--- title: "What Is the NIST AI Risk Management Framework? | Dawiso" description: "The NIST AI Risk Management Framework (AI RMF) is a voluntary framework for managing AI risk through four functions: Govern, Map, Measure, and Manage." url: "https://www.dawiso.com/glossary/nist-ai-rmf" language: "en" --- June 25, 2026 NIST AI RMFAI risk management frameworkAI governancetrustworthy AIGenerative AI ProfileAI risk assessment # What Is the NIST AI Risk Management Framework? The **NIST AI Risk Management Framework (AI RMF)** is a voluntary framework, published by the U.S. National Institute of Standards and Technology, for managing the risks of artificial intelligence across its lifecycle. Released as [**AI RMF 1.0**](https://www.nist.gov/itl/ai-risk-management-framework) on January 26, 2023, it gives organizations a common, technology-neutral way to identify, assess, and reduce AI risk while building the qualities of [trustworthy AI](https://www.dawiso.com/glossary/responsible-ai): validity, reliability, safety, security, accountability, transparency, explainability, privacy, and fairness. Unlike the [EU AI Act](https://www.dawiso.com/glossary/eu-ai-act), the AI RMF is not law and carries no penalties. It is a framework organizations adopt by choice: a structured, repeatable practice for governing AI that is widely used as a reference for AI risk programs, including outside the United States, and that NIST itself maps to other standards such as ISO/IEC 42001 through published [crosswalks](https://airc.nist.gov/airmf-resources/crosswalks/). TL;DR The **NIST AI Risk Management Framework (AI RMF 1.0**, January 2023) is a voluntary, technology-neutral framework for managing AI risk across the lifecycle. Its core is four functions: **Govern** (a cross-cutting culture of risk management), **Map** (establish context and identify risks), **Measure** (analyze and track them), and **Manage** (prioritize and act). A **Generative AI Profile** (NIST AI 600-1, July 2024) extends it to generative AI. It complements the binding [EU AI Act](https://www.dawiso.com/glossary/eu-ai-act) and the certifiable [ISO 42001](https://www.dawiso.com/glossary/iso-42001), and every function ultimately depends on governed, well-documented data. ## NIST AI RMF Defined The AI RMF was developed through an open, consensus-driven process (multiple public drafts, workshops, and comment periods) in response to a Congressional direction in the National Artificial Intelligence Initiative Act of 2020. The result is deliberately broad: NIST describes it as voluntary, rights-preserving, non-sector-specific, and use-case agnostic, meant for organizations of all sizes and sectors and for the teams that design, develop, deploy, or use AI systems. Its defining characteristics: - **Voluntary and non-prescriptive.** It describes outcomes to achieve, not a fixed checklist of controls, so organizations adapt it to their own risk profile and maturity. - **Risk-based and outcome-focused.** It centers on the qualities of trustworthy AI and on harms to people and society, not only on risk to the organization. - **Lifecycle-oriented.** It treats AI risk as a continuous practice across design, development, deployment, and monitoring, not a one-time gate. - **Technology-neutral.** The same four functions apply to a credit model, a chatbot, or an autonomous agent. NIST also ships companion resources: the **AI RMF Playbook** (suggested actions for each function), a **Roadmap**, a **Crosswalk** mapping the framework to other standards, and a set of profiles for specific use cases. NIST states that AI RMF 1.0 is being revised as part of the White House AI Action Plan, and on April 7, 2026 it released a [concept note for an AI RMF Profile on Trustworthy AI in Critical Infrastructure](https://www.nist.gov/programs-projects/concept-note-ai-rmf-profile-trustworthy-ai-critical-infrastructure), a sign of how the framework keeps extending into higher-stakes domains. ## The Four Core Functions The heart of the AI RMF is four functions, each broken into categories and subcategories. Three of them (Map, Measure, Manage) run as an iterative cycle, while the fourth, Govern, is a cross-cutting function that surrounds and informs the other three. - **Govern.** The cross-cutting function. It builds a culture of AI risk management: policies, accountable roles, oversight structures, and processes that connect the other three functions to the organization's values and legal obligations. Govern is what makes Map, Measure, and Manage durable rather than ad hoc. - **Map.** Establish the context and frame the risks. Who are the stakeholders, what is the intended purpose, what could go wrong, and how does this AI system fit into a wider business process? Map produces the shared understanding the other functions depend on. - **Measure.** Analyze, assess, and track the risks identified in Map, using quantitative and qualitative methods (metrics, evaluations, red-teaming, monitoring). Measure turns "this could be biased" into evidence you can act on. - **Manage.** Prioritize and respond. Allocate resources to the risks that matter most, decide whether to mitigate, transfer, avoid, or accept each one (the response options NIST lists under [MANAGE 1.3](https://airc.nist.gov/airmf-resources/airmf/5-sec-core/)), and plan for response and recovery when something goes wrong. The functions are not a strict sequence; NIST notes they can be performed in any order across the AI lifecycle. Map, Measure, and Manage are revisited continuously as a system changes, new data arrives, or new risks emerge, while Govern runs throughout. ## Generative AI & Agents On July 26, 2024, NIST released the [**Generative AI Profile (NIST AI 600-1)**](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence), a companion that names **twelve risks specific to generative AI**, including confabulation (hallucination), data privacy, harmful bias, intellectual-property exposure, information integrity, and **value chain and component integration**, the untraceable upstream data and components feeding a model. For each, it offers suggested actions tagged to the four functions (Govern, Map, Measure, Manage); a profile does not replace the AI RMF, it tailors the same actions to a specific setting. Several of those actions call directly for tracking the provenance and history of training data and for vetting third-party suppliers, which is catalog, lineage, and classification work by another name. NIST wrote the profile under Executive Order 14110, which was revoked on January 20, 2025 by [Executive Order 14148](https://www.federalregister.gov/documents/2025/01/28/2025-01901/initial-rescissions-of-harmful-executive-orders-and-actions); the profile itself remains published as a voluntary NIST resource. This matters as organizations move from single models to [agentic systems](https://www.dawiso.com/glossary/multi-agent-systems) that plan, call tools, and act with growing autonomy. Agents widen the risk surface: they retrieve data from many sources, take actions in live systems, and chain decisions in ways that are hard to trace. The AI RMF stays applicable because its functions are technology-neutral, but applying them well to agents demands far more rigorous context: an agent can only be governed if you know what data and tools it can reach, what each means, and who is accountable for them. The framework names the discipline; making it real for agents is a data and context problem. ## AI RMF vs EU AI Act & ISO 42001 These three are the reference points of modern [AI governance](https://www.dawiso.com/glossary/ai-governance), and they are complementary rather than competing: - **NIST AI RMF** is a *voluntary framework*. It tells you how to think about and operate AI risk management, with maximum flexibility and no certification or legal force. - **The [EU AI Act](https://www.dawiso.com/glossary/eu-ai-act)** is *binding law*. It tells you what you must do for AI placed on the EU market, with risk tiers and penalties. Under the Digital Omnibus on AI ([Regulation (EU) 2026/1744](https://eur-lex.europa.eu/eli/reg/2026/1744/oj), in force since July 27, 2026), its high-risk obligations apply from December 2, 2027 for Annex III systems and from August 2, 2028 for AI in Annex I products. Many of its obligations (risk management, data governance, documentation, human oversight) map closely onto AI RMF functions, so an AI RMF practice is strong preparation for it. - **[ISO 42001](https://www.dawiso.com/glossary/iso-42001)** is a *certifiable management-system standard*. It tells you how to run an organization-wide AI management system and lets an accredited body audit and certify it. A useful way to hold them together: NIST AI RMF gives you the *practice* of managing AI risk, ISO 42001 gives you a *certifiable system* to run that practice, and the EU AI Act sets the *legal floor* the practice has to clear. Many organizations use the AI RMF as the conceptual backbone and then formalize it through ISO 42001 and map it to AI Act obligations. NIST's own [crosswalk to ISO/IEC 42001](https://airc.nist.gov/airmf-resources/crosswalks/) and the framework's shared concepts (a [risk assessment](https://www.dawiso.com/glossary/risk-assessment) discipline, lifecycle thinking, documentation) make moving between them less work. ## Governed Context as the Foundation The AI RMF describes *what* good AI risk management looks like. It is largely silent on the substrate every one of its functions runs on: trustworthy, well-documented data and a clear, shared understanding of what that data means. In practice, that substrate is [data governance](https://www.dawiso.com/glossary/data-governance). Each function quietly depends on it: - **Map** cannot establish context or identify risk without knowing what data an AI system uses, where it came from, and how sensitive it is. That is the job of a [data catalog](https://www.dawiso.com/glossary/data-catalog), a business glossary, and [data classification](https://www.dawiso.com/glossary/data-classification). - **Measure** cannot assess bias, drift, or quality without trusted inputs and the [data lineage](https://www.dawiso.com/glossary/data-lineage) to trace a result back to its sources. - **Manage** cannot assign and act on risk without clear [ownership](https://www.dawiso.com/glossary/data-ownership) and accountability for the data and models involved. - **Govern** cannot evidence oversight without the documented record that catalog, glossary, and lineage produce as a by-product of normal work. This is where Dawiso fits. Its [AI governance](https://www.dawiso.com/solutions/ai-governance) approach treats governed context as a layer the organization maintains once and reuses: a catalog of the data assets connected to it, a glossary that fixes what each term means, lineage that traces how data flows, and classification labels that stewards assign to flag what is sensitive, with an accountable owner for each. Through the [Dawiso MCP server](https://help.dawiso.com/actual/dawiso-model-context-protocol-mcp/), MCP-compatible AI tools and agents can search that catalog, trace lineage, and preview objects and data samples. Dawiso documents this context; it does not test models for bias, monitor drift, or enforce access, which stay with your AI and data platforms. That governed context is what the AI RMF's Map, Measure, and Manage functions need to be real rather than aspirational, and what an AI agent needs in order to be governable at all. The framework is the discipline; governed context is what makes it operable. ## Conclusion The NIST AI Risk Management Framework has become a common language for talking about AI risk: voluntary, flexible, and technology-neutral, with a clear core of Govern, Map, Measure, and Manage. It pairs naturally with the certifiable ISO 42001 and the binding EU AI Act, giving organizations a coherent path from practice to system to legal compliance. But like every governance framework, it rests on a single capability that it does not itself provide: knowing, documenting, and trusting the data and context behind your AI. Get that foundation right and the four functions have something solid to stand on. ## Sources - NIST, [AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) (AI RMF 1.0 released January 26, 2023; revision under the AI Action Plan; critical infrastructure concept note of April 7, 2026). - NIST, [Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf). - NIST AI Resource Center, [AI RMF Core: Govern, Map, Measure, Manage](https://airc.nist.gov/airmf-resources/airmf/5-sec-core/) (risk response options in MANAGE 1.3). - NIST AI Resource Center, [AI RMF crosswalks](https://airc.nist.gov/airmf-resources/crosswalks/). - NIST, [Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence) (July 26, 2024). - NIST, [Concept Note: AI RMF Profile on Trustworthy AI in Critical Infrastructure](https://www.nist.gov/programs-projects/concept-note-ai-rmf-profile-trustworthy-ai-critical-infrastructure). - Federal Register, [Executive Order 14148, Initial Rescissions of Harmful Executive Orders and Actions](https://www.federalregister.gov/documents/2025/01/28/2025-01901/initial-rescissions-of-harmful-executive-orders-and-actions) (January 20, 2025; revokes EO 14110). - Publications Office of the EU (EUR-Lex), [Regulation (EU) 2024/1689, Artificial Intelligence Act](https://eur-lex.europa.eu/eli/reg/2024/1689/oj). - Publications Office of the EU (EUR-Lex), [Regulation (EU) 2026/1744, Digital Omnibus on AI](https://eur-lex.europa.eu/eli/reg/2026/1744/oj) (in force since July 27, 2026). - ISO, [ISO/IEC 42001:2023, AI management systems](https://www.iso.org/standard/81230.html). - Dawiso Help, [Dawiso Model Context Protocol (MCP)](https://help.dawiso.com/actual/dawiso-model-context-protocol-mcp/). - Dawiso Help, [AI features and integrations in Dawiso](https://help.dawiso.com/actual/ai-features-and-integrations-in-dawiso/). 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