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.
The framework is not the evidence
Selecting NIST AI RMF establishes a governance vocabulary. Review readiness still depends on whether current, attributable artifacts support the questions being asked about the specific AI system.
- Defined AI system and use case
- Named owners and accountable roles
- Risk, testing, data, oversight, and change records
- Accepted evidence relationships with reviewer rationale
- Open conditions and ordered remediation
Map evidence to reviewer questions
A procurement-grade package should show which supplied artifact supports which Govern, Map, Measure, or Manage question. Candidate relationships can organize review, but affirmative support should remain human accepted and bounded by stated limitations.
Preserve uncertainty
A policy can support a design claim without proving that the design operates effectively. Missing tests, stale approvals, unclear ownership, and contradictory records belong in the Gap Register rather than being converted into broad compliance claims.
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.