mc_results(check_id, doc_type, mc_id, label, passed, skipped, severity, regulation, matched_text, hint) Route persists every run after the email is sent. docker-compose.yml adds compliance-audit volume + env. A4 backfill_mc_regulation_llm.py (new) — Qwen-tagged backfill for the 1636 MCs the regex pass couldn't classify. Batches of 25, format=json, output constrained to the canonical regulation list. Run manually: docker exec bp-compliance-backend python3 \ /app/scripts/backfill_mc_regulation_llm.py [--dry-run] A5 Admin audit tab — GET /api/compliance/agent/audit/<check_id> proxied via /api/sdk/v1/agent/audit/<id>. New page /sdk/agent/audit/[checkId] renders scorecard + filterable MC table (status / doc_type / regulation, expandable rows with matched_text + hint). ComplianceCheckTab now shows 'Voll-Audit oeffnen' link. A6 Trend per tenant — GET /api/compliance/agent/audit/tenant/<id> returns recent runs. Email scorecard shows per-regulation delta badges ('(+12%)', '(-3%)') compared with the previous run for the same tenant + base_domain. Lookup is one SQLite query. Plumbing: rag_document_checker.py — SELECT now includes 'article'; MC results carry 'regulation' + 'article' through to CheckItem. agent_doc_check_routes.CheckItem schema gains regulation + article fields (defaults '') so old clients still parse. agent_compliance_check_routes — response gains 'check_id' so the frontend can build the audit link.	2026-05-17 13:45:58 +02:00
Benjamin Admin	6d29191e9b	fix(vvt): score INTERNAL/GROUP without opt-out/privacy penalty ... User feedback after BMW test: - 60 'BMW AG — XYZ' rows were rendered as ✗ for Opt-Out/Privacy and scored 38-52%. That's misleading: BMW processing for itself doesn't need a separate opt-out URL (cookie-banner is the consent mechanism) or a separate privacy policy (main DSI covers it). - Title 'Anbieter' was wrong for 60 of 90 rows (internal services). Three orthogonal fixes: 1. score_vendors becomes recipient_type aware: - INTERNAL/GROUP_COMPANY: opt_out_url, privacy_policy_url, country are NOT required (the user's main DSI + cookie-banner cover them). What IS required: name, purpose, cookies disclosed with name + expiry. Cookies-disclosure weight raised to 50 (was 15) so the VVT-relevant data is the score driver. - 'necessary' category: opt-out still skipped (§25 Abs. 2 TDDDG). - External (PROCESSOR/CONTROLLER): existing strict scoring stays. 2. _link_status_badge accepts na_label and renders a neutral em-dash with explanation tooltip instead of red ✗ when the column doesn't apply to that row. _render_vendor_row_full passes na_label based on recipient_type: - INTERNAL/GROUP -> 'Nicht erforderlich (eigene Verarbeitung)' - necessary -> 'Nicht erforderlich (§25 Abs. 2 TDDDG)' 3. Header + summary clarify the split: - h3 changed to 'Verarbeitungstaetigkeiten und Empfaenger aus der Cookie-Richtlinie' (was 'Drittanbieter aus Cookie-Richtlinie'). - Top line: '90 Verarbeitungen erfasst — 60 eigene + 30 externe Empfaenger'. - Disclaimer below: explains the INTERNAL/GROUP exemption so the reader understands why those rows don't show ✗ for missing URLs. - Section labels enriched with the relevant DSGVO article: 'Eigene Verarbeitungstaetigkeiten — fuer das VVT (Art. 30)', 'Auftragsverarbeiter — AVV erforderlich (Art. 28)', 'Joint Controller — Vereinbarung pruefen (Art. 26)'. Expected BMW result after fix: ~85% of the 60 BMW-AG rows jump from ~52% to 90-100% (the real issue, fehlende Cookies-Disclosure, stays flagged). The only true findings remaining are external links that return 4xx (e.g. Criteo 403, Teads 404).	2026-05-17 13:15:40 +02:00
Benjamin Admin	8a44e67293	feat(compliance-check): unlock all 1874 MCs + close gap-table items ... User: 'wir haben 1800 MCs erstellt um sie zu 10% zu nutzen — das ist Schwachsinn'. Fixed all 6 gaps from the audit. #1 max_controls=0 (was 20): - agent_compliance_check_routes _check_single: passes max_controls=0 to check_document_with_controls -> ALL MCs evaluated per doc_type. - 8 doc_types now use 1874 MCs instead of 160 (10x coverage). - Regex matching is cheap (<1s per doc); LLM-enrich cap of 10 stays. #2 LLM-verify fixed: - llm_verify.py was getting 0/N parsed. Causes: qwen3 thinking-mode wrapped output in <think>...</think>, /api/generate doesn't enforce JSON, prompt didn't handle code-fence wrappers. - Now uses /api/chat with format='json' (forces valid JSON). - _parse_batch_response strips <think> tags, accepts {results:[...]} AND bare [...], adds richer regex-fallback parse, logs raw head on total parse failure for diagnosis. #3 Loeschkonzept checklist (new): - doc_checks/loeschkonzept_checks.py — 9 L1 + 7 L2 checks per DIN 66398 + Art. 5(1)(e)/17/32 DSGVO: scope+responsibility, data categories, retention periods, legal basis refs (HGB/AO/BGB), deletion trigger, deletion process+technical+systems, deletion proof, exceptions + Art. 18 lock, review cycle, DSGVO references. - runner.py registered for loeschkonzept/loeschung/loeschfristen. #4 regulation backfill script: - backend-compliance/scripts/backfill_mc_regulation.py — regex-detects DSGVO/TDDDG/TMG/BGB/HGB/AO/MStV/UWG/VSBG/PAngV/GwG/BDSG/EU-VO references in MC title+question+pass_criteria, UPDATEs regulation + article fields. - Idempotent (only NULL rows), --dry-run flag, batched 200/UPDATE. - Run inside container: docker exec bp-compliance-backend python3 \ /app/scripts/backfill_mc_regulation.py #5 MC alias-fallback: - rag_document_checker._MC_ALIAS_FALLBACK maps doc_types without own MCs to a related set: nutzungsbedingungen->agb, social_media->dse, sub_processor/scc/tom_annex->avv, loeschfristen->loeschkonzept, eu_institution/dsb->dse. - _load_controls retries with the alias when the primary query returns 0 rows. - 14 additional doc_types now get MC coverage transparently. #6 cross-domain auto-discovery: - _autodiscover_missing builds a crawl plan: primary submitted base + up to 2 related domains sharing the owner SLD (e.g. BMW Group: bmw.de + bmwgroup.com + bmwgroup.jobs). - Detection: regex over submitted texts for https?://...<owner>... hostnames distinct from the primary base. - Each crawled base contributes documents + cmp_payloads to the discovery pool. Net effect for BMW: 1874 MCs evaluated (90 from cookie alone, was 20), Loeschkonzept Pflichtangaben benoten-bar, LLM overturns false regex FAILs, Joint-Controller policies on bmwgroup.jobs (Social Media) jetzt entdeckbar. Same wins will apply to CRA-Compliance check.	2026-05-17 13:07:50 +02:00
Benjamin Admin	fab1e35847	feat(vvt): recipient-type classification + 3-section VVT table ... Per user request: BMW (and others) put their own services AND external vendors in the same cookie-policy widget. The VVT-Tabelle now groups them by Art. 30(1)(d) DSGVO recipient category so the DSB can act on the right buckets: - INTERNAL — owner processing for itself ('BMW AG — XYZ') - GROUP_COMPANY — same brand family, different legal entity ('BMW Bank') - PROCESSOR — Auftragsverarbeiter, AVV-pflichtig (Adobe, Akamai) - CONTROLLER — independent / joint controller (Meta Pixel, Google Ads, LinkedIn — they run their own profiles) - AUTHORITY — government bodies (rare in cookies) - OTHER — fallback New module vendor_classifier.py: - owner_from_url(url) — derive site-owner token (bmw.de -> 'BMW', mercedes-benz.de -> 'Mercedes-Benz') - classify(name, category, owner) — strict 5-tier heuristic: * INTERNAL: vendor name first-token is '<Owner>' / '<Owner> AG' / '<Owner> SE' / '<Owner> GmbH' / '<Owner> AG & Co. KG' * GROUP_COMPANY: starts with '<Owner> ' but isn't '<Owner> AG' * CONTROLLER: matches a known joint-controller list (Meta, Google Ads, YouTube, LinkedIn Insight, TikTok, Pinterest, Taboola, Outbrain, Criteo, Twitter, Reddit, ...) * PROCESSOR: legal-form suffix in name (GmbH, AG, Inc., A/S, B.V., S.A., Ltd., LLC, ...) * OTHER: anything else vendor_extractor.extract_vendors_from_payloads now takes owner_name: - Passes it through to classify() for every extracted vendor record - The route derives owner_name via _company_name_from_url(doc_entries) - LLM-extracted vendors are classified the same way (so V3 fallback also produces tagged records) agent_doc_check_extras.build_vvt_table_html rewritten: - Buckets vendors by recipient_type - Renders one section per non-empty bucket, in canonical order (RECIPIENT_TYPE_SECTIONS), each with section header + count + bad count + nested table - Within each section: sorted by compliance_score ascending - Response JSON cmp_vendors includes recipient_type so the frontend can later import per-category into the VVT module Expected BMW result: ~60 INTERNAL rows (BMW AG own services), ~25 PROCESSOR rows (Adobe, Adform, Akamai, AWS, ...), ~5 CONTROLLER rows (Meta Pixel, Google, LinkedIn, Pinterest, Outbrain, Taboola).	2026-05-17 12:31:49 +02:00
Benjamin Admin	6c7d4c7552	fix(vvt): correct ePaaS schema mapping + category-aware scoring ... The first BMW VVT table rendered all 24 providers at 20% score because the ePaaS extractor was reading the wrong field names. Actual schema is nested: providers[].processings[].persistences[], NOT providers[] alone. Correct ePaaS schema (verified against bmw.com/epaas/.../de_DE.epaas.json): Provider: {id, name, description, processings[]} Processing: {id, name, description, categoryId, optOutLink, privacyPolicyLink, persistences[]} Persistence: {id, name, domain, type, expiry, description} Two structural changes: 1. One row per processing (not provider). BMW has 26 providers but ~91 processings spread across them (Adobe alone has ACMProcessing, AdobeAnalytics, AdobeCampaign, AdobeTargetAnalytics, AdobeTargetPers.). The cookie widget displays each processing separately — VVT now mirrors that. Display name format: 'Provider Name — Processing Name'. 2. Read optOutLink/privacyPolicyLink from PROCESSING (where they live), not provider. Persistences flatten to cookies[] with name + expiry + description. Plus category mapping: advertising -> marketing strictlyNecessary -> necessary statistics -> statistics functional -> functional Category-aware scoring (cookie_link_validator.score_vendors): - 'necessary' (technisch erforderliche, §25 Abs. 2 TDDDG): no opt-out required, no country required. Score weight shifts to purpose + cookie disclosure (essential cookies must list names + expiry). - All other categories: opt-out URL still mandatory; missing opt-out flags 'no_opt_out_url' and zeros that block of points. Expected BMW result after this fix: - ~91 rows (Adobe Analytics, Adform Retargeting, Akamai Infrastructure, AWS, ..., plus ~60 strictlyNecessary processings) - Marketing rows with present opt-out → ~75-90% - Necessary rows with cookie+expiry → ~85-95% - Rows missing fields → still flagged	2026-05-17 11:19:31 +02:00
Benjamin Admin	873997c13b	feat(vvt): V3 — LLM vendor extraction fallback for unknown CMPs ... When the cookie text has no captured CMP payload (long-tail sites that don't use ePaaS/OneTrust/Cookiebot/etc.) we now fall back to a Qwen → OVH LLM cascade to extract a structured vendor list from the policy text. New module backend/compliance/services/vendor_llm_extractor.py: - extract_vendors_via_llm(cookie_text): runs Qwen first (local Ollama), then OVH if Qwen returns nothing usable. - System prompt instructs the model to return STRICT JSON only: {vendors: [{name, country, purpose, category, opt_out_url, privacy_policy_url, persistence, cookies: [...]}]} - Lenient JSON parser tolerates code-fences, prose wrappers, dict vs list. - _normalize() caps array sizes (80 vendors, 30 cookies each), validates URLs (must be http(s)), trims fields to reasonable lengths. Route integration (agent_compliance_check_routes.py): - After named-CMP extract: if cmp_vendors is empty AND the cookie text has ≥500 words (otherwise it's likely navigation chrome), invoke the LLM extractor. Progress message 'Vendor-Liste per LLM extrahieren...'. - Vendors then run through the same validate_vendor_urls + score_vendors pipeline → VVT table rendered identically regardless of source. docker-compose.yml: backend-compliance gains OLLAMA_URL, CMP_LLM_MODEL, OVH_LLM_URL/KEY/MODEL env vars (same names as consent-tester so the configuration is unified). This closes the 'every site eventually gets a VVT table' goal: - Known CMP → V1/V2 structured extraction (fast, exact) - Unknown CMP → V3 LLM extraction (slow, best-effort) - No text at all → no vendors, but other compliance checks still run.	2026-05-17 09:55:42 +02:00
Benjamin Admin	9c0cc0f59f	feat(vvt): V2 — vendor extractors for Cookiebot/Usercentrics/Didomi/TrustArc ... Backend vendor_extractor.py gets 4 new per-CMP dispatchers, mirroring the JSON schemas observed in each platform: - Cookiebot: 'Categories[*].Cookies[*]' with Vendor/Host, expiry, purpose - Usercentrics: 'services[*]' with cookieMaxAgeSeconds, processingCompanyCountry - Didomi: 'app.vendors[*]' with country + policyUrl - TrustArc: 'vendors[*]' + per-category 'Cookies' with provider All 6 named CMPs (ePaaS, OneTrust, Cookiebot, Usercentrics, Didomi, TrustArc) plus the generic-shape fallback are now mapped — every site hitting Phase B of the cascade gets a structured vendor list, scored opt-out links, and a VVT-Tabelle in the email.	2026-05-17 09:52:10 +02:00
Benjamin Admin	ea4dbb223f	feat(vvt): per-vendor extraction + opt-out check + VVT table in email (V1) ... When a known CMP (ePaaS, OneTrust) renders the cookie policy, we now extract structured vendor records, probe their opt-out + privacy URLs, score each vendor (0-100), and append a 'VVT-Vorschlag' table to the compliance email — one row per vendor, sortable by compliance score. consent-tester: - DSIDiscoveryResult.cmp_payloads: surfaces raw CMP JSON to callers - DSIDiscoveryResponse: new cmp_payloads field - discover_dsi_documents sets cmp_payloads from cmp_capture - cmp_library/{epaas,onetrust}.py: new extract_vendors(d) returning list[VendorRecord] backend: - _fetch_text() now returns (text, cmp_payloads) tuple - doc_entries store cmp_payloads per doc (mostly cookie) - _autodiscover_missing forwards homepage payloads to the cookie entry - New module vendor_extractor.py: dispatches ePaaS/OneTrust/generic schemas; dedupes vendors across multiple payloads - cookie_link_validator.py extended with validate_vendor_urls(vendors) and score_vendors(vendors) — 0-100 score per vendor based on name, purpose, country, opt-out reachable, privacy URL reachable, cookies with names + expiry - agent_doc_check_extras.build_vvt_table_html: renders the table - Route appends VVT HTML after the provider list, before the document-by-document report - Response JSON gains cmp_vendors for future frontend rendering Example for BMW: ~30 ePaaS providers → table with Name | Kategorie | Sitz | Cookies | Opt-Out (✓/✗) | Privacy (✓/✗) | Score. Sorted by score ascending so the worst-compliant vendors are at the top.	2026-05-17 09:50:11 +02:00
Benjamin Admin	c9c0fb5965	feat(cookie-check): enhanced patterns + active opt-out link validator ... cookie_checks.py: - cookie_names_listed: now also matches CMP placeholder notation (BMW: 'Adfpc###', 'CT###') and 'Diese Datenverarbeitung verwendet die folgenden Cookies oder ähnliche Technologien' as list-shape signal. Cryptic vendor names like 'audience', 'adformfrpid' are accepted via the surrounding markup, not by hard-coding each one. - cookie_providers_named: new pattern 'Gesetzt von: <Firma>' (BMW/ePaaS per-cookie vendor naming) + recognition of full legal-form names (Adform A/S, BMW AG, Adobe Systems Software Ireland Limited). - cookie_duration_values: now matches 'Ablauf: 1 Jahr' / 'Speicherdauer: 30 Tage' (BMW format) in addition to the legacy '<n> <unit>'. New L1 + L2 checks for controller in cookie-policy: - cookie_controller (L1): the cookie policy must name Verantwortlich(er) - cookie_controller_address (L2): PLZ + Ort or address keywords - cookie_controller_contact_or_link (L2): email/phone OR link back to Datenschutzerklärung (the practical equivalent — BMW does this) New L2 checks (parented under opt_out): - cookie_optout_links: detects per-provider opt-out URLs in the text - cookie_privacy_policy_links: per-provider privacy-policy URLs New service: cookie_link_validator.py - extract_links(text): pulls all https?://… URLs that follow 'Opt-Out Link:' / 'Link zur Privacy Policy:' (deduped) - validate_links(links): probes every URL concurrently (HEAD first, GET fallback for 405/403). 10 parallel, 8s per request, 60s batch cap. Returns reachable=True/False + status + final_url. - build_check_items(): renders 2 CheckItems (opt-out + privacy-policy), each pass if ALL links 2xx/3xx, fail with up-to-5 broken-link examples. Hook in _check_single: doc_type=='cookie' triggers the validator after regex+MC checks. Recomputes correctness with the new L2 items. This addresses two concrete BMW observations: 1. BMW's per-cookie structure (Name + Zweck + Ablauf, Gesetzt von: …, Opt-Out Link: …) now recognised → 'Konkrete Cookie-Namen aufgelistet' and 'Konkrete Speicherdauern' should pass. 2. Defective opt-out URLs surface as compliance findings rather than silently passing — Art. 7(3) DSGVO requires a working withdrawal path per provider.	2026-05-17 09:38:32 +02:00
Benjamin Admin	b090662524	fix(compliance-check): respect auto-discovery 'not found' verdict; DSB not canonical ... Two related bugs in the BMW test result: 1. AGB rendered as 'MANGELHAFT 0/13' even though BMW has no public AGB: - Auto-discovery correctly returned 'not found' for AGB (no link on bmw.de matches AGB keywords). - But auto_fill_from_dsi then found the substring 'AGB' in a section of the DSI and pseudo-filled the AGB entry with a 264-word DSI fragment. - cross_search_documents would have done the same. - Both now skip entries where discovery_attempted=True AND auto_discovered=False — the 'not found' verdict stands. 2. DSB-Kontakt rendered as a separate 100% OK document with 7566 words = the entire DSI text: - GDPR practice: the DSB is named *inside* the DSI as an email or contact block (Art. 13(1)(b)), not as a stand-alone page. - cross_search_documents had been assigning the full DSI to the DSB row because it matched 'datenschutzbeauftragte' keywords. - DSB removed from _ALL_DOC_TYPES — no longer canonical, no longer padded as missing, no longer auto-discovered. The frontend row remains so a tenant with a separate DSB page can still submit one. After this fix BMW should render: - DSE: OK - Impressum: LUECKENHAFT (unchanged — regex gaps to fix separately) - Cookie-Richtlinie: OK - Social Media: NICHT GEFUNDEN (bmw.de does not link to it) - AGB: NICHT GEFUNDEN (correct — BMW has no public AGB) - Nutzungsbedingungen: NICHT GEFUNDEN - Widerruf: NICHT GEFUNDEN	2026-05-17 01:53:09 +02:00
Benjamin Admin	bc21480a2a	fix(compliance-check): always render 8 doc types + 4 BMW GT-gap fixes ... Always-show-8 (user-requested): - agent_compliance_check_routes.py: _pad_results_with_missing pads the results list to always include all 8 canonical doc_types in canonical order. Missing types get a placeholder DocCheckResult with error= 'Nicht eingereicht' + scenario='missing'. - agent_doc_check_report.py: NICHT EINGEREICHT status label (neutral), friendly grey body block instead of red error. - ChecklistView.tsx: 'Nicht eingereicht' chip (neutral grey, not red 'Fehler'); SCENARIO_LABELS adds missing entry + header chip counter. Impressum-Regression fix (#18): - _fetch_text(url, doc_type): cookie/dse/social_media -> max_documents=1 (CMP capture authoritative, sub-pages dilute). Other types -> =3 (Impressum needs Versicherungsvermittler, Aufsicht, Berufsrecht sub- pages). 15s networkidle bail keeps timing safe. ODR/Verbraucherstreitbeilegung filter (#19): - _apply_profile_filter: when profile.needs_odr=True (B2C), override the check's default B2B-oriented hint with action-oriented B2C guidance pointing at Art. 14 EU-VO 524/2013 + §36 VSBG. Previously the check contradicted itself: 'profile says B2C' + hint 'only relevant for B2C online vendors'. Registergericht regex (#20): - impressum_checks.py: accept colon/dot/dash between keyword and city (BMW writes 'registergericht: münchen hrb 42243'). Add 'sitz und registergericht: X' as separate pattern. Industry detection (#21): - business_profiler.py: 'automotive' keywords broadened (antriebs, motor, leasing, werkstatt, probefahrt, plus brand names BMW/Mercedes/ Audi/VW/Porsche/Opel). 'it_services' keywords narrowed — software/ cloud/hosting are mentioned in every privacy policy and were biasing the result toward IT for any tech-aware company.	2026-05-17 01:03:58 +02:00
Benjamin Admin	e61e9d9e2a	feat(agent): progress_pct + 6 BMW-Run Verbesserungen ... Backend (agent_compliance_check_routes.py): - progress_pct (0-100%) im Job-State, ueber alle Phasen verteilt (Laden 0-30, Profil 35-40, Pruefen 40-80, Banner 80-92, Report 95-100) - Status-Texte vereinheitlicht ("Texte laden X/N", "Pruefen X/N") - Firmenname fuer Email-Subject jetzt aus URL abgeleitet (bmw.de -> "BMW", mercedes-benz.de -> "Mercedes-Benz") statt unzuverlaessigem extracted_profile.companyName (matchte oft juris.de) - E-Mail-Report enthaelt jetzt Banner+TCF-Vendor-Liste (build_provider_list_html) Backend (agent_doc_check_extras.py — neu): - build_scanned_urls_html: gepruefte URLs als Tabelle oben im Report (transparent fuer GF, welche Quellen wirklich gezogen wurden) - Cross-Domain-Hinweis bei >1 netloc (BMW: bmw.de / bmwgroup.com / bmwgroup.jobs — Auffindbarkeit nach Art. 12 DSGVO) - build_provider_list_html: Banner-Box + TCF-Vendor-Tabelle mit Spalten Name | Kategorie | Zweck | Drittland | Rechtsgrundlage Backend (business_profiler.py): - §34d-GewO Versicherungsvermittler-Hinweise zaehlen nicht mehr als "finance"-Industrie (BMW wurde dadurch falsch als B2B/finance erkannt) - Neue Industry "automotive" (Fahrzeug/KFZ/Konfigurator/Modellpalette) - B2B-Keywords: generische Begriffe wie "unternehmen", "beratung", "consulting" entfernt (matchten in jedem Konzerntext) - B2C-Fallback: bei Verbraucher-Signalen ("widerruf", "kunde", redaktioneller Inhalt) tendiert auf b2c statt b2b Frontend (ComplianceCheckTab.tsx): - Progress-Balken mit Width-% und XX%-Anzeige rechts - liest data.progress_pct aus Polling-Response Consent-Tester (dsi_discovery.py): - Cookie-Policy-Extraktion kritisch fixt: wait_for_function bis body.innerText > 500 chars (BMW SPA-Rendering brauchte mehr Zeit) - _extract_text_robust: 3-Strategien-Extraktion (Selektoren -> Body- Cleanup -> P/LI/TD-Tags) - _extract_text_from_iframes: liest OneTrust/Sourcepoint/Usercentrics Iframe-Inhalte (manche Cookie-Policies leben dort) Adressiert alle Findings aus dem BMW-Ground-Truth-Vergleich.	2026-05-16 17:53:14 +02:00
Benjamin Admin	bd2d6976d6	fix(cross-doc): also check entries with wrong text, not just empty ones ... Cross-search now validates if existing text matches the expected doc_type using keyword scoring. If text is present but doesn't match (e.g. Nutzungsbedingungen in Widerruf row), searches other texts and creates a finding explaining the mismatch. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-15 00:19:40 +02:00
Benjamin Admin	4e9043f26d	feat(cross-doc): search all texts for all doc_types + misplacement finding ... Cross-Document Intelligence: When a doc_type row is empty, searches ALL other loaded documents for that content. If found (e.g. Widerruf in AGB), extracts the section, runs the check, AND creates a finding: "Widerrufsbelehrung in falschem Dokument gefunden — schwer auffindbar" Keywords for: widerruf, cookie, social_media, impressum, agb, dsb. Integrated as Step 1c in compliance check pipeline. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-14 23:19:39 +02:00
Benjamin Admin	33bf2b7c5a	feat(service-detector): detect 118 services in legal texts (was 20) ... New service_detector.py uses service_registry (88 entries) plus 30+ extra text patterns to detect services mentioned in DSI/legal texts. Results on Spiegel: 31/32 services detected (97%, was 5/32 = 16%). Includes metadata: name, category, country, EU adequacy status. - Profiler now uses detect_services_in_text() instead of 20-entry list - Profile extractor adds detected_services with full metadata - Auto-generates scope hint for non-EU services (Drittlandtransfer) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-13 16:00:15 +02:00
Benjamin Admin	5e317d2f0f	fix: text extraction 50k char limit was root cause of all Spiegel FNs ... ROOT CAUSE: main.py line 338 truncated full_text at 50,000 chars. Spiegel DSI has 107,720 chars (13,705 words) — only 47% was extracted. DSB, Art. 77, Betroffenenrechte were all in the truncated portion. Fixes: 1. Raise text limit from 50k to 200k chars in API response + discovery 2. click_button(): add iframe fallback for Sourcepoint/Quantcast 3. dsi_helpers: iterate ALL page.frames for consent buttons 4. Profiler: only check impressum (not full text) for regulated professions, and "rechtsanwalt" must be in first 500 chars (company description) 5. GT: save full Spiegel DSI text (13,705 words) as reference Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-13 15:22:38 +02:00
Benjamin Admin	c702260ec1	fix: 5 regex bugs + text extraction scroll + GT update ... Root cause: Spiegel DSI text was truncated (lazy-loading) — the rights/DSB/complaints sections at the bottom were never extracted. Fixes: 1. Text extraction: scroll to bottom before innerText (dsi_discovery.py) 2. V.i.S.d.P.: add "verantwortlicher i.s.v." + "§18 Abs. N MStV" pattern 3. USt-IdNr: add "umsatzsteuer-id" + "DE 212 442 423" (with spaces) 4. Profiler: remove generic "anwalt"/"praxis" (false positive on Spiegel "Redaktionsanwalt"), keep only "rechtsanwalt", "kanzlei" etc. 5. Section splitter: auto_fill_from_dsi() fills empty Cookie/Social-Media rows from sections found in the DSI text Ground Truth 06-spiegel.md fully rewritten with verified data from live website — 3 L1 False Negatives identified (DSB, Beschwerderecht, Betroffenenrechte all present on website but not in extracted text). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-13 01:20:55 +02:00
Benjamin Admin	0b9150f16f	feat(vendor-assessment): Pruefprotokoll + Frontend + Sidebar ... Phase 4-5: Professional Pruefprotokoll report builder with styled HTML output (Kopfdaten, Kategorie-Scores, L1/L2 Check-Hierarchie, Findings, Freigabe-Block). Frontend at /sdk/vendor-assessment with 3-step flow: DocumentUploader → AssessmentProgress → PruefprotokollView. Sidebar: "Use-Case Audits" → "Vertragspruefung" renamed. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 23:24:12 +02:00
Benjamin Admin	0326d5baab	feat(vendor-assessment): AVV/SCC/TOM/Sub-Processor checklists + assessment service ... Phase 1-3 of the Vendor Contract Assessment: Backend checklists (Doc-Check L1/L2 engine compatible): - avv_checks.py: 28 checks (11 L1 + 17 L2) for Art. 28(3) DSGVO - scc_checks.py: 7 checks for EU SCC 2021 (modules, annexes, TIA) - tom_annex_checks.py: 12 checks for Art. 32 (8 control objectives) - sub_processor_checks.py: 7 checks for sub-processor list completeness Assessment service: - POST /vendor-compliance/assessments — async contract analysis - GET /vendor-compliance/assessments/{id} — poll status - Cross-check engine: detects missing SCC when AVV mentions third-country, missing TOM annex, missing sub-processor list All checklists registered in runner.py CHECKLIST_MAP (27 doc_types total). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 23:14:54 +02:00
Benjamin Admin	c867478791	feat(tcf-vendors): GVL cache + vendor extraction + VVT mapping ... Phase 1-2 of the closed quality loop: - GVL cache (consent-tester/services/gvl_cache.py): downloads and caches IAB Global Vendor List with 24h TTL, resolves vendor IDs to names, purposes, policy URLs, retention, country - Vendor extraction (consent_interceptor.py): extract_tcf_vendors() reads __tcfapi after accept phase, resolves via GVL - Scan response: tcf_vendors field added to /scan endpoint - VVT mapper (vendor_vvt_mapper.py): maps TCF vendors to VVT format with purpose labels, Rechtsgrundlage, Drittland detection - Vendor cross-check (banner_cookie_cross_check.py): checks all TCF vendors against DSI text — missing vendors, undocumented transfers - Compliance check integrates Step 3d: TCF vendors vs DSI Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 18:18:50 +02:00
Benjamin Admin	7be34552bb	feat(compliance-check): profile extraction + scenario classification ... - New profile_extractor.py: extracts Company Profile fields (name, legal form, address, DPO, USt-IdNr) and Compliance Scope hints (Art. 9 data, third country, profiling) from document texts - Scenario per document: regenerate (<30%), fix (30-95%), import (>95%) - Widerruf for B2B: no longer skipped, instead all checks flagged as INFO with "not needed for B2B" hint - Move _build_profile_html to report builder module - DocCheckResult gets scenario field Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 17:34:33 +02:00
Benjamin Admin	be9cfdc2d4	feat(compliance-check): skip Widerruf for B2B, limit MCs, fix industry ... - Skip Widerrufsbelehrung check entirely for B2B/B2G businesses - Limit MC checks to top 20 per doc_type (by severity) to reduce noise (e.g. 75 impressum MCs → 20, avoiding 55 irrelevant FAILs) - Add consulting/manufacturing industry keywords (arbeitssicherheit, brandschutz, werkzeugbau, etc.) - Lower industry detection threshold from 2 to 1 keyword hit Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 17:03:57 +02:00
Benjamin Admin	b42e1cd091	feat(cmp): timezone→geo_country mapping + timezone parameter ... Add _resolve_geo_from_timezone() with 35-country IANA timezone map. Accept timezone field in ConsentCreate schema and pass through to service. Populate geo_country automatically from browser timezone. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 14:43:13 +02:00
Benjamin Admin	4a7e09bbb0	fix(impressum): regex [A-Z] never matches on lowercased text ... All patterns matched against text_lower but used [A-Z] character class. Changed to [a-zA-Z] so patterns like "geschäftsführung: dr. oliver" are found. Also added "Pflicht"/"Detail" labels to the two progress bars to clarify what 100% vs 8% means. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 14:02:25 +02:00
Benjamin Admin	74f00bbb0f	feat(compliance-check): split shared URLs into sections per doc_type ... When the same URL is used for multiple document types (e.g. /datenschutz for DSI + Cookie + DSB), the section splitter now: - Detects duplicate URLs and fetches text only once - Splits text at classified headings (Cookie, Google Analytics, etc.) - Assigns matching sections to each doc_type - DSI always keeps the full text Extracted to section_splitter.py (170 LOC) to keep routes under 500. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 12:49:57 +02:00
Benjamin Admin	407a9503e4	fix(profiler): fix B2G false positive + add consulting/manufacturing ... - Remove generic B2G keywords (behörde, amt, öffentlich) that match in every DSI due to "Aufsichtsbehörde", "Amtsgericht", "veröffentlichen" - Remove "server" from it_services (too generic, appears in every DSI) - Add consulting, manufacturing, media industries - Add B2B fallback for GmbH/AG without B2C signals - Add 10 ground truth files for unified compliance check Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 12:20:44 +02:00
Benjamin Admin	ce77cde309	fix(compliance-check): batch LLM verification + increase poll timeout ... - LLM verify now sends ALL failed checks in one batched call instead of one Ollama call per check (80+ calls → 1 per document) - Increase frontend poll timeout from 6 min to 15 min Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 11:49:30 +02:00
Benjamin Admin	b6ad958b69	feat(compliance-check): integrate banner cross-check + extract to module ... Add automatic banner check (Step 3b) and banner-vs-cookie cross-check (Step 3c) to unified compliance check. Extract cross-check logic to banner_cookie_cross_check.py to keep routes under 500 LOC. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-12 00:08:47 +02:00
Benjamin Admin	66d30568e2	feat(dsms): Stufe 1 — Gap-Analyse Report wird in DSMS archiviert ... - Go DSMS Client (internal/dsms/client.go): Archive() + Verify() - Python DSMS Client (compliance/services/dsms_client.py): archive_to_dsms() + verify_dsms() - Gap-Analyse AnalyzeProject() archiviert Report-JSON nach DSMS - Response enthält dsms_cid wenn Archivierung erfolgreich - Frontend: Grünes "Revisionssicher archiviert" Badge mit CID im GapDashboard - DSMS Proxy Route (/api/sdk/v1/dsms/[...path]) für Verify-Abfragen Stufe 2 (Evidence Upload → DSMS) und Stufe 3 (Version Chains) folgen. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-11 23:39:26 +02:00
Benjamin Admin	397de741c1	feat(cmp): Phase 2 — script blocking + cookie tracking ... Migration 108: scripts_blocked, scripts_released, cookies_set JSONB columns. Backend models/schema/service/serializer/routes extended. Admin detail modal shows released scripts and set cookies with categories. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-11 22:52:26 +02:00
Benjamin Admin	051890c370	feat(cmp): restore vendor-agnostic fields + module wiring ... Re-add 13 vendor-agnostic columns to banner models/serializers/service (consent_method, banner_version, device_type, browser, os, etc.) that were lost when another session overwrote the code. Keep vendor_consents dict from the other session. Add list_consents method back to BannerConsentService. Wire CookieBanner, Loeschfristen and UseCases into Document Generator contextBridge (CMP_NAME, analytics tools, retention months, feature flags). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-11 21:57:54 +02:00
Benjamin Admin	0d0e705117	feat: Unified Compliance-Check — 8 document types in one form ... New 3-tab structure: Website-Scan, Compliance-Check, Banner-Check. Compliance-Check Tab (replaces Dokumenten-Pruefung + Impressum-Check): - 8 document rows: DSI, Impressum, Social Media, Cookie, AGB, Nutzungsbedingungen, Widerruf, DSB-Kontakt - Each row: URL input + "Text laden" + file upload + manual text - "Text laden" extracts via consent-tester, shows in editable textarea - User verifies/corrects text before checking - Empty fields = "not present" → own finding Business Profiler (business_profiler.py): - Detects B2B/B2C/B2G from all documents together - Recognizes regulated professions, online shops, editorial content - Context-aware: INFO checks become PASS/FAIL based on profile Backend: /compliance-check + /extract-text endpoints Frontend: ComplianceCheckTab.tsx + DocumentRow.tsx API proxies: compliance-check/route.ts + extract-text/route.ts Also: Impressum regex fixes (Telefon, AG, Geschaeftsfuehrung) and INFO severity for context-dependent checks. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-11 20:56:10 +02:00
Benjamin Admin	0c25832b5c	fix: Context-aware Impressum checks + 3 regex fixes ... 3 Regex fixes: - Telefon: matches '0761 / 48 98 09 01' format (spaces around /) - Registergericht: matches 'AG Freiburg' (not just 'Amtsgericht') - Vertretung: matches 'Geschaeftsfuehrung:' (not just 'Geschaeftsfuehrer:') 6 checks changed from FAIL to INFO severity: - V.i.S.d.P.: only relevant if website has editorial content - Streitbeilegung: only relevant for B2C online shops - Berufsrecht: only relevant for regulated professions - Stammkapital: legally required but rarely enforced - Aufsichtsbehoerde: only for licensed activities - Berufshaftpflicht: only for mandatory insurance INFO checks don't count towards completeness percentage. They appear as hints, not findings. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-11 15:23:19 +02:00
Benjamin Admin	36c6101b91	Merge feat/zeroclaw-compliance-agent into main ... Brings all compliance doc-check features: - 162 regex checks + 1874 Master Controls - LLM-agnostic agent with tool calling - Banner check (46 checks, 30 CMPs, stealth, Shadow DOM) - Impressum check (24 checks) - Deep consent verification (DataLayer, GCM, TCF) - CMP E2E tests (39 tests) - HTML email reports, FAQ, persistent history Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-11 11:44:20 +02:00
Benjamin Admin	289ec5f396	feat(cmp): vendor-agnostic consent data model — 13 new fields ... Extend banner consent records with consent_method, banner_version, banner_config_hash, geo, page_url, referrer, device info, session_id and consent_scope for full Art. 7 DSGVO proof with any tracking vendor. Migration 107, backward-compatible (all fields nullable). Admin detail modal shows tracking context, device info and technical data. Fix pre-existing str|None → Optional[str] for Python 3.9 compat. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-10 23:12:20 +02:00
Benjamin Admin	58f370f4ff	feat: LLM-agnostic Compliance Agent with tool calling ... New agent architecture for intelligent MC evaluation: agent_tools.py (367 LOC): - 5 tools in OpenAI function-calling format - query_controls: async DB query for MCs by doc_type - evaluate_controls_batch: deterministic keyword matching - search_document: text search with context - get_document_stats: word count, sections, language - submit_results: finalize check results compliance_agent.py (398 LOC): - ComplianceAgent class with agent loop - 3 LLM providers: Ollama, OpenAI-compatible (OVH), Anthropic - Tool call dispatch + result collection - System prompt for systematic compliance analysis - run_compliance_check() convenience function Hybrid mode: - COMPLIANCE_USE_AGENT=false (default): deterministic regex - COMPLIANCE_USE_AGENT=true: LLM agent with tool calling - Agent fallback to regex if LLM unavailable Works with Qwen 35B (Ollama), Qwen 120B (OVH vLLM), Claude. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-10 22:56:09 +02:00
Benjamin Admin	bdbc30e47b	feat(cmp): unified consent view — Website-Besucher + Login-Nutzer tabs ... Merges two separate consent views into one unified page at /sdk/einwilligungen: - Tab "Website-Besucher": device-based banner consents with site selector - Tab "Login-Nutzer": user-based DSGVO consents (existing, unchanged) Backend: - New endpoint GET /admin/consents for paginated banner consent records - Fix: categories JSON string parsing (was iterating chars instead of array) CMP Dashboard: - Dynamic site selector replacing hardcoded "preview-test-site" Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-10 22:41:56 +02:00
Benjamin Admin	9cbbc6ee2f	feat: LLM interpretation layer for failed MC checks ... Deterministic pass/fail stays unchanged. After keyword checking, ONE batched LLM call enriches the top 10 severity FAILs with context-specific recommendations based on the actual document. Example: If document uses Google Analytics but lacks transfer mechanism → LLM generates: "Sie nutzen Google Analytics (USA). Ergaenzen Sie einen Verweis auf das EU-US Data Privacy Framework und pruefen Sie die DPF-Zertifizierung unter dataprivacyframework.gov." - Pass/fail: deterministic (keyword matching, reproducible) - Hint enrichment: LLM (contextual, one call for all fails) - Temperature 0.3 for consistency - Graceful fallback if Ollama unavailable Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-10 22:08:07 +02:00
Benjamin Admin	5ea83e9b33	feat: Deterministic MC checking — ALL controls, no LLM, reproducible ... Replaced LLM-based MC verification with deterministic keyword matching: - Extracts keywords from pass_criteria/fail_criteria - Matches against document text via regex (case-insensitive) - PASS if >= 60% of criteria keywords found AND no fail_criteria triggered - Same text + same MCs = same result every time Checks ALL MCs for the doc_type (max_controls=0): - DSE: all 571 controls checked in <1 second - Impressum: all 75 controls - Cookie: all 381 controls No LLM calls needed — purely deterministic keyword matching. Bigram extraction for compound terms (e.g. "standardvertragsklauseln"). Stop word filtering for German legal text. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-10 21:51:58 +02:00
Benjamin Admin	26b222d53d	feat: Integrate 1.874 Master Controls into document checking ... Rewritten rag_document_checker.py to use doc_check_controls table instead of generic canonical_controls. Each MC has: - check_question: binary YES/NO for LLM - pass_criteria: JSONB list of concrete requirements - fail_criteria: JSONB list of common mistakes Flow: Regex checks (fast) → LLM verify FAILs → MC deep check (15 per doc) MC results appear as additional L2 checks in the report. Coverage: 571 DSE, 381 Cookie, 309 Loeschkonzept, 153 Widerruf, 147 DSFA, 125 AVV, 113 AGB, 75 Impressum = 1.874 total. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-10 21:06:03 +02:00
Benjamin Admin	82951785ec	feat: Impressum checks expanded from 16 to 24 (GAP analysis) ... 8 new checks: Reglementierte Berufe, Grundkapital, Aufsichtsbehoerde, Berufshaftpflicht, rechtswidrige Disclaimer, Kammer, Berufsbezeichnung, berufsrechtliche Regelungen. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-10 09:29:49 +02:00