Measurement results regarding AI system trustworthiness in deployment context(s) and across AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing consistently as intended. Results are documented. Measured results are validated against the judgement of people who know the context, so a result inside its operational limits on paper is confirmed as adequate in practice.
NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-MS-4.2 is one control. If you already hold one of the frameworks below, a reviewed crosswalk already says how much of NIST AI Risk Management Framework (AI RMF 1.0) your existing evidence covers. Hold EU AI Act and 48 of 72 NIST AI Risk Management Framework (AI RMF 1.0) controls already carry evidence.
Each report names every control your existing framework evidences, every one it does not, the reasoning behind each claim, and the claims that were argued against and rejected. 10 were rejected on the EU AI Act pair alone.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.