1. Soft hyphens (/\xad) stripped before regex matching —
fixes "Datenübertragbarkeit" not matching
2. Art. 15/17/20: allow adjectives between "Recht auf" and keyword
("Recht auf unentgeltliche Auskunft" now matches)
3. DSB contact: regex spans up to 300 chars across newlines
(DSB section with company address between heading and email)
4. Löschkonzept: added "Fortfall", "Entfall", "Beendigung" as
deletion trigger words alongside "Ablauf"/"Wegfall"
Reduces etogruppe FPs from 5 to ~1.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Every hint now reads like a mini-consultation from a data protection
lawyer — with specific legal references, court rulings, and common
mistakes. Examples:
- EuGH C-210/16 (Fanpage), C-298/17 (Kontaktpflicht), C-311/18 (Schrems II)
- BGH I ZR 228/03 (ladungsfaehige Anschrift), XI ZR 388/10 (AGB)
- EDSA Guidelines 2/2019 (lit. b misuse), WP 248 Rev.01 (DSFA)
- DSK-Orientierungshilfe, CNIL-Leitlinien, SDM, BSI-IT-Grundschutz
- §25 TDDDG, §38 BDSG, §309 BGB, §312k BGB, Art. 246a EGBGB
This is the core value proposition: no lawyer can deliver this level
of specific, actionable compliance feedback in 60 seconds.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Hint now explicitly warns that EU-US Privacy Shield is invalid since
Schrems II (July 2020) and recommends DPF or SCC as replacements.
This is the kind of specific, actionable feedback that makes the tool
valuable — catching outdated legal references no human would spot
in under a minute.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1. LLM model: qwen3:32b → qwen3.5:35b-a3b (actual model on Mac Mini)
2. Section splitter: headings MUST be preceded by a blank line.
This prevents cookie table entries ("Funktionale Cookies",
"Session Cookies") from splitting the cookie section.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fixes based on manual verification of all 30 failed checks:
1. Cookie table: recognize "folgende cookies" + column headers as text
2. Cookie names: add JSESSIONID, cookieinfo, et_id, BT_* patterns
3. Essential justified: match "sitzung zuordnen", "betrieb der website"
4. Social bookmarks: recognize as 2-click alternative
5. DSFA plural: "kanaelen" now matches alongside "kanal"
6. Section splitter: skip-headings no longer lose subsequent text
(Risikoabwaegung section was cut from DSFA, losing risk scores)
7. Cookie legal basis: accept Art. 6(1)(f) in cookie context
Reduces false positives from 7 to ~1-2 for IHK Konstanz test case.
Ground truth table: zeroclaw/docs/ground-truth-ihk-konstanz.md
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Each check now has a "hint" field explaining what is missing and
what the customer should do to fix it. Hints are shown in the
frontend below failed checks in red text.
Examples:
- "Bei Verarbeitung auf Basis von Art. 6(1)(f) muss dokumentiert
werden, warum Ihr berechtigtes Interesse die Rechte der
Betroffenen ueberwiegt."
- "Die ladungsfaehige Anschrift fehlt. Erforderlich: Strasse,
Hausnummer, PLZ und Ort."
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Patterns ran on text.lower() but searched [A-Z] — changed to [a-z].
Also accept D-12345 prefix (common German format).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Only use controls whose test_procedure mentions document-type-specific terms:
- DSI: test_procedure must contain 'datenschutzerkl' or 'art. 13/14'
- Cookie: must contain 'cookie', 'einwilligung', 'consent'
- Impressum: must contain 'impressum'
This filters out internal governance controls (Datenmodelle, Infrastruktur)
that are irrelevant for public document checks.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Complete rewrite of rag_document_checker.py:
- Queries canonical_controls table (294K controls, 10K data_protection)
- Filters by category + title keywords per document type
- Uses test_procedure field as actual check instructions
- Regex pre-check extracts key terms from procedure → fast match
- LLM fallback only for regex misses (saves tokens)
- /no_think prefix for direct JSON output
SQL approach advantages:
- Structured data with test_procedure, pass_criteria, fail_criteria
- Category filtering (data_protection, compliance, governance)
- No Qdrant API key issues
- Controls are actual check criteria, not general legal texts
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
LLM returns {fulfilled: true} instead of {"fulfilled": true}.
Now fixes unquoted keys, True→true, and falls back to text-based
boolean extraction when JSON parsing fails entirely.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Qwen 3.5 uses all tokens for thinking, leaving response empty.
Using /no_think prefix to get direct JSON output.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Qwen 3.5 with latest Ollama returns structured thinking in separate
'thinking' field, leaving 'response' empty. Now checks both fields.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replaces scroll+filter approach with proper semantic search:
1. Embed query via bp-core-embedding-service (bge-m3, 1024 dim)
2. Vector search in Qdrant (bp_compliance_datenschutz + bp_compliance_gesetze)
3. Sort by cosine similarity score
4. No API key needed — local Qdrant on Mac Mini
Falls back gracefully: SDK first, then semantic Qdrant, then empty.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Go SDK points to external Qdrant (qdrant-dev.breakpilot.ai) with expired API key.
Fallback: search directly in local Qdrant (bp-core-qdrant:6333) which has
all collections: bp_compliance_datenschutz, bp_compliance_gesetze, atomic_controls_dedup.
Search strategy:
1. Try Go SDK RAG endpoint (preferred, has embedding-based search)
2. Fallback: Qdrant scroll with text-based regulation filter
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New module: rag_document_checker.py
- Searches RAG (Qdrant) for controls relevant to document type
- Filters by regulation (DSGVO Art.13, TDDDG §25, BGB §355 etc.)
- LLM (Qwen 3.5:35b) verifies each control against document text
- Returns fulfilled/missing with evidence text + severity
- Supports: DSI, Cookie, Impressum, Widerruf, AGB, DSFA, AVV, Loeschkonzept
Integration in doc-check endpoint:
- Regex checklist runs first (fast, deterministic)
- RAG checks run after (semantic, catches what regex misses)
- Both results combined in single response
LLM prompt returns JSON: {fulfilled, evidence, issue, severity}
Think-tags stripped, JSON extracted from response.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New "Dokumenten-Pruefung" tab in Compliance Agent:
- User adds multiple URLs with document type (DSI, AGB, Impressum, Cookie, Widerruf)
- Each document loaded via Playwright, accordions expanded, text extracted
- Checked against type-specific legal checklist
- Optional: Cookie banner check via checkbox
Checklisten-UX (solves "100% looks like nothing was checked"):
- All checks shown per document: green checkmark + matched text excerpt
- Red X for missing fields with legal reference
- Builds user trust: "9 Punkte geprueft, alle bestanden"
- Expandable per document with completeness bar
New checklists:
- Impressum: §5 TMG (6 fields: name, address, contact, register, VAT, representative)
- Cookie-Richtlinie: §25 TDDDG (5 fields: types, purposes, retention, third-party, opt-out)
Backend:
- POST /agent/doc-check — async with polling (same pattern as /scan)
- DocCheckResult includes checks[] with passed/failed + matched_text
- dsi_document_checker returns all_checks in SCORE finding
- Email report shows per-document checklist
Files: agent_doc_check_routes.py (280 LOC), DocCheckTab.tsx (248 LOC),
ChecklistView.tsx (130 LOC), dsi_document_checker.py (+70 LOC)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Service detection:
- Only search script tags + src/href attributes for service patterns
- Prevents false positives from DSE text mentioning services
(e.g. IHK DSE describes etracker, 'google analytics' in text)
- Technical patterns (with regex chars) still checked in full HTML
Short documents:
- Documents with < 200 words flagged as 'Kurzhinweis' instead of
'MANGELHAFT' — too short for Art. 13 completeness check
- Prevents 96-word navigation pages from showing 8 missing fields
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: When all 9 Art. 13 checks passed (100%), no SCORE finding
was created (line: 'if pct < 100'). The backend then defaulted to
completeness=0 because it looked for the SCORE finding to extract the %.
Fix: Always generate SCORE finding, even at 100%. Added 'OK' severity
for fully compliant documents.
This was the cause of 8 documents showing '0% MANGELHAFT' despite
containing all required information.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
These files existed on the feature branch but were never cherry-picked
to main, causing ModuleNotFoundError on import:
- dse_parser.py — parses DSE HTML into structured sections
- dse_matcher.py — matches detected services against DSE sections
- mandatory_content_checker.py — checks Art. 13 DSGVO mandatory fields
- legal_basis_validator.py — validates legal basis (lit. a-f)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- EmailDeliveryService: load template → find published version →
render {{variables}} → send via SMTP → audit log. Fallback to
inline HTML when no published template exists.
- Migration 117: Professional HTML/text content for all 5 DSR
templates (receipt, completion, rejection, identity, extension)
with branded styling and proper Art. references
- DSRArt11Service now uses EmailDeliveryService with dsr_rejection
template instead of hardcoded HTML
[migration-approved]
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- New DSRArt11Service: handles rejection with proper legal basis,
automated email notification to requester explaining Art. 11
- POST /dsr/{id}/reject-art11 endpoint
- ActionButtons.tsx: "Nicht identifizierbar (Art. 11)" button
shown when identity is not yet verified
- Also fixes: DSR export type-cast rollback handling
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- tenant_id kept as string (PostgreSQL handles UUID cast)
- Einwilligungen query uses CAST(:tid AS VARCHAR) for compatibility
- Each data source query wrapped with rollback on failure to prevent
cascading "transaction aborted" errors
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- DSRExportService: aggregates all CMP data about a user from
Banner Consents, Einwilligungen, Audit Trail, DSR History
- GET /dsr/{id}/export-user-data?format=json|csv|pdf endpoint
- PDF: A4 reportlab with 4 sections (Consents, Einwilligungen,
Audit-Trail, DSR-Anfragen) + cover page
- CSV: BOM-encoded for Excel with flattened data rows
- JSON: structured export with all data categories
- ActionButtons.tsx: PDF/JSON/CSV export buttons now functional
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Migration 115: compliance_role_training_mapping table (org roles → training codes)
- TrainingLinkService: queries training_modules/matrix/assignments to find gaps
per person and role. Gracefully degrades when Go training tables don't exist yet.
- document_review_routes: 2 new endpoints (training-requirements, training-gaps)
- _notify_approval() now checks training gaps and sends emails to persons
with outstanding modules, linking to /sdk/training/learner
[migration-approved]
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Backend: _ensure_list() converts null/string/malformed JSONB to []
for requirements, test_procedure, evidence, open_anchors, tags.
Frontend: defensive Array.isArray() check on ControlDetail.tsx.
Fixes: TypeError: A.requirements.map is not a function
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- CookieBannerOverlay: shows vendors per category with expandable tables
(Verarbeiter, Cookies, Dauer, Land) for full transparency
- Demo vendors: 4 necessary, 3 statistics, 3 marketing, 3 functional
- cookie_table_generator.py: renders {{COOKIE_TABLE}} Markdown tables
from vendor configs (DB) or service registry (fallback)
- SERVICE_COOKIES: 16 known vendor-to-cookie mappings with provider + country
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- CookieBannerOverlay: shows vendors per category with expandable tables
(Verarbeiter, Cookies, Dauer, Land) for full transparency
- Demo vendors: 4 necessary, 3 statistics, 3 marketing, 3 functional
- cookie_table_generator.py: renders {{COOKIE_TABLE}} Markdown tables
from vendor configs (DB) or service registry (fallback)
- SERVICE_COOKIES: 16 known vendor-to-cookie mappings with provider + country
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fundamental architecture fix: data processing happens through APIs/scripts/
cookies — NOT through visible page text. A news site about healthcare does
NOT process health data.
Before: Qwen reads website text → guesses "health_data: true" (WRONG)
After: Google Analytics detected → tracking: true (CORRECT, deterministic)
New flow: detect services from HTML → map service categories to flags →
feed flags into UCCA assessment. No LLM needed for flag extraction.
SERVICE_TO_FLAGS maps categories: tracking→tracking, marketing→marketing+
third_party_sharing, payment→payment_data, heatmap→profiling, etc.
SPECIFIC_SERVICE_FLAGS for Klarna (Art.22), Stripe (US transfer), etc.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Benjamin Admin