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.
Start with the review boundary
The same policy can apply to many systems, but a reviewer still needs to know which application, model, environment, data flow, owner, and deployment the evidence describes.
Separate mapping from effectiveness
A mapping states that an artifact is relevant to a control question. It does not by itself establish implementation, operating effectiveness, inheritance, or authorization.
- Artifact identity and content hash
- Control reference and review question
- System association
- Reviewer rationale and authority
- Limitations, freshness, and conditions
Use gaps to drive remediation
Missing access records, incomplete boundary documentation, unsupported retention claims, or unverified tenant-segregation evidence should become ordered evidence actions tied to review dependencies.
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.