NIST AI Risk Management Framework.

Overview

The NIST AI Risk Management Framework (AI RMF 1.0, published January 26, 2023 as NIST AI 100-1) gives organizations a repeatable process for identifying, measuring, and responding to AI risks across the full development and deployment lifecycle. AI RMF 1.0 remains the current version as of June 2026 — NIST has not released a 2.0 and has instead expanded the framework through companion profiles.

NIST AI RMF carries no direct enforcement authority — no penalties attach to ignoring it, and it has always been voluntary. For a period it sat at the center of two bodies of real legal obligation, but both of those mandates have since been withdrawn or rewritten, and that history matters for understanding where the framework stands today:

• The federal mandate is gone. Executive Order 14110 (October 2023) had directed federal agencies toward NIST AI guidance, and OMB M-24-10 (March 2024) translated that into specific minimum practices tied to AI RMF outcomes. EO 14110 was rescinded on January 20, 2025, and OMB M-24-10 was rescinded and replaced by OMB M-25-21 on April 3, 2025. Federal AI governance now flows from EO 14179 and M-25-21 — which keep risk management for "high-impact" AI but no longer prescribe the NIST AI RMF by name (see Who It Applies To).
• The Colorado safe harbor is gone. The original Colorado AI Act (SB 24-205) cited alignment with recognized AI risk management frameworks as evidence of "reasonable care." Colorado repealed and reenacted that law as SB 26-189 (signed May 14, 2026, effective January 1, 2027), eliminating the duty of reasonable care and the framework-based affirmative defense. The new law does not reference the NIST AI RMF.

What endures is the framework itself. The four-function structure — Govern, Map, Measure, Manage — applies to any industry and any deployment size, and it remains the most widely used operational backbone for AI governance in the US. A 15-person fintech running a single credit-scoring model and a health system operating 200 clinical decision-support tools both use the same framework to document AI risk ownership and track remediation progress — now as a voluntary best practice rather than as a statutory or safe-harbor requirement.

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Who It Applies To

No statutory scope rule applies — NIST AI RMF is voluntary. But adoption is functionally expected in three situations, even though the specific legal mandates that once compelled it have changed:

Federal agencies and their contractors

Federal AI governance shifted in 2025. EO 14110 was rescinded on January 20, 2025 and OMB M-24-10 was rescinded and replaced by OMB M-25-21 ("Accelerating Federal Use of AI Through Innovation, Governance, and Public Trust," issued April 3, 2025) under EO 14179. M-25-21 keeps the core machinery — agencies must designate a Chief AI Officer (CAIO), publish AI use-case inventories, and apply minimum risk-management practices (including pre-deployment testing and AI impact assessments) for "high-impact" AI. But it takes a pro-innovation posture and, unlike M-24-10, does not prescribe the NIST AI RMF by name; agencies now develop their own risk-management approaches. The NIST AI RMF remains the most practical off-the-shelf structure for meeting M-25-21's high-impact requirements, and procurement officers still frequently ask for it in RFPs — but contractors should treat alignment as a strong practical expectation rather than a framework the current federal policy formally mandates.

Companies operating in Colorado

Colorado's AI law was rewritten in 2026. SB 24-205 had named alignment with recognized AI risk management frameworks (including NIST AI RMF) as a factor demonstrating reasonable care. That entire reasonable-care regime — along with mandatory risk-management programs, annual impact assessments, the framework-based affirmative defense, and the duty to notify the Attorney General of discovered algorithmic discrimination — was repealed and reenacted as SB 26-189 (signed May 14, 2026, effective January 1, 2027). SB 26-189 is a narrower notice, disclosure, and consumer-rights regime for automated decision-making technology (ADMT): developers must supply documentation, deployers must give consumers clear notice before ADMT materially influences a consequential decision and a plain-language explanation within 30 days of an adverse outcome, and consumers gain rights to correction and meaningful human review. SB 26-189 does not reference the NIST AI RMF and does not grant a Colorado-specific liability shield for framework alignment. Adopting the NIST AI RMF is still the most efficient way to operationalize SB 26-189's documentation and oversight duties — it just no longer functions as a Colorado safe harbor.

Financial services and healthcare firms

The OCC, FRB, FDIC, and CFPB have cited NIST AI RMF in interagency discussion of AI risk, and FDA's framework for AI/ML-based Software as a Medical Device references the same core functions. Documented NIST AI RMF alignment gives compliance teams a pre-built answer to examiner questions about AI governance — particularly for models that affect credit decisions (subject to ECOA, FCRA) or clinical outcomes.

Organizations pursuing ISO 42001 certification also benefit: NIST maintains a published crosswalk between AI RMF subcategories and ISO 42001 clauses. The two frameworks are complementary — AI RMF provides risk management operational substance, ISO 42001 provides the management system shell.

Roles Within the Framework

The NIST AI RMF addresses distinct organizational roles:

• AI actors — all individuals and organizations involved in the AI lifecycle: designers, developers, deployers, operators, evaluators, and end users
• Governance bodies — boards, executives, and oversight committees responsible for AI risk governance
• Third parties — vendors, suppliers, and service providers in the AI supply chain

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Core Functions

The NIST AI RMF is organized around four core functions. Each function contains categories (written as GOVERN 1, MAP 2, etc.) and subcategories (GOVERN 1.1, MAP 2.3, etc.) that describe specific outcomes organizations should achieve.

Govern

Govern establishes the organizational infrastructure that makes AI risk management repeatable rather than ad hoc. It is cross-cutting: the policies and accountability structures defined under Govern shape how Map, Measure, and Manage operate.

Six concrete outcomes define the Govern function:

• Documented policies and roles (GOVERN 1.1): Who owns AI risk decisions? Which risk levels require board-level approval? Which require legal sign-off before deployment? Write these down in a policy document before your first audit or enforcement inquiry.
• Board-level accountability (GOVERN 1.2): AI risk has explicit executive ownership, documented in board minutes or a designated officer's mandate — not just in a data science team's wiki.
• Risk tolerance statements (GOVERN 1.6): Define in writing what levels of bias error rates, failure modes, or accuracy degradation are acceptable for each system category. A credit underwriting model and a content recommendation engine warrant different tolerance thresholds.
• Interdisciplinary review: Governance bodies include legal, data science, compliance, product, and affected-community representatives — not solely the team that built the model.
• Third-party oversight (GOVERN 6.1–6.2): For every vendor AI system deployed — hiring tools, credit models, clinical decision support — document the vendor's name, the documentation obtained, and how compliance obligations are enforced contractually.
• Organizational culture: Regular AI risk training for staff who operate or oversee AI systems, with documented completion records.

Map

Map pins each AI system to its real-world operating context before broader risk work begins. Without Map, risk assessment floats free of the specific deployment and misses use-case-specific failure modes.

Five outputs define a complete Map for each AI system:

• Use-context documentation (MAP 1.1): Who runs this system? What decisions does it influence? What happens downstream when it produces an error? A hiring tool screening 50,000 applications per year has different Map outputs than the same algorithm used to shortlist 20 internal transfer candidates.
• Affected stakeholder inventory (MAP 3.5): Identify not just system users but communities impacted by AI outputs. For an automated benefits-eligibility system, affected parties include applicants who never interact directly with the UI.
• Risk catalog across seven trustworthiness dimensions (MAP 5.1): Validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness. Each dimension gets a separate risk entry with likelihood, severity, and ownership assigned.
• Benefit-cost assessment (MAP 2.3): Document why deploying this system is worth the identified risks. This record becomes critical if a regulator or plaintiff asks why the organization deployed the system given known limitations.
• Interdependency map (MAP 5.2): What data sources feed this system? What other systems consume its output? A credit model pulling from three external data vendors whose scores feed four downstream decisions has a different risk surface than a standalone tool.

Measure

Measure provides the quantitative and qualitative evidence base for AI risk management decisions. It converts risk identification from the Map function into documented findings that can be tracked, compared, and reported.

Four outputs define the Measure function:

• Metrics and measurement approaches (MEASURE 1.1): Select metrics that fit the specific risk type. Bias measurement for a hiring model uses demographic parity or equalized odds — not generic accuracy. Document which metric applies to which risk and why.
• AI system testing (MEASURE 2.5–2.7): Test against identified risks before deployment and at defined post-deployment intervals. For high-risk systems in regulated industries, red-teaming and adversarial testing are standard practice. Bias-testing records remain a strong best practice and are still required under some regimes (for example, NYC Local Law 144 bias audits), even though Colorado's SB 26-189 no longer mandates impact-assessment bias-testing records the way SB 24-205 did.
• Production monitoring (MEASURE 3.1–3.3): Track model performance, data drift, and emergent risks in production. Define thresholds that trigger review — not just alert — when crossed.
• Feedback mechanisms: Collect input from AI system users and affected communities after deployment. Document what feedback was received and what changes were made in response.

Manage

Manage converts risk findings into decisions and action. It allocates resources, prioritizes response, and closes the cycle from measurement to treatment.

Five outputs define the Manage function:

• Risk prioritization (MANAGE 1.1): Rank identified risks by likelihood × severity × organizational risk tolerance. Not every identified risk warrants immediate remediation — document the prioritization rationale.
• Risk treatment (MANAGE 2.2): Choose a response for each risk: accept (with documented rationale), mitigate (with specific control), transfer (insurance, contractual), or avoid (discontinue the use case). Accept is a legitimate answer when it is documented.
• Resource allocation: Assign specific personnel, tools, and budget to each risk treatment. Underfunded risk management plans routinely fail audits.
• Incident response (MANAGE 3.1): Define what constitutes an AI system incident, who gets notified, within what timeframe, and what remediation steps follow. Strong incident-response and notification procedures remain a core MANAGE outcome and may be required by other laws (breach-notification statutes, sector regulators). Note that Colorado's earlier SB 24-205 duty to notify the Attorney General within 90 days of discovering algorithmic discrimination was eliminated by SB 26-189, so that specific Colorado deadline no longer applies.
• Continuous improvement (MANAGE 4.1–4.2): Update risk assessments and treatments based on incidents, model changes, regulatory updates, and evolving best practices. Document each update cycle with dates and responsible parties.

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AI & NIST AI RMF Intersection

The NIST AI RMF remains the operational backbone of AI compliance in the United States — but its legal footing changed sharply in 2025–2026. The mandates that once pointed to it directly have been rescinded or rewritten, so the framework now functions as a voluntary, cross-jurisdiction governance layer rather than as a directly enforced or safe-harbor-granting standard.

• Federal: EO 14110 (which had directed agencies toward NIST AI guidance) was rescinded on January 20, 2025, and OMB M-24-10 was rescinded and replaced by OMB M-25-21 on April 3, 2025 under EO 14179. M-25-21 keeps minimum risk-management practices for high-impact federal AI but no longer prescribes the NIST AI RMF by name.
• Colorado: SB 24-205 had treated NIST AI RMF alignment as evidence of "reasonable care." SB 26-189 (signed May 14, 2026, effective January 1, 2027) repealed that standard entirely and does not reference the NIST AI RMF.

The framework still maps cleanly onto the obligations that do remain in force across these and other regimes, which is why organizations continue to use it as a single governance substrate. The table below shows how AI RMF functions correspond to the substance of common AI requirements — note that the Colorado row reflects the original SB 24-205 impact-assessment concept, which is no longer a Colorado statutory requirement but remains a useful governance practice.

Mapping to Common Requirements

Trustworthiness Characteristics

The NIST AI RMF defines seven characteristics of trustworthy AI that organizations should assess across all AI systems:

1. Valid and Reliable — the AI system performs as intended under expected and unexpected conditions
2. Safe — the AI system does not endanger human life, health, property, or the environment
3. Secure and Resilient — the AI system maintains confidentiality, integrity, and availability, and can withstand adverse events
4. Accountable and Transparent — the AI system's processes and decisions can be explained and attributed to responsible parties
5. Explainable and Interpretable — AI system outputs and processes can be understood by intended users and stakeholders
6. Privacy-Enhanced — the AI system respects data privacy norms and minimizes privacy risks
7. Fair — with Harmful Bias Managed — the AI system's outcomes do not systematically disadvantage individuals or groups

Generative AI Profile (NIST AI 600-1)

On July 26, 2024, NIST published the Generative AI Profile — a companion document that maps generative AI-specific risks to the AI RMF's core functions. It identifies 12 unique risk categories for generative AI:

• CBRN Information — risks of generating chemical, biological, radiological, or nuclear weapon information
• Confabulation — generating false but plausible content (hallucination)
• Data Privacy — training data memorization and regurgitation of personal information
• Environmental — computational and energy costs of large model training and inference
• Information Integrity — generation of misleading or manipulated content
• Information Security — novel attack vectors including prompt injection and model extraction
• Intellectual Property — copyright and IP risks from training data and generated outputs
• Obscene, Degrading, and/or Abusive Content — generation of harmful content
• Toxicity, Bias, and Homogenization — amplification of biases and reduction of content diversity
• Human-AI Configuration — risks from over-reliance, anthropomorphization, or inappropriate trust calibration
• Value Chain and Component Integration — supply chain risks from foundation models through deployment
• Dangerous or Violent Recommendations — generating actionable harmful instructions

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Profiles & Tiers

AI RMF Profiles

A profile is a customized implementation of the AI RMF tailored to a specific context. Profiles allow organizations to select and prioritize the subcategories most relevant to their situation.

Current NIST-published profiles:

• Generative AI Profile (AI 600-1) — maps GAI-specific risks to AI RMF subcategories
• Crosswalk profiles — NIST maintains mappings between the AI RMF and other frameworks (ISO 42001, EU AI Act requirements)
• Sector-specific work in progress — NIST continues to develop additional profiles (for example, a concept note for a Trustworthy AI in Critical Infrastructure profile was released in 2026)

How organizations use profiles:

1. Current Profile — document which AI RMF outcomes you are currently achieving
2. Target Profile — define which outcomes you need to achieve based on your regulatory obligations, risk appetite, and stakeholder expectations
3. Gap Analysis — identify the difference between current and target profiles and create an action plan

Implementation Tiers

While the NIST AI RMF does not prescribe formal maturity tiers like the NIST Cybersecurity Framework, organizations commonly adopt a tiered approach:

• Tier 1 — Partial — AI risk management is ad hoc and reactive; limited awareness of AI-specific risks at the organizational level
• Tier 2 — Risk-Informed — AI risk management practices exist but may not be organization-wide; management is aware of AI risks but processes are inconsistent
• Tier 3 — Repeatable — organization-wide AI risk management policies are established and consistently applied; regular review and update cycles are in place
• Tier 4 — Adaptive — AI risk management is fully integrated into organizational decision-making; continuous improvement based on lessons learned, incidents, and evolving best practices

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Use-Case Guidance

The NIST AI RMF is designed to be applied to specific AI use cases rather than adopted as a blanket organizational policy. Key considerations for common enterprise use cases:

High-Risk Use Cases

Use cases where AI system outputs directly affect individuals' rights, safety, or access to critical services:

• Hiring and employment decisions — resume screening, candidate scoring, performance evaluation (governed by NYC LL 144, Colorado's SB 26-189 ADMT regime, Illinois AIVIRA)
• Credit and lending decisions — underwriting models, credit scoring, loan approval systems (ECOA, Fair Lending)
• Healthcare diagnostics and treatment — clinical decision support, diagnostic imaging, treatment recommendations (FDA, HIPAA)
• Criminal justice — risk assessment tools, predictive policing, recidivism prediction (no federal law, but significant litigation and state-level scrutiny)
• Insurance underwriting — pricing models, claims assessment, fraud detection (state insurance regulation)

For high-risk use cases, organizations should implement all four core functions comprehensively and document their risk management processes in detail. This documentation supports defensible AI governance and helps satisfy the notice, disclosure, oversight, and record-keeping duties imposed by current state laws — even though framework alignment is no longer a statutory safe harbor in Colorado.

Medium-Risk Use Cases

Use cases with meaningful but less direct impacts on individuals:

• Customer service automation — chatbots, virtual assistants, automated ticket routing
• Content moderation — AI-driven content filtering and classification
• Supply chain optimization — demand forecasting, logistics planning

For medium-risk use cases, organizations should focus on Map and Measure functions to understand and monitor risks, with proportionate Govern and Manage processes.

Lower-Risk Use Cases

Use cases with limited direct impact on individuals:

• Internal analytics — business intelligence, operational dashboards
• Code generation — developer tools, code completion
• Document summarization — internal document processing

Even for lower-risk use cases, the Govern function should be in place to ensure organizational oversight and the ability to escalate if risk characteristics change.

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Compliance Timeline

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Regulatory References

The NIST AI RMF is voluntary. As of June 2026 it is no longer directly mandated by federal executive order or referenced as a statutory safe harbor by Colorado law, but it remains broadly aligned with — and a practical tool for satisfying — many enforceable requirements.

Federal Posture (Current)

• Executive Order 14179 ("Removing Barriers to American Leadership in Artificial Intelligence," January 23, 2025) — the current governing executive order on federal AI; replaced the rescinded EO 14110.
• OMB Memorandum M-25-21 (April 3, 2025) — "Accelerating Federal Use of AI Through Innovation, Governance, and Public Trust." Rescinded and replaced M-24-10. Retains Chief AI Officer designation, public AI use-case inventories, and minimum risk-management practices (pre-deployment testing, AI impact assessments) for "high-impact" AI, but takes a pro-innovation posture and does not prescribe the NIST AI RMF by name.

Superseded Federal Authorities (No Longer in Force)

• Executive Order 14110 (October 30, 2023) — rescinded January 20, 2025. Had directed federal agencies toward NIST AI governance guidance.
• OMB Memorandum M-24-10 (March 28, 2024) — rescinded and replaced by M-25-21 on April 3, 2025. Had required minimum AI risk-management practices for federal agencies tied to NIST AI RMF principles.

State Law

• Colorado AI Act — SB 26-189 (signed May 14, 2026; effective January 1, 2027) — repealed and reenacted the original SB 24-205. It is a notice, disclosure, and consumer-rights regime for automated decision-making technology (ADMT). It eliminated the duty of reasonable care, mandatory risk-management programs, annual impact assessments, the AG algorithmic-discrimination notification duty, and the NIST AI RMF / ISO 42001 affirmative defense. SB 26-189 does not reference the NIST AI RMF and grants no framework-alignment safe harbor. Enforcement is by the Colorado Attorney General (no private right of action), with violations treated as deceptive trade practices.
• NYC Local Law 144 — requires independent bias audits for automated employment decision tools; the AI RMF's Measure function supports the underlying testing and documentation.

Alignment and Crosswalks

• EU AI Act — NIST has published crosswalk documents mapping AI RMF subcategories to EU AI Act requirements; organizations subject to both US state laws and the EU AI Act can use the AI RMF as a unifying governance layer. Note that the EU's "Digital Omnibus" package (provisional Council–Parliament agreement reached in early May 2026) defers the EU AI Act's high-risk obligations: stand-alone Annex III systems move from August 2, 2026 to December 2, 2027, and AI embedded in regulated products under Annex I from August 2, 2027 to August 2, 2028. These dates take legal effect upon formal adoption and publication in the Official Journal.
• ISO/IEC 42001 — the AI RMF's four functions (Govern, Map, Measure, Manage) map to ISO 42001's management system structure, enabling organizations pursuing certification to build on their AI RMF implementation.
• NIST Cybersecurity Framework (CSF) — the AI RMF is designed to complement the CSF; organizations already using CSF can extend their governance processes to cover AI-specific risks.

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Implementation Steps

Use this roadmap to implement the NIST AI RMF in your organization:

1. Establish AI governance (Govern). Designate an AI risk management owner — whether a Chief AI Officer, risk committee, or existing governance body. Document AI policies, roles, and accountability structures. Define your organization's AI risk appetite and tolerance levels.

2. Inventory your AI systems. Create a comprehensive inventory of all AI systems in development, deployment, and production. Include vendor-provided AI systems and AI components embedded in third-party software.

3. Categorize by risk level. For each AI system, assess the potential impact on individuals, communities, and the organization. Use the Map function to document intended purposes, stakeholders, and identified risks. Prioritize high-risk systems for immediate attention.

4. Build your profiles. Create a Current Profile documenting your existing AI risk management practices. Define a Target Profile based on your regulatory obligations (Colorado SB 26-189 ADMT duties, EU AI Act, sector-specific requirements) and risk appetite. Identify gaps.

5. Implement measurement and testing (Measure). Establish metrics for each AI system's trustworthiness characteristics. Implement bias testing, performance monitoring, adversarial testing, and drift detection. For generative AI systems, apply the AI 600-1 profile's risk categories.

6. Establish risk treatments (Manage). For each identified and measured risk, select a treatment: mitigate, accept, transfer, or avoid. Allocate resources and assign owners. Build incident response procedures for AI system failures.

7. Document and communicate. Maintain comprehensive documentation of your AI risk management process. While framework alignment is no longer a statutory safe harbor in Colorado, this documentation is still your strongest evidence of responsible AI governance and supports the notice, disclosure, and record-keeping duties that current laws do impose. Communicate your AI governance practices to stakeholders, regulators, and the public as appropriate.

8. Monitor and iterate. AI risk management is continuous, not one-time. Schedule regular reviews of AI system performance, risk assessments, and governance processes. Update profiles as regulations evolve, new risks emerge, and organizational context changes.

9. Align with regulatory requirements. Map your NIST AI RMF implementation to specific regulatory obligations: Colorado SB 26-189 ADMT notices and consumer-rights workflows, NYC LL 144 bias audits, EU AI Act conformity assessments. Use the AI RMF as the single governance backbone that feeds compliance documentation for multiple regulations.

10. Engage with the NIST AI RMF community. NIST actively solicits feedback and publishes updated resources. Monitor NIST's AI RMF website for new profiles, crosswalk documents, and playbook updates. Participate in sector-specific profile development where relevant.

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Frequently Asked Questions

Is the NIST AI RMF legally binding? No — the NIST AI RMF is a voluntary framework and has no direct penalties. It is not currently mandated by executive order: EO 14110, which had directed federal agencies toward NIST AI guidance, was rescinded on January 20, 2025 and replaced by EO 14179, and OMB M-25-21 (April 3, 2025) governs federal AI use without prescribing the NIST AI RMF by name. It also no longer functions as a Colorado statutory safe harbor after SB 26-189. Despite these changes, it remains the most influential and widely used reference for AI governance in the US.

What are the four core functions of the NIST AI RMF? Govern (organizational AI risk governance), Map (context and risk identification), Measure (risk assessment and monitoring), and Manage (risk treatment and response). Govern is cross-cutting and informs the other three functions.

How does the NIST AI RMF relate to the EU AI Act? NIST has published crosswalk documents mapping AI RMF subcategories to EU AI Act requirements. Organizations subject to both frameworks can use the AI RMF as a unifying governance layer. The AI RMF's risk management approach is broadly compatible with the EU AI Act's risk-based classification, though the EU AI Act adds prescriptive legal requirements (conformity assessments, CE marking, registration) that go beyond the AI RMF. Note that the EU's Digital Omnibus package has deferred several high-risk compliance deadlines into 2027–2028.

Does the NIST AI RMF cover generative AI? Yes. The Generative AI Profile (NIST AI 600-1), published July 26, 2024, extends the AI RMF to address 12 generative AI-specific risk categories including confabulation, data privacy, information integrity, and CBRN information risks.

How much does NIST AI RMF implementation cost? The framework itself is free. Implementation costs vary based on organizational size and AI system portfolio. Unlike ISO 42001, there is no certification fee — the NIST AI RMF is self-assessed. Organizations typically invest in governance process development, technical tooling for bias testing and monitoring, and training for AI development and deployment teams.

Can NIST AI RMF compliance protect against enforcement actions? There is no longer a statutory safe harbor tied to it. Colorado's SB 24-205 had treated alignment with recognized frameworks (including NIST AI RMF) as evidence of reasonable care, but SB 26-189 repealed that defense. Adopting the NIST AI RMF does not create an affirmative legal defense today, but it does produce the governance documentation, testing records, and oversight trail that help demonstrate responsible practice to regulators and reduce the likelihood and severity of enforcement.

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Official Sources

• NIST AI Risk Management Framework (AI 100-1)
• NIST AI RMF Playbook
• Generative AI Profile (AI 600-1)
• Executive Order 14179 — Removing Barriers to American Leadership in AI (Federal Register)
• OMB M-25-21 — Accelerating Federal Use of AI