Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and other end users. Approaches are documented. The measurement design is informed by people who understand the deployment context, because the risks that matter there are often not visible to those running the evaluation.
NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-MS-4.1 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.