The NIST AI Risk Management Framework provides a vocabulary and process model for AI risk management. For runtime teams, it is useful for connecting engineering controls with organizational governance, documentation, monitoring, accountability, and review cadence.
At a glance
- Organization
- NIST
- Runtime role
- Risk management framework
- Category
- Security and Governance
- Official documentation
- Visit official documentation opens in a new tab
Where it fits in the runtime stack
Cross-layer governance and operational assurance.
Primary runtime role
Use NIST AI RMF to organize risk identification, measurement, management, and governance around AI runtime behavior.
Not the same as
The framework does not prescribe a specific runtime architecture or certify a deployment as compliant.
Integration notes
- Translate governance outcomes into runtime controls, evidence, and ownership records.
- Tie risk statements to trace, audit, retention, approval, and incident-response requirements.
- Date all regulatory or governance claims and re-review them on a fixed schedule.
Questions before production use
- Which runtime events are required evidence for risk management?
- Who owns review, escalation, and corrective action for high-risk runtime behavior?
- How are risks updated after incidents, model changes, or new tool integrations?
Review and deprecation posture
This profile is reviewed as part of the aRuntime.com quarterly resource audit. If the official documentation moves, the project is archived, or the resource changes scope, this page should be updated with a dated status note rather than silently removed.
Sources and further reading
- AI Risk Management Framework opens in a new tab — NIST; official framework documentation; accessed 2026-06-20 UTC.
Last reviewed: .