NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-MS-3.1: Approaches, personnel, and documentation are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance in deployed contexts
Approaches, personnel, and documentation are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance in deployed contexts. Risk identification continues after deployment with assigned personnel and a tracking record, so risks that emerge in real use are captured rather than only those anticipated at design.
Maintained by Gerard Blokdyk·Verified against the published standard ·Control text last updated
What else in your programme already covers this
This control maps to 3 controls across 2 other frameworks. If you already hold one of them, the evidence you collected for it is the starting point here rather than new work.
You are reading one control. How much of NIST AI Risk Management Framework (AI RMF 1.0) have you already done?
NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-MS-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.