fix: Add missing service modules required by agent_scan_routes
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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>
This commit is contained in:
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"""
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DSE Matcher — matches detected services against DSE sections and
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generates TextReferences with original text, position, and corrections.
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"""
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import logging
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import re
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from dataclasses import dataclass
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from compliance.services.dse_parser import DSESection, find_section_by_content, find_section_by_category
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logger = logging.getLogger(__name__)
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# Category → typical DSE section heading keywords
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CATEGORY_SECTION_MAP = {
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"tracking": ["cookie", "tracking", "webanalyse", "analytics", "statistik", "reichweitenmessung"],
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"marketing": ["marketing", "werbung", "newsletter", "remarketing", "werbe"],
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"payment": ["zahlung", "payment", "bezahl", "zahlungsabwicklung", "zahlungsdienst"],
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"chatbot": ["chat", "kommunikation", "kundenservice", "kontakt", "livechat"],
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"cdn": ["hosting", "bereitstellung", "technisch", "infrastruktur", "content delivery"],
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"other": ["sonstig", "weitere", "dritte", "extern", "dienstleister"],
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}
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@dataclass
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class TextReference:
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"""Reference to a specific text block in the DSE."""
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found: bool
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source_url: str = ""
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document_type: str = "Datenschutzerklaerung"
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section_heading: str = ""
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section_number: str = ""
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parent_section: str = ""
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paragraph_index: int = 0
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original_text: str = ""
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issue: str = "" # "missing", "incomplete", "incorrect"
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correction_type: str = "" # "insert", "replace", "append"
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correction_text: str = ""
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insert_after: str = ""
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def match_service_to_dse(
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service_name: str,
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service_category: str,
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sections: list[DSESection],
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url: str = "",
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) -> TextReference:
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"""Find where a service is mentioned in the DSE and build a TextReference."""
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# Step 1: Search for exact service name
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section = find_section_by_content(sections, service_name)
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if section:
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# Found — extract the relevant paragraph
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original = _extract_relevant_paragraph(section.content, service_name)
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return TextReference(
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found=True,
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source_url=url,
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section_heading=section.heading,
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section_number=section.section_number,
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parent_section=section.parent_heading,
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paragraph_index=_find_paragraph_index(section.content, service_name),
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original_text=original,
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issue="", # Found and present — caller determines if complete
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)
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# Step 2: Search for provider name (e.g., "Google" for "Google Analytics")
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# But only if the provider name is specific enough — avoid "Google" matching YouTube
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provider = service_name.split()[0] if " " in service_name else service_name
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if len(provider) < 4 or provider.lower() in ("the", "a", "an"):
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provider = service_name # Too short/generic, use full name
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section = find_section_by_content(sections, provider)
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# Verify: the section must actually be about THIS service, not just mention the provider
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if section and provider.lower() != service_name.lower():
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# Check if the full service name or a close variant is in the section
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content_lower = section.content.lower()
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service_words = service_name.lower().split()
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# At least 2 words of the service name must match (not just "Google")
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matching_words = sum(1 for w in service_words if w in content_lower)
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if matching_words < 2 and service_name.lower() not in content_lower:
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section = None # False match — provider name found but wrong context
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if section:
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original = _extract_relevant_paragraph(section.content, provider)
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return TextReference(
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found=True,
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source_url=url,
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section_heading=section.heading,
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section_number=section.section_number,
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parent_section=section.parent_heading,
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paragraph_index=_find_paragraph_index(section.content, provider),
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original_text=original,
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issue="incomplete", # Provider mentioned but not specific service
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)
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# Step 3: Not found — suggest insertion point
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insert_section = find_section_by_category(sections, service_category)
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insert_after = insert_section.heading if insert_section else ""
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# If no category match, find the last "Cookies"/"Tracking" or "Sonstiges" section
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if not insert_after:
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for s in reversed(sections):
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h = s.heading.lower()
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if any(kw in h for kw in ["cookie", "datenschutz", "daten"]):
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insert_after = s.heading
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break
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return TextReference(
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found=False,
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source_url=url,
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document_type="Datenschutzerklaerung",
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issue="missing",
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correction_type="insert",
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insert_after=insert_after,
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)
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def build_text_references(
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detected_services: list[dict],
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dse_services: list[dict],
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sections: list[DSESection],
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url: str = "",
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) -> dict[str, TextReference]:
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"""Build TextReferences for all detected services.
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Returns dict: service_id → TextReference
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"""
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refs: dict[str, TextReference] = {}
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for svc in detected_services:
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service_id = svc.get("id", svc.get("name", ""))
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service_name = svc.get("name", "")
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category = svc.get("category", "other")
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ref = match_service_to_dse(service_name, category, sections, url)
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# Check if service is in the DSE SOLL list
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dse_match = _find_in_dse_list(service_name, dse_services)
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if ref.found and dse_match:
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ref.issue = "" # All good — documented and present
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elif ref.found and not dse_match:
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# Found in text but not in LLM extraction — still OK
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ref.issue = ""
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elif not ref.found:
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ref.issue = "missing"
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ref.correction_type = "insert"
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refs[service_id] = ref
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return refs
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def _extract_relevant_paragraph(content: str, search_term: str) -> str:
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"""Extract the paragraph containing the search term."""
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search_lower = search_term.lower()
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content_lower = content.lower()
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# Find position of search term
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pos = content_lower.find(search_lower)
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if pos == -1:
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return content[:300]
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# Find sentence/paragraph boundaries
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# Look backwards for paragraph break
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start = max(0, content.rfind(".", 0, pos))
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if start > 0:
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start += 2 # Skip ". "
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else:
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start = max(0, pos - 100)
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# Look forward for end of paragraph
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end = content.find(".", pos + len(search_term))
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if end == -1 or end - pos > 500:
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end = min(len(content), pos + 300)
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else:
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end += 1 # Include the period
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return content[start:end].strip()
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def _find_paragraph_index(content: str, search_term: str) -> int:
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"""Find which paragraph (1-based) contains the search term."""
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paragraphs = re.split(r"\n\n|\n(?=[A-Z])", content)
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search_lower = search_term.lower()
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for i, para in enumerate(paragraphs, 1):
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if search_lower in para.lower():
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return i
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return 0
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def _find_in_dse_list(service_name: str, dse_services: list[dict]) -> dict | None:
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"""Check if a service appears in the LLM-extracted DSE service list."""
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name_lower = service_name.lower()
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for svc in dse_services:
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dse_name = svc.get("name", "").lower()
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if name_lower in dse_name or dse_name in name_lower:
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return svc
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# Check first word (provider match)
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if name_lower.split()[0] in dse_name:
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return svc
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return None
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@@ -0,0 +1,224 @@
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"""
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DSE Parser — parses privacy policy HTML into structured sections.
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Extracts headings, section numbers, content blocks and builds a
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hierarchical structure that enables precise text references.
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"""
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import logging
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import re
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from dataclasses import dataclass, field
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from html.parser import HTMLParser
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logger = logging.getLogger(__name__)
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@dataclass
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class DSESection:
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"""A section in a privacy policy."""
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heading: str
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heading_level: int # 1-4
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section_number: str # "2.5" or "" if no number
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content: str # Plain text content
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html: str # Original HTML content
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parent_heading: str = ""
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url: str = ""
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element_id: str = ""
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paragraph_count: int = 0
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class _HeadingExtractor(HTMLParser):
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"""Extract headings and their content from HTML."""
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def __init__(self):
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super().__init__()
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self.sections: list[dict] = []
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self._current_tag = ""
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self._in_heading = False
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self._heading_level = 0
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self._heading_text = ""
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self._heading_id = ""
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self._content_parts: list[str] = []
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self._html_parts: list[str] = []
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self._skip_tags = {"script", "style", "nav", "footer", "header"}
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self._skip_depth = 0
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self._p_count = 0
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def handle_starttag(self, tag, attrs):
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attrs_dict = dict(attrs)
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if tag in self._skip_tags:
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self._skip_depth += 1
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return
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if self._skip_depth > 0:
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return
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if tag in ("h1", "h2", "h3", "h4"):
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# Save previous section
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if self._heading_text:
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self._save_section()
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self._in_heading = True
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self._heading_level = int(tag[1])
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self._heading_text = ""
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self._heading_id = attrs_dict.get("id", "")
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self._content_parts = []
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self._html_parts = []
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self._p_count = 0
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if tag == "p":
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self._p_count += 1
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# Reconstruct HTML
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attr_str = " ".join(f'{k}="{v}"' for k, v in attrs)
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self._html_parts.append(f"<{tag}{' ' + attr_str if attr_str else ''}>")
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def handle_endtag(self, tag):
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if tag in self._skip_tags and self._skip_depth > 0:
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self._skip_depth -= 1
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return
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if self._skip_depth > 0:
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return
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if tag in ("h1", "h2", "h3", "h4"):
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self._in_heading = False
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self._html_parts.append(f"</{tag}>")
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def handle_data(self, data):
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if self._skip_depth > 0:
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return
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if self._in_heading:
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self._heading_text += data.strip()
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else:
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self._content_parts.append(data)
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self._html_parts.append(data)
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def _save_section(self):
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if not self._heading_text:
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return
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content = " ".join(self._content_parts)
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content = re.sub(r"\s+", " ", content).strip()
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self.sections.append({
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"heading": self._heading_text.strip(),
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"heading_level": self._heading_level,
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"element_id": self._heading_id,
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"content": content,
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"html": "".join(self._html_parts),
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"paragraph_count": self._p_count,
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})
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def finalize(self):
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"""Call after feeding all data to save the last section."""
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if self._heading_text:
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self._save_section()
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def parse_dse(html: str, url: str = "") -> list[DSESection]:
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"""Parse privacy policy HTML into structured sections."""
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extractor = _HeadingExtractor()
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try:
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extractor.feed(html)
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extractor.finalize()
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except Exception as e:
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logger.warning("HTML parsing failed, falling back to regex: %s", e)
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return _regex_fallback(html, url)
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if not extractor.sections:
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return _regex_fallback(html, url)
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# Build parent hierarchy
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sections: list[DSESection] = []
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parent_stack: list[str] = [""] # Stack of parent headings by level
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for raw in extractor.sections:
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heading = raw["heading"]
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level = raw["heading_level"]
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# Extract section number (e.g., "2.5" from "2.5 Webanalyse")
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num_match = re.match(r"^(\d+(?:\.\d+)*)\s*[.:]?\s*", heading)
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section_number = num_match.group(1) if num_match else ""
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||||||
|
|
||||||
|
# Track parent headings
|
||||||
|
while len(parent_stack) > level:
|
||||||
|
parent_stack.pop()
|
||||||
|
parent = parent_stack[-1] if parent_stack else ""
|
||||||
|
parent_stack.append(heading)
|
||||||
|
|
||||||
|
sections.append(DSESection(
|
||||||
|
heading=heading,
|
||||||
|
heading_level=level,
|
||||||
|
section_number=section_number,
|
||||||
|
content=raw["content"][:2000], # Cap content length
|
||||||
|
html=raw["html"][:3000],
|
||||||
|
parent_heading=parent,
|
||||||
|
url=url,
|
||||||
|
element_id=raw["element_id"],
|
||||||
|
paragraph_count=raw["paragraph_count"],
|
||||||
|
))
|
||||||
|
|
||||||
|
logger.info("Parsed DSE: %d sections from %s", len(sections), url)
|
||||||
|
return sections
|
||||||
|
|
||||||
|
|
||||||
|
def _regex_fallback(html: str, url: str) -> list[DSESection]:
|
||||||
|
"""Fallback parser using regex when HTML parsing fails."""
|
||||||
|
# Strip scripts and styles
|
||||||
|
clean = re.sub(r"<(script|style)[^>]*>.*?</\1>", "", html, flags=re.DOTALL | re.IGNORECASE)
|
||||||
|
|
||||||
|
sections = []
|
||||||
|
# Find all headings
|
||||||
|
for match in re.finditer(r"<h([1-4])[^>]*(?:id=[\"']([^\"']*)[\"'])?[^>]*>(.*?)</h\1>", clean, re.DOTALL | re.IGNORECASE):
|
||||||
|
level = int(match.group(1))
|
||||||
|
elem_id = match.group(2) or ""
|
||||||
|
heading = re.sub(r"<[^>]+>", "", match.group(3)).strip()
|
||||||
|
|
||||||
|
# Get content until next heading
|
||||||
|
start = match.end()
|
||||||
|
next_heading = re.search(r"<h[1-4]", clean[start:], re.IGNORECASE)
|
||||||
|
end = start + next_heading.start() if next_heading else start + 2000
|
||||||
|
content = clean[start:end]
|
||||||
|
content = re.sub(r"<[^>]+>", " ", content)
|
||||||
|
content = re.sub(r"\s+", " ", content).strip()
|
||||||
|
|
||||||
|
num_match = re.match(r"^(\d+(?:\.\d+)*)", heading)
|
||||||
|
|
||||||
|
sections.append(DSESection(
|
||||||
|
heading=heading,
|
||||||
|
heading_level=level,
|
||||||
|
section_number=num_match.group(1) if num_match else "",
|
||||||
|
content=content[:2000],
|
||||||
|
html="",
|
||||||
|
url=url,
|
||||||
|
element_id=elem_id,
|
||||||
|
))
|
||||||
|
|
||||||
|
return sections
|
||||||
|
|
||||||
|
|
||||||
|
def find_section_by_content(sections: list[DSESection], search_text: str) -> DSESection | None:
|
||||||
|
"""Find the section that contains specific text."""
|
||||||
|
search_lower = search_text.lower()
|
||||||
|
for section in sections:
|
||||||
|
if search_lower in section.content.lower():
|
||||||
|
return section
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
def find_section_by_category(sections: list[DSESection], category: str) -> DSESection | None:
|
||||||
|
"""Find the section most likely to contain a service category."""
|
||||||
|
category_keywords = {
|
||||||
|
"tracking": ["cookie", "tracking", "webanalyse", "analytics", "statistik"],
|
||||||
|
"marketing": ["marketing", "werbung", "newsletter", "remarketing"],
|
||||||
|
"payment": ["zahlung", "payment", "bezahlung", "zahlungsabwicklung"],
|
||||||
|
"chatbot": ["chat", "kommunikation", "kundenservice", "kontakt"],
|
||||||
|
"cdn": ["hosting", "bereitstellung", "technisch", "infrastruktur", "cdn"],
|
||||||
|
"other": ["sonstig", "weitere", "dritte", "extern"],
|
||||||
|
}
|
||||||
|
keywords = category_keywords.get(category, category_keywords["other"])
|
||||||
|
|
||||||
|
for section in sections:
|
||||||
|
heading_lower = section.heading.lower()
|
||||||
|
content_lower = section.content.lower()[:500]
|
||||||
|
for kw in keywords:
|
||||||
|
if kw in heading_lower or kw in content_lower:
|
||||||
|
return section
|
||||||
|
return None
|
||||||
@@ -0,0 +1,179 @@
|
|||||||
|
"""
|
||||||
|
Legal Basis Validator — checks if the correct DSGVO legal basis (lit. a-f)
|
||||||
|
is used for each processing purpose in the privacy policy.
|
||||||
|
|
||||||
|
⚠️ TECHNISCHE SCHULD / HARDCODED KNOWLEDGE:
|
||||||
|
Dieses Modul enthält hartkodierte Rechtsgrundlagen-Zuordnungen (CORRECT_BASIS dict).
|
||||||
|
Das ist ein TEMPORAERER Fallback bis die Control Library entsprechende Controls hat.
|
||||||
|
|
||||||
|
MITTELFRISTIGES ZIEL: Dieses Dict durch RAG/Control-Library-Abfragen ersetzen.
|
||||||
|
Neue Controls sollten in der Pipeline generiert werden, z.B.:
|
||||||
|
"Cookie-Tracking erfordert Art. 6(1)(a) Einwilligung (EuGH C-673/17 Planet49)"
|
||||||
|
→ canonical_controls mit scope_conditions + legal_ref
|
||||||
|
|
||||||
|
BIS DAHIN: Dieses Dict wird als Fallback genutzt mit einem Warning-Log wenn
|
||||||
|
es herangezogen wird. Bei jedem neuen Gesetz/Urteil muss SOWOHL die Pipeline
|
||||||
|
als auch dieses Dict aktualisiert werden — oder besser: das Dict entfernen und
|
||||||
|
nur noch Controls nutzen.
|
||||||
|
|
||||||
|
Erstellt: 2026-04-29 | Review-Datum: 2026-07-01 | Owner: Agent-Team
|
||||||
|
|
||||||
|
Common mistakes detected:
|
||||||
|
- Cookie tracking on lit. f (legitimate interest) instead of lit. a (consent)
|
||||||
|
- Marketing emails on lit. f instead of lit. a
|
||||||
|
- Analytics on lit. b (contract) — incorrect overextension
|
||||||
|
- Klarna credit check without Art. 22 reference
|
||||||
|
"""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import re
|
||||||
|
from dataclasses import dataclass
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class LitFinding:
|
||||||
|
purpose: str
|
||||||
|
stated_basis: str
|
||||||
|
correct_basis: str
|
||||||
|
severity: str
|
||||||
|
text: str
|
||||||
|
legal_ref: str
|
||||||
|
original_text: str = ""
|
||||||
|
|
||||||
|
|
||||||
|
# Purpose → correct legal basis mapping
|
||||||
|
# Based on: DSK Kurzpapiere, Planet49 (EuGH C-673/17), BGH Cookie-Urteil
|
||||||
|
CORRECT_BASIS: dict[str, dict] = {
|
||||||
|
"cookie_tracking": {
|
||||||
|
"correct": "lit. a (Einwilligung)",
|
||||||
|
"wrong_patterns": ["berechtigtes interesse", "lit. f", "lit.f", "legitimate interest"],
|
||||||
|
"detect_patterns": ["cookie", "tracking", "pixel", "analytics.*cookie"],
|
||||||
|
"ref": "EuGH C-673/17 (Planet49), §25 TDDDG",
|
||||||
|
},
|
||||||
|
"web_analytics": {
|
||||||
|
"correct": "lit. a (Einwilligung)",
|
||||||
|
"wrong_patterns": ["berechtigtes interesse", "lit. f", "lit.f", "vertragserfuellung", "lit. b", "lit.b"],
|
||||||
|
"detect_patterns": ["google analytics", "webanalyse", "web analytics", "reichweitenmessung",
|
||||||
|
"nutzungsanalyse", "hotjar", "matomo"],
|
||||||
|
"ref": "DSK Orientierungshilfe Telemedien, §25 TDDDG",
|
||||||
|
},
|
||||||
|
"marketing_email": {
|
||||||
|
"correct": "lit. a (Einwilligung)",
|
||||||
|
"wrong_patterns": ["berechtigtes interesse", "lit. f", "lit.f"],
|
||||||
|
"detect_patterns": ["newsletter", "marketing.*mail", "werbe.*mail", "werbe.*email",
|
||||||
|
"marketing.*email", "werbliche.*kommunikation"],
|
||||||
|
"ref": "Art. 7 DSGVO, §7 UWG (Double Opt-In)",
|
||||||
|
},
|
||||||
|
"remarketing": {
|
||||||
|
"correct": "lit. a (Einwilligung)",
|
||||||
|
"wrong_patterns": ["berechtigtes interesse", "lit. f", "lit.f"],
|
||||||
|
"detect_patterns": ["remarketing", "retargeting", "personalisierte werbung",
|
||||||
|
"personalized advertising", "custom audience"],
|
||||||
|
"ref": "§25 TDDDG, EuGH C-673/17",
|
||||||
|
},
|
||||||
|
"credit_check": {
|
||||||
|
"correct": "lit. b/f + Art. 22 DSGVO Hinweis",
|
||||||
|
"wrong_patterns": [], # Not about wrong basis, but missing Art. 22
|
||||||
|
"detect_patterns": ["bonitaet", "bonität", "kreditprüfung", "kreditpruefung",
|
||||||
|
"schufa", "auskunftei", "klarna.*rechnung", "ratenzahlung"],
|
||||||
|
"ref": "Art. 22 DSGVO (automatisierte Einzelentscheidung)",
|
||||||
|
"must_contain": ["art. 22", "art.22", "automatisierte entscheidung",
|
||||||
|
"automated decision", "einzelentscheidung"],
|
||||||
|
},
|
||||||
|
"social_media_embed": {
|
||||||
|
"correct": "lit. a (Einwilligung)",
|
||||||
|
"wrong_patterns": ["berechtigtes interesse", "lit. f", "lit.f"],
|
||||||
|
"detect_patterns": ["facebook.*plugin", "social.*plugin", "like.*button",
|
||||||
|
"share.*button", "instagram.*embed", "twitter.*embed"],
|
||||||
|
"ref": "EuGH C-40/17 (Fashion ID), 2-Klick-Loesung",
|
||||||
|
},
|
||||||
|
"session_recording": {
|
||||||
|
"correct": "lit. a (Einwilligung)",
|
||||||
|
"wrong_patterns": ["berechtigtes interesse", "lit. f", "lit.f"],
|
||||||
|
"detect_patterns": ["session.?recording", "session.?replay", "heatmap",
|
||||||
|
"mouseflow", "hotjar.*recording", "clarity.*recording",
|
||||||
|
"fullstory", "lucky orange"],
|
||||||
|
"ref": "§25 TDDDG, Aufzeichnung von Nutzerverhalten",
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def validate_legal_bases(dse_text: str) -> list[LitFinding]:
|
||||||
|
"""Check if correct legal bases are used in the privacy policy.
|
||||||
|
|
||||||
|
⚠️ Uses HARDCODED CORRECT_BASIS dict as fallback.
|
||||||
|
TODO: Replace with RAG/Control Library query when lit-mapping Controls exist.
|
||||||
|
"""
|
||||||
|
logger.warning(
|
||||||
|
"legal_basis_validator: Using HARDCODED rules (CORRECT_BASIS dict). "
|
||||||
|
"This should be replaced with Control Library queries. Review date: 2026-07-01"
|
||||||
|
)
|
||||||
|
findings = []
|
||||||
|
text_lower = dse_text.lower()
|
||||||
|
|
||||||
|
for purpose_id, rules in CORRECT_BASIS.items():
|
||||||
|
# Step 1: Is this purpose mentioned in the DSE?
|
||||||
|
purpose_found = False
|
||||||
|
matched_text = ""
|
||||||
|
for pattern in rules["detect_patterns"]:
|
||||||
|
match = re.search(pattern, text_lower)
|
||||||
|
if match:
|
||||||
|
purpose_found = True
|
||||||
|
# Extract surrounding context (200 chars)
|
||||||
|
start = max(0, match.start() - 100)
|
||||||
|
end = min(len(text_lower), match.end() + 200)
|
||||||
|
matched_text = dse_text[start:end].strip()
|
||||||
|
break
|
||||||
|
|
||||||
|
if not purpose_found:
|
||||||
|
continue
|
||||||
|
|
||||||
|
context_lower = matched_text.lower()
|
||||||
|
|
||||||
|
# Step 2: Check if wrong legal basis is stated
|
||||||
|
for wrong in rules["wrong_patterns"]:
|
||||||
|
if wrong in context_lower:
|
||||||
|
findings.append(LitFinding(
|
||||||
|
purpose=purpose_id,
|
||||||
|
stated_basis=wrong,
|
||||||
|
correct_basis=rules["correct"],
|
||||||
|
severity="HIGH",
|
||||||
|
text=f"Falsche Rechtsgrundlage: '{_purpose_label(purpose_id)}' nutzt "
|
||||||
|
f"'{wrong}' statt '{rules['correct']}'",
|
||||||
|
legal_ref=rules["ref"],
|
||||||
|
original_text=matched_text[:300],
|
||||||
|
))
|
||||||
|
break
|
||||||
|
|
||||||
|
# Step 3: Special check — must_contain (e.g., Art. 22 for credit checks)
|
||||||
|
if "must_contain" in rules:
|
||||||
|
has_required = any(req in context_lower for req in rules["must_contain"])
|
||||||
|
if not has_required:
|
||||||
|
findings.append(LitFinding(
|
||||||
|
purpose=purpose_id,
|
||||||
|
stated_basis="(fehlt)",
|
||||||
|
correct_basis=rules["correct"],
|
||||||
|
severity="HIGH",
|
||||||
|
text=f"Pflichthinweis fehlt: '{_purpose_label(purpose_id)}' erwaehnt "
|
||||||
|
f"keine automatisierte Entscheidungsfindung ({rules['ref']})",
|
||||||
|
legal_ref=rules["ref"],
|
||||||
|
original_text=matched_text[:300],
|
||||||
|
))
|
||||||
|
|
||||||
|
return findings
|
||||||
|
|
||||||
|
|
||||||
|
def _purpose_label(purpose_id: str) -> str:
|
||||||
|
"""German label for purpose ID."""
|
||||||
|
labels = {
|
||||||
|
"cookie_tracking": "Cookie-Tracking",
|
||||||
|
"web_analytics": "Webanalyse",
|
||||||
|
"marketing_email": "Marketing-Emails/Newsletter",
|
||||||
|
"remarketing": "Remarketing/Retargeting",
|
||||||
|
"credit_check": "Bonitaetspruefung",
|
||||||
|
"social_media_embed": "Social Media Einbindung",
|
||||||
|
"session_recording": "Session Recording/Heatmaps",
|
||||||
|
}
|
||||||
|
return labels.get(purpose_id, purpose_id)
|
||||||
@@ -0,0 +1,302 @@
|
|||||||
|
"""
|
||||||
|
Mandatory Content Checker — verifies that legally required content
|
||||||
|
is present on a website. Checks for missing documents, sections,
|
||||||
|
and mandatory information within documents.
|
||||||
|
|
||||||
|
Knows what MUST be there (not just what IS there).
|
||||||
|
"""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import re
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
|
||||||
|
from compliance.services.dse_parser import DSESection
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class MandatoryFinding:
|
||||||
|
code: str
|
||||||
|
severity: str # "HIGH", "MEDIUM", "LOW"
|
||||||
|
category: str # "document_missing", "section_missing", "info_missing"
|
||||||
|
text: str
|
||||||
|
legal_ref: str
|
||||||
|
expected: str # What should be there
|
||||||
|
suggestion: str = "" # How to fix
|
||||||
|
|
||||||
|
|
||||||
|
# ═══════════════════════════════════════════════════════════════
|
||||||
|
# MANDATORY DOCUMENTS (must exist as pages/links on the website)
|
||||||
|
# ═══════════════════════════════════════════════════════════════
|
||||||
|
|
||||||
|
MANDATORY_DOCUMENTS = [
|
||||||
|
{
|
||||||
|
"id": "impressum",
|
||||||
|
"name": "Impressum",
|
||||||
|
"legal_ref": "§5 TMG, §18 MStV",
|
||||||
|
"patterns": [r"impressum", r"imprint", r"legal.?notice"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "datenschutz",
|
||||||
|
"name": "Datenschutzerklaerung",
|
||||||
|
"legal_ref": "Art. 13/14 DSGVO",
|
||||||
|
"patterns": [r"datenschutz", r"privacy", r"dsgvo"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "agb",
|
||||||
|
"name": "AGB / Nutzungsbedingungen",
|
||||||
|
"legal_ref": "§305 BGB (bei Vertragsschluss)",
|
||||||
|
"patterns": [r"agb", r"nutzungsbedingung", r"terms"],
|
||||||
|
"severity": "MEDIUM",
|
||||||
|
"only_ecommerce": True, # Nur bei Shops/Buchungsseiten
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "widerruf",
|
||||||
|
"name": "Widerrufsbelehrung",
|
||||||
|
"legal_ref": "§355 BGB, Art. 246a §1 EGBGB (nur Fernabsatz)",
|
||||||
|
"patterns": [r"widerruf", r"cancellation.?policy", r"right.?of.?withdrawal"],
|
||||||
|
"severity": "MEDIUM",
|
||||||
|
"only_ecommerce": True, # Nur bei Fernabsatzvertraegen
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
# ═══════════════════════════════════════════════════════════════
|
||||||
|
# MANDATORY DSE SECTIONS (Art. 13 DSGVO Pflichtangaben)
|
||||||
|
# ═══════════════════════════════════════════════════════════════
|
||||||
|
|
||||||
|
MANDATORY_DSE_CONTENT = [
|
||||||
|
{
|
||||||
|
"id": "verantwortlicher",
|
||||||
|
"name": "Name und Kontakt des Verantwortlichen",
|
||||||
|
"legal_ref": "Art. 13 Abs. 1 lit. a DSGVO",
|
||||||
|
"keywords": ["verantwortlich", "responsible", "controller", "betreiber"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "dsb_kontakt",
|
||||||
|
"name": "Kontaktdaten des Datenschutzbeauftragten",
|
||||||
|
"legal_ref": "Art. 13 Abs. 1 lit. b DSGVO",
|
||||||
|
"keywords": ["datenschutzbeauftragt", "data protection officer", "dsb", "dpo",
|
||||||
|
"behördlichen datenschutz", "behoerdlichen datenschutz",
|
||||||
|
"datenschutz@", "datenschutzbeauftragter"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "zwecke",
|
||||||
|
"name": "Zwecke der Datenverarbeitung",
|
||||||
|
"legal_ref": "Art. 13 Abs. 1 lit. c DSGVO",
|
||||||
|
"keywords": ["zweck", "purpose", "verarbeitungszweck", "verarbeitungszwecke",
|
||||||
|
"wozu", "wofuer", "zu welchem zweck", "nutzungszweck",
|
||||||
|
"zweck und rechtsgrundlage", "zwecke der verarbeitung"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "rechtsgrundlage",
|
||||||
|
"name": "Rechtsgrundlagen der Verarbeitung",
|
||||||
|
"legal_ref": "Art. 13 Abs. 1 lit. c DSGVO",
|
||||||
|
"keywords": ["rechtsgrundlage", "legal basis", "art. 6", "art.6",
|
||||||
|
"berechtigtes interesse", "einwilligung", "vertragserfuellung",
|
||||||
|
"vertragserfüllung", "rechtliche verpflichtung"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "speicherdauer",
|
||||||
|
"name": "Speicherdauer / Loeschfristen",
|
||||||
|
"legal_ref": "Art. 13 Abs. 2 lit. a DSGVO",
|
||||||
|
"keywords": ["speicherdauer", "aufbewahrung", "loeschung", "loeschfrist",
|
||||||
|
"storage period", "retention", "deletion"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "betroffenenrechte",
|
||||||
|
"name": "Betroffenenrechte (Auskunft, Loeschung, etc.)",
|
||||||
|
"legal_ref": "Art. 13 Abs. 2 lit. b-d DSGVO",
|
||||||
|
"keywords": ["betroffenenrecht", "auskunft", "berichtigung", "loeschung",
|
||||||
|
"einschraenkung", "widerspruch", "data subject rights",
|
||||||
|
"right to access", "right to erasure"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "beschwerderecht",
|
||||||
|
"name": "Beschwerderecht bei Aufsichtsbehoerde",
|
||||||
|
"legal_ref": "Art. 13 Abs. 2 lit. d DSGVO",
|
||||||
|
"keywords": ["aufsichtsbehoerde", "aufsichtsbehörde", "beschwerde",
|
||||||
|
"supervisory authority", "datenschutzbehoerde",
|
||||||
|
"landesbeauftragte", "bundesdatenschutz", "bfdi"],
|
||||||
|
"severity": "MEDIUM",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "drittlandtransfer",
|
||||||
|
"name": "Drittlandtransfer-Information",
|
||||||
|
"legal_ref": "Art. 13 Abs. 1 lit. f DSGVO",
|
||||||
|
"keywords": ["drittland", "drittst", "third countr", "usa", "transfer",
|
||||||
|
"standardvertragsklausel", "adequacy"],
|
||||||
|
"severity": "MEDIUM",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "automatisierte_entscheidung",
|
||||||
|
"name": "Automatisierte Entscheidungsfindung / Profiling",
|
||||||
|
"legal_ref": "Art. 13 Abs. 2 lit. f DSGVO",
|
||||||
|
"keywords": ["automatisiert", "profiling", "automated decision", "scoring"],
|
||||||
|
"severity": "MEDIUM",
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
# ═══════════════════════════════════════════════════════════════
|
||||||
|
# MANDATORY IMPRESSUM CONTENT (§5 TMG)
|
||||||
|
# ═══════════════════════════════════════════════════════════════
|
||||||
|
|
||||||
|
MANDATORY_IMPRESSUM_CONTENT = [
|
||||||
|
{
|
||||||
|
"id": "geschaeftsfuehrer",
|
||||||
|
"name": "Geschaeftsfuehrer / Vertretungsberechtigter",
|
||||||
|
"legal_ref": "§5 Abs. 1 Nr. 1 TMG",
|
||||||
|
"keywords": ["geschaeftsfuehrer", "geschäftsführer", "ceo", "managing director",
|
||||||
|
"vertretungsberechtig", "vorstand"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "handelsregister",
|
||||||
|
"name": "Handelsregisternummer",
|
||||||
|
"legal_ref": "§5 Abs. 1 Nr. 4 TMG",
|
||||||
|
"keywords": ["handelsregister", "hrb", "hra", "amtsgericht", "registergericht",
|
||||||
|
"commercial register"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "ust_id",
|
||||||
|
"name": "Umsatzsteuer-Identifikationsnummer",
|
||||||
|
"legal_ref": "§5 Abs. 1 Nr. 6 TMG",
|
||||||
|
"keywords": ["ust-id", "ust.-id", "umsatzsteuer", "vat", "de\\d{9}"],
|
||||||
|
"severity": "MEDIUM",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "anschrift",
|
||||||
|
"name": "Anschrift (Strasse, PLZ, Ort)",
|
||||||
|
"legal_ref": "§5 Abs. 1 Nr. 1 TMG",
|
||||||
|
"keywords": ["str.", "straße", "strasse", "plz", "postleitzahl"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "kontakt",
|
||||||
|
"name": "Kontaktmoeglichkeit (Email oder Telefon)",
|
||||||
|
"legal_ref": "§5 Abs. 1 Nr. 2 TMG",
|
||||||
|
"keywords": ["@", "telefon", "phone", "e-mail", "email", "kontakt"],
|
||||||
|
"severity": "HIGH",
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
ECOMMERCE_INDICATORS = [
|
||||||
|
r"warenkorb", r"cart", r"shop", r"bestell", r"order",
|
||||||
|
r"checkout", r"kasse", r"buy", r"kaufen", r"add.?to.?cart",
|
||||||
|
r"stripe|paypal|klarna|mollie|adyen", # Payment providers
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def _is_ecommerce(scanned_pages: list[str], html_content: str = "") -> bool:
|
||||||
|
"""Detect if website is an e-commerce/transactional site."""
|
||||||
|
all_text = " ".join(scanned_pages).lower() + " " + html_content.lower()
|
||||||
|
return any(re.search(p, all_text) for p in ECOMMERCE_INDICATORS)
|
||||||
|
|
||||||
|
|
||||||
|
def check_mandatory_documents(
|
||||||
|
scanned_pages: list[str], page_status: dict[str, int],
|
||||||
|
html_content: str = "",
|
||||||
|
) -> list[MandatoryFinding]:
|
||||||
|
"""Check if mandatory documents/pages exist on the website."""
|
||||||
|
findings = []
|
||||||
|
is_shop = _is_ecommerce(scanned_pages, html_content)
|
||||||
|
|
||||||
|
for doc in MANDATORY_DOCUMENTS:
|
||||||
|
# Skip e-commerce-only checks for non-shop websites
|
||||||
|
if doc.get("only_ecommerce") and not is_shop:
|
||||||
|
continue
|
||||||
|
|
||||||
|
found = False
|
||||||
|
for page in scanned_pages:
|
||||||
|
if any(re.search(p, page, re.IGNORECASE) for p in doc["patterns"]):
|
||||||
|
status = page_status.get(page, 200)
|
||||||
|
if status < 400:
|
||||||
|
found = True
|
||||||
|
else:
|
||||||
|
findings.append(MandatoryFinding(
|
||||||
|
code=f"DOC-ERROR-{doc['id'].upper()}",
|
||||||
|
severity="HIGH",
|
||||||
|
category="document_error",
|
||||||
|
text=f"{doc['name']} existiert aber gibt HTTP {status} zurueck (Ladefehler!)",
|
||||||
|
legal_ref=doc["legal_ref"],
|
||||||
|
expected=doc["name"],
|
||||||
|
suggestion=f"Seite {page} ist nicht erreichbar. Pruefen ob ein Deployment-Fehler vorliegt.",
|
||||||
|
))
|
||||||
|
found = True # Exists but broken
|
||||||
|
break
|
||||||
|
|
||||||
|
if not found:
|
||||||
|
findings.append(MandatoryFinding(
|
||||||
|
code=f"DOC-MISSING-{doc['id'].upper()}",
|
||||||
|
severity=doc["severity"],
|
||||||
|
category="document_missing",
|
||||||
|
text=f"{doc['name']} nicht auf der Website gefunden ({doc['legal_ref']})",
|
||||||
|
legal_ref=doc["legal_ref"],
|
||||||
|
expected=f"Link zu {doc['name']} muss von jeder Seite erreichbar sein",
|
||||||
|
))
|
||||||
|
|
||||||
|
return findings
|
||||||
|
|
||||||
|
|
||||||
|
def check_dse_mandatory_content(
|
||||||
|
sections: list[DSESection], full_text: str,
|
||||||
|
) -> list[MandatoryFinding]:
|
||||||
|
"""Check if privacy policy contains all mandatory sections per Art. 13 DSGVO."""
|
||||||
|
findings = []
|
||||||
|
text_lower = full_text.lower()
|
||||||
|
|
||||||
|
for req in MANDATORY_DSE_CONTENT:
|
||||||
|
found = any(kw in text_lower for kw in req["keywords"])
|
||||||
|
if not found:
|
||||||
|
# Also check section headings
|
||||||
|
found = any(
|
||||||
|
any(kw in s.heading.lower() or kw in s.content.lower()[:200]
|
||||||
|
for kw in req["keywords"])
|
||||||
|
for s in sections
|
||||||
|
)
|
||||||
|
|
||||||
|
if not found:
|
||||||
|
findings.append(MandatoryFinding(
|
||||||
|
code=f"DSE-CONTENT-{req['id'].upper()}",
|
||||||
|
severity=req["severity"],
|
||||||
|
category="section_missing",
|
||||||
|
text=f"Pflichtangabe fehlt: {req['name']} ({req['legal_ref']})",
|
||||||
|
legal_ref=req["legal_ref"],
|
||||||
|
expected=req["name"],
|
||||||
|
))
|
||||||
|
|
||||||
|
return findings
|
||||||
|
|
||||||
|
|
||||||
|
def check_impressum_mandatory_content(
|
||||||
|
impressum_text: str,
|
||||||
|
) -> list[MandatoryFinding]:
|
||||||
|
"""Check if Impressum contains all mandatory info per §5 TMG."""
|
||||||
|
findings = []
|
||||||
|
text_lower = impressum_text.lower()
|
||||||
|
|
||||||
|
for req in MANDATORY_IMPRESSUM_CONTENT:
|
||||||
|
found = any(re.search(kw, text_lower) for kw in req["keywords"])
|
||||||
|
if not found:
|
||||||
|
findings.append(MandatoryFinding(
|
||||||
|
code=f"IMP-CONTENT-{req['id'].upper()}",
|
||||||
|
severity=req["severity"],
|
||||||
|
category="info_missing",
|
||||||
|
text=f"Impressum: {req['name']} fehlt ({req['legal_ref']})",
|
||||||
|
legal_ref=req["legal_ref"],
|
||||||
|
expected=req["name"],
|
||||||
|
))
|
||||||
|
|
||||||
|
return findings
|
||||||
Reference in New Issue
Block a user