Decision-makings related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a diverse team (e.g., diversity of demographics, disciplines, experience, expertise, and backgrounds). The people making AI risk decisions span disciplines and backgrounds, and where internal composition is narrow, external input is obtained and recorded.
NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-GV-3.1 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.