The NIST AI Risk Management Framework is a voluntary framework to help organizations manage risks across the AI lifecycle and build more trustworthy AI systems.
The NIST AI Risk Management Framework is a voluntary, outcome-based framework that helps organizations identify, assess, and manage risks across the AI lifecycle to build more trustworthy systems. It is organized around four core functions — Govern, Map, Measure, and Manage — and is accompanied by a Playbook and profiles for practical implementation. It is not a certification scheme, but a widely referenced foundation for responsible AI programs.
Because it is voluntary and outcome-based, there is no audit or deadline. Organizations adopt it incrementally, maturing their Govern/Map/Measure/Manage practices over time.
Public information about the framework itself. We don't claim certifications, assessment status, or authorizations for our own product.
How the platform supports your NIST AI RMF program — from first scope to ongoing monitoring.
Establish AI governance, roles, and policies, captured as documented activities and evidence.
Identify context and risks for each AI system in an AI risk register.
Assess and track AI risks and trustworthiness characteristics with supporting evidence.
Prioritize and document mitigations, keeping the record current as systems evolve.
Public, high-level control or requirement areas — for orientation, not a complete control list.
NIST AI RMF shares controls with frameworks you may already run. A passing test can satisfy requirements in more than one place — so adding the next framework means reusing work, not repeating it.
No. It is a voluntary, outcome-based framework rather than a certification scheme.
They overlap heavily on governance and risk; many teams use the AI RMF as a practical foundation and ISO 42001 for certification.
Govern, Map, Measure, and Manage — the core functions around which the framework is organized.
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