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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Measure function · TEVV · 19 subcategories · the audit-triad home.
More on NIST AI RMF · MEASURE.
Prepare evidence for NIST AI RMF · MEASURE review.
First evidence record within 21 days of access · re-runs in a single business day. Planisphere measures model behaviour and emits a reproducible, sha-pinned record — it does not certify, file, or give legal advice.