AI-enabled cyber-physical systems (AI-CPS) turn data-driven decisions into physical actions, creating assurance challenges across sensing, computation, control, human oversight, and governance. ISO/IEC 42001:2023 specifies requirements for an artificial intelligence management system (AIMS), but conformity assessment alone does not show which architectural layers are affected, whether practices are mature enough for the risk context, or what actions should follow. We present CEDAR-42001 (Control-Evidence Decision and Action Reasoning), a two-stage method that converts ISO/IEC 42001 audit evidence into an architecture-aware assurance posture traceable to the audit record. Stage A preserves the conformity determination. Stage B adds four outputs to each audit row: (i) attribution to a governance stratum or one of seven AI-CPS layers; (ii) a five-dimensional maturity profile with binding-constraint identification; (iii) a risk-proportionate target maturity; and (iv) a rulebook-derived action recommendation. The enriched rows are aggregated into strategic, operational, and tactical decision products. We evaluate CEDAR-42001 using a synthetic autonomous-fleet AIMS and by comparing conformity-only results with the enriched outputs. Although 89.9 percent of audit rows were conforming, only 34.3 percent of conforming rows reached the baseline High-assurance category; across alternative operationalizations, this proportion ranged from 22.4 percent to 46.2 percent. A retrospective application to the 2023 Cruise robotaxi incident shows how the method captures documented concerns across governance, perception, decision-making, and human oversight and maps them to layer-specific actions. CEDAR-42001 does not estimate exploitability or replace technical CPS-security testing; it identifies where audit evidence warrants deeper technical assurance, organizational improvement, or remediation.

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