Benjamin Admin
d0e3621192
Mail Render V2 (compliance/services/mail_render_v2/) — 11-Modul-Subpackage
das einen einheitlichen Audit-Mail-Output erzeugt mit:
- Header + KPI-Kacheln (Score / Findings / Docs / Vendors)
- TOC + Sprung-Links
- 3-Bucket-Trennung: Kritische Befunde / Manuelle Prüfung / Interne Reminder
- Cookie-Inventar (Name·Vendor·Kategorie·Speicherdauer·Löschfrist·Sitzland·Quelle·Status)
- Sofortmaßnahmen-Aggregator ("Sitzland ergänzen für 11 Cookies")
- 24 Legacy-Wrappers — alle alten build_*_html in V2-Sections
- Scope-Filter: FIN/GOV/MED/INS/EDU/LEG aus Berichten wenn nicht relevant
- Hint/Action-Dedup: keine doppelten Sätze pro Card mehr
Aktiviert via env MAIL_RENDER_V2=true (Default: legacy renderer).
5 neue deterministische Findings als Phase D-2b/B4/B5/B6/B7/B8:
B4 vendor_consistency_check — Cross-Doc-Provider-Widerspruch
(Elli: DSE nennt Vertex AI für Chatbot, /de/cookies nennt Iadvize → HIGH).
6 Service-Types: chatbot/analytics/tag_manager/pixel/cdn/cmp.
B5 ai_act_transparency_check — AI Act Art. 50 Transparenzpflicht
(Elli: Vertex AI vorhanden ohne Pre-Chat-Disclosure → HIGH).
Plus B5-Erweiterung: Rechtsgrundlage Art-6-Abs-1-lit-f bei AI → MED
(Einwilligung empfehlen).
B6 cross_doc_dpo_check — DPO in DSE genannt, nicht im Impressum (LOW).
B7 doc_staleness_check — Datum-Extraktion aus DSE/AGB/Nutzungsbedingungen.
Cap: AGB/NB 3y, DSE 2y. Älter → MEDIUM (Elli NB Stand 2018 → HIGH).
B8 cmp_fingerprint_check — Banner detected, aber CMP-Provider generic
(kein Usercentrics/OneTrust/Cookiebot/etc → MED).
B3-Erweiterung detect_intra_doc_contradictions — Widersprüchliche
Speicherdauer im SELBEN Doc (Elli: Logfile 7d vs 30d → HIGH).
LLM-Plausibility-Phase (Phase D-2b, finding_plausibility_check.py):
- Läuft AFTER MC pipeline, BEFORE D3 render
- Prompt mit Beispiel-IDs + 3-Phase-Mapping: exact-ID / position-fallback /
fuzzy-tail-match
- Stempelt llm_title / llm_severity / llm_recommendation / llm_drop auf
jeden FAIL CheckItem
- V2-Render zeigt "🤖 LLM-Plausibility:" Box pro Finding wenn gestempelt
- KNOWN ISSUE: qwen3:30b-a3b liefert oft empty content auf format='json' +
8000-char-excerpt prompts. Pipeline läuft mit stamped=0 weiter. Task #16.
Coverage gegen Elli Ground Truth (zeroclaw/docs/ground-truth/elli_eco_2026-06-06.json,
13 expected findings via WebFetch-Agent-Crawl):
- 4/4 HIGH-Findings ✓ (COOKIE-CONSENT-UX-001 + WIDERRUFSBELEHRUNG-001 +
VENDOR-CONSISTENCY-001 + AI-ACT-TRANSPARENCY-001)
- 4/6 MEDIUM ✓
- 2/3 LOW ✓
- Total: 10/13 = 77% (Sprung von 4/13 = 31%)
Restliche 3 Gaps als Task #17: IMPRESSUM-001 (multi-entity USt-IdNr),
TRANSFER-001 (Vendor-Mechanismus DPF/SCC), TH-RETENTION-002 (AI-Retention
pro Datenkategorie).
V2-Mail-Preview in Mailpit: 'v2all@local.test' Subject '[V2 ALL] ELLI'.
Backend healthy, B1+B3+B4+B5+B6+B7+B8 alle live im Orchestrator.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
"""B5 wiring — AI-Act Art. 50 Transparenzpflicht-Check + HTML block.
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Runs after B4 (vendor-consistency). Deterministic detection of
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AI-Provider mentions + disclosure-phrase mentions. When an AI is
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present but no Art-50-disclosure → HIGH finding; when both present
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the renderer flags MEDIUM/manual-review because the LIVE pre-chat
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UI hint cannot be verified without a consent-tester DOM scan.
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"""
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from __future__ import annotations
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import html
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import logging
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from compliance.services.ai_act_transparency_check import (
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check_ai_act_transparency,
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)
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logger = logging.getLogger(__name__)
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def run_b5(state: dict) -> None:
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findings = check_ai_act_transparency(state)
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state["ai_act_findings"] = findings
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if not findings:
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return
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state["ai_act_html"] = _render(findings)
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logger.info(
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"B5 AI-Act: %d findings (HIGH=%d, MEDIUM=%d)",
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len(findings),
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sum(1 for f in findings if (f.get("severity") or "") == "HIGH"),
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sum(1 for f in findings if (f.get("severity") or "") == "MEDIUM"),
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)
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def _render(findings: list[dict]) -> str:
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cards = []
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for f in findings:
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sev = (f.get("severity") or "").upper()
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color = "#dc2626" if sev == "HIGH" else "#f59e0b"
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vendors_html = ""
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if f.get("ai_vendors"):
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chips = "".join(
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f"<span style='display:inline-block;background:#f1f5f9;"
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f"padding:2px 8px;border-radius:999px;margin:2px 4px 2px 0;"
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f"font-size:11px;'>{html.escape(v.get('vendor') or '—')}</span>"
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for v in f["ai_vendors"]
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)
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vendors_html = (
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"<div style='margin-top:6px;font-size:13px;'>"
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f"<strong>Erkannte AI-Vendors:</strong> {chips}</div>"
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)
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signals_html = (
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f"<div style='font-size:12px;color:#475569;margin-top:6px;'>"
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f"<em>{html.escape(f.get('detected_signals') or '')}</em></div>"
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)
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cards.append(
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f"<div style='margin:12px 0;padding:14px;background:#fff;"
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f"border-left:3px solid {color};border-radius:4px;'>"
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f"<div style='font-weight:600;color:{color};font-size:14px;'>"
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f"{sev} · {html.escape(f.get('check_id') or '')}</div>"
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f"<div style='font-size:14px;margin-top:4px;'>"
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f"<strong>{html.escape(f.get('title') or '')}</strong></div>"
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f"<div style='font-size:12px;color:#64748b;margin-top:2px;'>"
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f"{html.escape(f.get('norm') or '')}</div>"
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f"{vendors_html}{signals_html}"
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f"<div style='font-size:13px;margin-top:8px;background:#dcfce7;"
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f"padding:8px 10px;border-radius:4px;'>"
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f"<strong>→ Empfehlung:</strong> "
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f"{html.escape(f.get('action') or '')}</div>"
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"</div>"
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)
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return (
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"<div style='margin:24px 0;padding:16px;border-left:4px solid #dc2626;"
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"background:#fef2f2;border-radius:4px;'>"
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"<h2 style='margin:0 0 8px;color:#991b1b;font-size:16px;'>"
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"🤖 AI-Act Art. 50 — Transparenzpflicht KI-Interaktion"
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"</h2>"
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+ "".join(cards) +
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"</div>"
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)
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