Turn framework language into a reviewer-readable evidence position.
The objective is not to reproduce a framework as a static list. It is to identify the current AI system, preserve the evidence supplied, record accepted support and limitations, and expose unresolved dependencies before a procurement or assurance decision.
Candidate mapping accelerates review
Automation can surface likely relationships between artifacts and requirements, reduce search effort, and help prioritize examination.
Acceptance carries authority
A human reviewer determines whether the artifact actually supports the requirement for the system and context under review.
The ledger should preserve both states
The record should distinguish proposed, rejected, accepted, conditional, and reverification-required relationships rather than collapsing them into one score.
Establish what the current evidence can support.
The $3,500 fixed-scope Baseline covers one defined AI system and one review context. No annual license is required.