What ongoing-monitoring evidence does M-25-21 expect?
M-25-21 expects high-impact AI to be monitored after deployment so that performance degradation or drift is caught, mapping to NIST AI RMF MEASURE 2.4 (monitor functionality and behaviour in production). Useful evidence is a recurring, comparable evaluation…
Answer.
M-25-21 expects high-impact AI to be monitored after deployment so that performance degradation or drift is caught, mapping to NIST AI RMF MEASURE 2.4 (monitor functionality and behaviour in production). Useful evidence is a recurring, comparable evaluation against a stable probe set with a recorded delta between runs. Planisphere's single-business-day re-run cadence produces that drift delta as a reproducible artifact; it surfaces behaviour change, it does not decide the remediation.
Cite-anchor: NIST AI 100-1 (AI Risk Management Framework) · MEASURE function
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