Benjamin Admin
66be23f0c4
The first multi-regulation pattern: each capability declares `covers_targets`, so we can answer the convergence USP — "which capability satisfies CRA AND MaschinenVO at once?" - knowledge: transition_pattern_iso27001_to_cra_maschinenvo_v1.yaml (pattern_type: regulatory_convergence, status draft). The cyber-safety bridge = MaschinenVO Annex III 1.1.9 "protection against corruption" overlapping CRA integrity. 4 convergence capabilities cover BOTH; 5 CRA-only; 3 MaschinenVO-only. - product: compliance/transition_reasoning/convergence.py — regulatory_convergence() pure/deterministic/computed-not-stored, no new graph/class (freeze v1.0 untouched). No app caller yet -> non-runtime, no deploy (ADR-001). - reference suite: Cross-Regulation Capability Mapping section renders the customer sentence "von N neuen Massnahmen erfuellen M gleichzeitig CRA und MaschinenVO". - README: term -> Regulatory Transition / Convergence Pattern; covers_targets documented. - tests: test_regulatory_convergence (18 transition+company pass), mypy --strict clean. Curated expert knowledge, AI first draft (L1/draft) — Annex/Article refs indicative, review_required by a machinery-safety expert. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
"""Cross-Regulation Capability Mapping (Regulatory Convergence) — RS-005.
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Answers the USP question: „Welche Capability deckt gleichzeitig mehrere Regelwerke ab?"
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Given, per capability, the set of target regulations it covers (`covers_targets`,
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curated knowledge from a Regulatory Convergence Pattern), it computes how many
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capabilities cover >= 2 regulations at once — the basis for the customer sentence
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„von N Maßnahmen decken M gleichzeitig CRA und MaschinenVO".
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Pure, deterministic, computed-not-stored. No new graph/base class/meta-model class
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(freeze v1.0 untouched). Python 3.9 compatible (no `|` unions).
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"""
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from __future__ import annotations
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from typing import Dict, List
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from pydantic import BaseModel, Field
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class RegulatoryConvergence(BaseModel):
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targets: List[str] = Field(default_factory=list)
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per_target_count: Dict[str, int] = Field(default_factory=dict) # capabilities each target requires
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multi_target_capabilities: List[str] = Field(default_factory=list) # cover >= 2 targets
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single_target_capabilities: List[str] = Field(default_factory=list)
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headline: str = "" # NO percentages
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def regulatory_convergence(
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capability_targets: Dict[str, List[str]], targets: List[str]
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) -> RegulatoryConvergence:
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"""capability_targets: capability_id -> regulations it covers. `targets`: the regulations in scope."""
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target_set = set(targets)
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per: Dict[str, int] = {t: 0 for t in targets}
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multi: List[str] = []
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single: List[str] = []
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for cap, covers in capability_targets.items():
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in_scope = [t for t in covers if t in target_set]
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for t in in_scope:
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per[t] += 1
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if len(in_scope) >= 2:
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multi.append(cap)
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elif len(in_scope) == 1:
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single.append(cap)
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headline = (
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"%d von %d Capabilities decken >= 2 Regelwerke gleichzeitig ab (%s)."
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% (len(multi), len(capability_targets), " + ".join(targets))
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)
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return RegulatoryConvergence(
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targets=list(targets),
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per_target_count=per,
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multi_target_capabilities=sorted(multi),
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single_target_capabilities=sorted(single),
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headline=headline,
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)
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