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.
Scope must be established first
Documentation pressure depends on the organization role, system use, deployment geography, risk classification, and applicable obligations. Those assumptions should be recorded rather than silently embedded in a checklist.
Build evidence around the actual system
A reviewer-readable package can preserve system description, intended use, data and model records, human oversight, transparency materials, testing, post-market responsibilities, and change history.
- System and role assumptions
- Applicable requirement set
- Supplied supporting artifacts
- Accepted mappings and limitations
- Open legal or operational questions
Keep legal interpretation separate from evidence integrity
Counsel may determine what obligations apply. The assurance package should then show whether the supplied artifacts are identifiable, current, traceable, attributable, and reviewable for that defined interpretation.
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.