Standards · NIST AI RMF
Version 1.0 · Generative AI Profile since July 2024NIST AI RMF from Govern to Manage
The voluntary U.S. framework for AI risk management. Four functions organise the work, seven characteristics define trustworthy AI, and a cross-sectoral profile extends it to generative AI. Modulos maps all of it in the governance graph.
The framework is voluntary, but it is referenced or accepted in some U.S. laws, agency guidance, and procurement contexts as the reference for AI risk practice. The four functions below are its organising core; everything else on this page supports them.
Govern, the cross-cutting function
Cultivates a culture of risk management. Govern establishes the policies, accountability, and oversight that make the other three functions repeatable. It is cross-cutting: it sits above Map, Measure, and Manage and is the only function that spans the entire organisation.
6 categories · 19 subcategories · what it looks like in practice
- Policies, processes, and procedures for AI risk management
- Accountability structures: defined roles, responsibilities, and lines of communication
- Workforce competence, diversity, and oversight of human-AI configurations
- Risk culture, transparency, and engagement with relevant AI actors
- Third-party and supply-chain risk policies
Map
Establishes context and identifies risks for a specific AI system. Map captures intended use, stakeholders, lifecycle dimensions, and the trustworthiness characteristics applicable to the system. The output is a documented system context that the rest of the framework builds on.
5 categories · 18 subcategories · what it looks like in practice
- Documented system context: purpose, intended use, deployment setting
- Categorisation of the AI system (classifier, generative model, recommender, and so on)
- Mapped risks and benefits across all components, including third-party data and software
- Impacts to individuals, groups, communities, and society identified and prioritised
Measure
Analyses and tracks identified risks using both quantitative and qualitative methods. Measure puts the metrics, evaluation processes, and monitoring infrastructure in place that turn the Map output into evidence the organisation can act on.
4 categories · 22 subcategories · what it looks like in practice
- Approaches and metrics selected for the most significant AI risks
- AI systems evaluated for trustworthy AI characteristics (test sets, TEVV documentation)
- Mechanisms to track existing, unanticipated, and emergent risks over time
- Feedback on measurement efficacy gathered and integrated
Manage
Allocates resources to treat risks, document residual risk, respond to incidents, and integrate findings back into Govern and Map. Manage closes the loop and keeps the framework operating as a continuous risk-management process instead of a one-time documentation exercise.
4 categories · 13 subcategories · what it looks like in practice
- Risk treatment decisions: proceed, modify, defer, or terminate
- Strategies to maximise AI benefits and reduce negative impacts
- Third-party risk monitoring and risk-control application
- Post-deployment monitoring, incident response, and recovery plans
The Generative AI Profile
NIST AI 600-1 layers twelve GenAI-specific risks onto the four functions above.
- CBRN information or capabilities
- Confabulation
- Dangerous, violent, or hateful content
- Data privacy
- Environmental impacts
- Harmful bias and homogenisation
- Human-AI configuration
- Information integrity
- Information security
- Intellectual property
- Obscene, degrading, or abusive content
- Value chain and component integration
The profile layers on top of AI RMF 1.0 without replacing it. Organisations governing generative AI start with the core framework and add the profile to capture GenAI-specific risks and the suggested actions that address them. Profiles are how AI RMF tailors to a context: sectoral profiles apply the whole framework to one industry, and cross-sectoral profiles like AI 600-1 apply it to one technology class across all sectors.
Source: NIST AI 600-1, Generative AI Profile (PDF, nist.gov)
The regimes overlap where the controls live
NIST AI RMF sits in the framework library as 105 requirements mapped onto shared controls beside ISO/IEC 42001 and the EU AI Act. AI RMF is the risk operating model; where another framework shares a control, the evidence behind it is written once and reused, with each framework keeping its own scope.
Framework library v1.0.28
Frequently asked questions
What is the NIST AI RMF?
The NIST AI Risk Management Framework 1.0 is voluntary guidance from the U.S. National Institute of Standards and Technology, published in January 2023 (NIST AI 100-1). It helps organisations identify, assess, and manage AI risks across the AI system lifecycle. It is organised around four core functions, Govern, Map, Measure, and Manage, and seven characteristics of trustworthy AI.
Is the NIST AI RMF mandatory?
No. NIST AI RMF is voluntary guidance with no force of law, but it is widely used by U.S. federal agencies, regulators, and enterprises as the de-facto reference for trustworthy AI, and some U.S. state AI laws and procurement contexts reference it explicitly, Texas HB 149 among them, where alignment supports a defence. For organisations selling into regulated U.S. buyers it functions as an expected baseline.
Does NIST AI RMF provide certification?
No. NIST does not certify organisations or products against the AI RMF. Any vendor or consultant claiming "NIST AI RMF certification" is referring to private training certifications; no NIST-issued attestation exists. The framework itself is voluntary guidance. Organisations seeking a certifiable AI management system typically use ISO/IEC 42001, which is internationally certifiable.
When was NIST AI RMF published?
AI RMF 1.0 (NIST AI 100-1) was published in January 2023. The Generative AI Profile (NIST AI 600-1), a cross-sectoral profile that applies AI RMF to generative AI specifically, followed in July 2024.
What are the four core functions of NIST AI RMF?
Govern, Map, Measure, and Manage. Govern is cross-cutting and sits above the other three: it establishes the policies, accountability, and culture that make risk management repeatable. Map establishes context and identifies risks for a specific AI system. Measure analyses and tracks those risks using quantitative and qualitative methods. Manage allocates resources to treat them.
What are the seven characteristics of trustworthy AI?
Per NIST AI RMF 1.0 Section 3: Valid and Reliable; Safe; Secure and Resilient; Accountable and Transparent; Explainable and Interpretable; Privacy-Enhanced; and Fair, with Harmful Bias Managed. These characteristics are not independent boxes to tick. They often trade off against one another, and managing those trade-offs is part of what risk management is for.
What is the Generative AI Profile (NIST AI 600-1)?
Published in July 2024, the Generative AI Profile is a cross-sectoral profile that applies AI RMF to generative AI specifically. It maps GenAI-specific risks, including CBRN information, confabulation, data privacy, harmful bias, information integrity, and value chain risks, onto the four core functions and provides suggested actions for each.
How does NIST AI RMF compare to ISO/IEC 42001?
AI RMF is voluntary U.S. guidance; ISO/IEC 42001 is a certifiable international management system standard. They layer. Most mature programs use NIST AI RMF as the risk-management operating model inside an ISO 42001 AI management system, which is the certifiable wrapper that makes the program auditable and durable.
How does NIST AI RMF relate to the EU AI Act?
AI RMF is voluntary; the EU AI Act is binding regulation with legal obligations and penalties. Implementing AI RMF helps build the risk-management practices the Act expects of high-risk AI providers (Article 9 risk management), but it does not replace the Act’s specific compliance and conformity-assessment requirements. The two are complementary at different layers.
What is the difference between NIST AI RMF and the NIST Cybersecurity RMF?
Different frameworks. The Cybersecurity RMF (NIST SP 800-37) governs information system security risk for federal information systems. The AI RMF (NIST AI 100-1) governs AI-specific risks, including data quality, model behaviour, and socio-technical harms that the Cybersecurity RMF does not address. Organisations operating both share governance processes where possible.
How do you implement NIST AI RMF?
Define scope, stand up Govern first, run Map, Measure, and Manage on a pilot AI system, build a profile (sectoral or cross-sectoral) tailored to your context, then operate it as a continuous loop. The framework is an operating model that keeps running after the first pass. Treating it as a one-time checklist is the most common failure mode.
How long does NIST AI RMF implementation take?
Implementation timelines vary widely with starting maturity and scope. Smaller organisations with a tight scope and existing governance can stand up a working AI RMF program in weeks; larger organisations with multiple AI portfolios and no prior governance typically work in quarters. The dominant variables are scope and the maturity of the Govern function.
How does Modulos help with NIST AI RMF implementation?
Modulos automates evidence collection and assessments across the four core functions; AI agents (Scout, Evidence Agent, Control Assessment Agent) reduce manual work; controls map across NIST AI RMF, ISO/IEC 42001, the EU AI Act, and other frameworks simultaneously. The platform holds CertX product conformity certificate 213-001/24 against ISO/IEC 42001:2023, the management system standard most mature NIST AI RMF programs operate inside.
Can small organisations implement NIST AI RMF?
Yes. The framework is scalable. Smaller organisations typically scope tightly to one or two AI systems, use the AI RMF Playbook’s suggested actions as a starting point, and grow the program incrementally. The voluntary, profile-based design means the framework adapts to the organisation rather than forcing the organisation to adapt to it.
Modulos has been an active member of NIST's AI Safety Institute Consortium (AISIC), now the Center for AI Standards and Innovation (CAISI), since its founding in 2024: working groups, briefs, and public comments on AI safety standards. nist.gov/aisi →
See NIST AI RMF mapped in the governance graph
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The ISO 42001 page covers the certifiable wrapper most mature AI RMF programs adopt, and the compliance guide covers the global regulatory picture around the framework.