{"id":4993,"date":"2026-09-08T14:11:05","date_gmt":"2026-09-08T08:41:05","guid":{"rendered":"https:\/\/skillarbitra.ge\/blog\/?p=4993"},"modified":"2026-09-09T21:11:35","modified_gmt":"2026-09-09T15:41:35","slug":"nist-ai-risk-management-framework-explained","status":"publish","type":"post","link":"https:\/\/old.skillarbitra.ge\/blog\/nist-ai-risk-management-framework-explained\/","title":{"rendered":"NIST AI Risk Management Framework Explained"},"content":{"rendered":"\n\n<p>The NIST AI Risk Management Framework is a voluntary framework published by the US National Institute of Standards and Technology in January 2023 as NIST AI 100-1, written to help organisations identify and manage the risks of the AI systems they build, deploy or buy. Its Core is four functions, govern, map, measure and manage, broken into 19 categories and 72 subcategories and pointed at seven named characteristics of trustworthy AI. The framework is voluntary and does not itself create legal or regulatory obligations. The framework can be applied through profiles, including the Generative AI Profile released in July 2024.<\/p>\n\n<p>Profiles help organisations translate the framework&#8217;s general guidance into a specific use case or operating context.<\/p>\n<p>NIST developed the framework following direction provided by the National Artificial Intelligence Initiative Act of 2020. The <a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ai\/NIST.AI.100-1.pdf\" target=\"_blank\" rel=\"noopener\">National Artificial Intelligence Initiative Act of 2020<\/a> (P.L. 116-283) directed the agency to produce it, and the drafting ran through two public comment drafts and a series of workshops before version 1.0 was released on 26 January 2023. The framework describes itself as voluntary, rights-preserving, non-sector-specific and use-case agnostic.<\/p>\n<p>Although the framework is voluntary, organisations may encounter it through procurement requirements, vendor assessments and internal AI governance policies. Enterprise vendor questionnaires, client security reviews and internal AI policies cite it constantly, which means an Indian services firm or a freelance consultant working with US clients tends to meet the framework across a contract rather than a regulation. That&#8217;s a different kind of pressure from the one the <a href=\"https:\/\/skillarbitra.ge\/blog\/eu-ai-act-risk-levels-explained\/\" target=\"_blank\" rel=\"noopener\">EU AI Act risk levels<\/a> apply, and it usually arrives sooner, because a purchase order moves faster than a compliance deadline.<\/p>\n\n<hr>\n\n<nav class=\"ls-toc\" aria-label=\"Table of contents\">\n<h2>Table of Contents<\/h2>\n<ol class=\"ls-toc-list\">\n<li><a href=\"#h2-1\">The four functions of the NIST AI Risk Management Framework<\/a>\n<\/li>\n<li><a href=\"#h2-2\">Applying the NIST AI Risk Management Framework through profiles<\/a>\n<\/li>\n<li><a href=\"#h2-3\">Self-assessment, revisions and the ISO 42001 crosswalk<\/a>\n<\/li>\n<li><a href=\"#h2-4\">Frequently asked questions<\/a>\n<\/li>\n<li><a href=\"#h2-5\">References<\/a>\n<\/li>\n<\/ol>\n<\/nav>\n\n<hr>\n\n<a id=\"h2-1\"><\/a><\/p>\n<h2 id=\"the-four-functions-of-the-nist-ai-risk-management-framework\">The four functions of the NIST AI Risk Management Framework<\/h2>\n<p>The four functions of the NIST AI Risk Management Framework are govern, map, measure and manage, and they aren&#8217;t a sequence. Govern sits across the other three rather than in front of them. NIST puts it plainly in the <a href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/5-sec-core\/\" target=\"_blank\" rel=\"noopener\">Core<\/a>: govern is &#8220;a cross-cutting function that is infused throughout AI risk management and enables the other functions of the process.&#8221; Most summaries flatten this into a four-step cycle, which loses the point.<\/p>\n<p>The document itself runs to 48 pages and splits in two. Part 1 covers foundational material: how the framework frames risk, who it&#8217;s written for, the difficulty of measuring AI risk at all, and the trustworthiness characteristics. Part 2 holds the Core and the profiles, and four appendices follow it. One of those appendices earns its place immediately, because Appendix A describes the AI actor tasks that the rest of the framework keeps referring back to.<\/p>\n<p>AI actors is the framework&#8217;s term for everyone with a hand in a system, and it reaches well past the development team. NIST describes AI actors and lifecycle activities across the AI system lifecycle, including testing, evaluation, verification and validation (TEVV). But that modification carries an instruction rather than a label. The framework separates the actors who build and use models from those who verify and validate them, and it calls the separation a best practice.<\/p>\n<p>Underneath the functions sit categories, and underneath those, subcategories. The distribution is uneven and the unevenness is informative. Govern carries 6 categories and 23 subcategories, map carries 5 and 18, measure carries 4 and 18, and manage carries 4 and 13. That&#8217;s 19 categories and 72 subcategories in total, of which more than half describe organisational behaviour rather than technical testing.<\/p>\n<p>Govern covers the policies and processes for AI risk management, the accountability structures that give teams authority to act, workforce composition, an organisational culture in which risk gets raised, engagement with external actors including the people affected by a system, and supply chain risk. And that last category, Govern 6, is where third-party models and datasets enter the framework, pairing with Manage 3 on third-party entities. Anyone who has already worked in a <a href=\"https:\/\/skillarbitra.ge\/blog\/third-party-risk-management-career-path\/\" target=\"_blank\" rel=\"noopener\">third party risk management<\/a> function will recognise most of the vocabulary. Govern 1.1 addresses the need for legal and regulatory requirements involving AI to be understood, managed and documented.<\/p>\n<p>Map establishes context. It asks what the system is for, who the users are, what the intended and unintended uses look like, what the benefits and costs are, and where the impacts land. Map 4 extends this to all components of the system including third-party software and data, This is particularly relevant to organisations that rely on third-party AI software, models or data.<\/p>\n<p>Measure is where the testing sits, and its shape is worth noticing. Measure 2 alone holds 13 of the function&#8217;s 18 subcategories, and it&#8217;s the one tied directly to the trustworthiness characteristics. The other three categories cover choosing methods and metrics, tracking identified risks over time, and, unusually, feeding back on whether the measurement itself is working. That last category exists because NIST says openly in Part 1 that AI risk measurement is immature, that third-party data and models complicate it, and that consensus metrics often don&#8217;t exist yet.<\/p>\n<p>Manage handles response. Risks get prioritised and treated, with response options that include mitigating, transferring, avoiding or accepting. Manage 1.4 addresses the documentation of negative residual risks identified after risk treatment.<\/p>\n<p>Third-party risks are managed under Manage 3, and documentation and monitoring plans under Manage 4. Manage 2 sits between them, covering the resources needed to run the response, including the viable non-AI alternatives an organisation considered and rejected.<\/p>\n<p>The seven characteristics of trustworthy AI run underneath all of it: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. But NIST doesn&#8217;t pretend these sit comfortably together. The framework states that trade-offs are usually involved, gives interpretability against privacy and predictive accuracy against interpretability as examples, and says such trade-offs should be resolved in a way that&#8217;s transparent and justifiable.<\/p>\n<p>One line in the framework does more work than its length suggests. Describing the actions under each subcategory, NIST writes that they &#8220;do not constitute a checklist, nor are they necessarily an ordered set of steps.&#8221; The framework is deliberately not a control list, and reading it as one produces a spreadsheet nobody uses.<\/p>\n<p>So does working through all 72 subcategories tell an organisation what to actually do? Not on its own, and the framework never claims it does.<\/p>\n<hr>\n<p>\n\n<figure class=\"ls-infographic-wrap\" style=\"margin:2rem 0;\">\n<div class=\"sa-ig-rmfcore\" style=\"margin:2rem 0;max-width:820px;\">\n<style>\n.sa-ig-rmfcore, .sa-ig-rmfcore *, .sa-ig-rmfcore *::before, .sa-ig-rmfcore *::after { margin:0; padding:0; box-sizing:border-box; }\n.sa-ig-rmfcore { font-family:-apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Arial, sans-serif; color:#212121; }\n.sa-ig-rmfcore .infographic { max-width:820px; margin:0 auto; border:1px solid #e0e0e0; border-radius:10px; overflow:hidden; background:#ffffff; }\n.sa-ig-rmfcore .title-bar { background:#2941ba; color:#ffffff; padding:22px 24px 20px; text-align:center; }\n.sa-ig-rmfcore .ig-title { font-size:21px; font-weight:700; line-height:1.3; letter-spacing:0.1px; }\n.sa-ig-rmfcore .stamp { display:inline-block; margin-top:12px; padding:5px 14px; border-radius:999px; background:#feae2d; color:#21306f; font-size:13px; font-weight:700; letter-spacing:0.3px; }\n.sa-ig-rmfcore .content { padding:22px 24px 16px; }\n.sa-ig-rmfcore .lead { font-size:12.5px; color:#5a6472; font-weight:700; text-transform:uppercase; letter-spacing:0.6px; margin-bottom:14px; }\n.sa-ig-rmfcore .govern { border:2px solid #2941ba; border-radius:10px; background:#f2f5fd; padding:14px 15px 13px; }\n.sa-ig-rmfcore .gv-head { display:flex; align-items:baseline; gap:12px; flex-wrap:wrap; }\n.sa-ig-rmfcore .gv-name { font-size:16.5px; font-weight:800; color:#2941ba; letter-spacing:0.3px; }\n.sa-ig-rmfcore .gv-count { font-size:11.8px; font-weight:700; color:#ffffff; background:#2941ba; border-radius:999px; padding:3px 10px; letter-spacing:0.3px; }\n.sa-ig-rmfcore .gv-note { font-size:12.8px; line-height:1.5; color:#3d4653; margin-top:7px; }\n.sa-ig-rmfcore .inner { margin-top:13px; display:flex; gap:10px; }\n.sa-ig-rmfcore .fn { flex:1 1 0; border:1px solid #dfe3ec; border-radius:8px; background:#ffffff; overflow:hidden; }\n.sa-ig-rmfcore .fn-band { height:7px; }\n.sa-ig-rmfcore .fn-in { padding:12px 13px 13px; }\n.sa-ig-rmfcore .fn-name { font-size:15px; font-weight:700; line-height:1.2; }\n.sa-ig-rmfcore .fn-count { font-size:11.4px; color:#5a6472; font-weight:700; margin-top:4px; letter-spacing:0.2px; }\n.sa-ig-rmfcore .fn-duty { font-size:12.6px; line-height:1.45; color:#313a45; margin-top:8px; }\n.sa-ig-rmfcore .fn-key { font-size:11.4px; line-height:1.45; color:#6b7482; margin-top:8px; padding-top:8px; border-top:1px dotted #dfe3ec; }\n.sa-ig-rmfcore .totals { margin-top:16px; display:flex; gap:10px; }\n.sa-ig-rmfcore .tot { flex:1 1 0; border:1px solid #dfe3ec; border-radius:8px; background:#fbfcfe; padding:11px 13px; text-align:center; }\n.sa-ig-rmfcore .tot-n { font-size:20px; font-weight:800; color:#21306f; line-height:1.15; }\n.sa-ig-rmfcore .tot-l { font-size:11px; color:#6b7482; font-weight:700; text-transform:uppercase; letter-spacing:0.5px; margin-top:3px; }\n.sa-ig-rmfcore .aside { margin-top:16px; border:1px dashed #b9c2d4; border-radius:8px; padding:13px 15px; background:#f6f8fd; }\n.sa-ig-rmfcore .aside-h { font-size:13px; font-weight:700; color:#2941ba; margin-bottom:5px; letter-spacing:0.2px; }\n.sa-ig-rmfcore .aside-t { font-size:12.8px; line-height:1.5; color:#3d4653; }\n.sa-ig-rmfcore .note { margin-top:13px; font-size:11.8px; line-height:1.5; color:#6b7482; }\n.sa-ig-rmfcore .brand { border-top:1px solid #e8ebf1; padding:13px 24px; display:flex; align-items:center; justify-content:space-between; gap:16px; background:#ffffff; }\n.sa-ig-rmfcore .src { font-size:11px; color:#7b8595; line-height:1.45; }\n.sa-ig-rmfcore .brand img { width:132px; height:31px; display:block; flex:0 0 auto; }\n@media (max-width:640px){\n  .sa-ig-rmfcore .inner, .sa-ig-rmfcore .totals { flex-direction:column; }\n  .sa-ig-rmfcore .brand { flex-direction:column; align-items:flex-start; }\n}\n<\/style>\n<div class=\"infographic\">\n  <div class=\"title-bar\">\n    <div class=\"ig-title\">The NIST AI RMF Core: four functions, 19 categories, 72 subcategories<\/div>\n    <div class=\"stamp\">AI RMF 1.0, NIST AI 100-1<\/div>\n  <\/div>\n  <div class=\"content\">\n    <div class=\"lead\">Govern is cross-cutting, not the first step in a cycle<\/div>\n\n    <div class=\"govern\">\n      <div class=\"gv-head\">\n        <div class=\"gv-name\">GOVERN<\/div>\n        <div class=\"gv-count\">6 categories &middot; 23 subcategories<\/div>\n      <\/div>\n      <div class=\"gv-note\">Policies and processes, accountability structures, workforce, risk culture, engagement with external actors, and supply chain risk under Govern 6. NIST calls it a cross-cutting function that is infused throughout AI risk management and enables the other three.<\/div>\n\n      <div class=\"inner\">\n        <div class=\"fn\">\n          <div class=\"fn-band\" style=\"background:#1b7f4d;\"><\/div>\n          <div class=\"fn-in\">\n            <div class=\"fn-name\" style=\"color:#146340;\">MAP<\/div>\n            <div class=\"fn-count\">5 categories &middot; 18 subcategories<\/div>\n            <div class=\"fn-duty\">Establishes context: intended purpose, users, benefits and costs, and where impacts land.<\/div>\n            <div class=\"fn-key\">Map 4 reaches third-party software and data.<\/div>\n          <\/div>\n        <\/div>\n        <div class=\"fn\">\n          <div class=\"fn-band\" style=\"background:#d9822b;\"><\/div>\n          <div class=\"fn-in\">\n            <div class=\"fn-name\" style=\"color:#a85f18;\">MEASURE<\/div>\n            <div class=\"fn-count\">4 categories &middot; 18 subcategories<\/div>\n            <div class=\"fn-duty\">Methods and metrics, evaluation against the trustworthiness characteristics, risk tracking, feedback on the measuring itself.<\/div>\n            <div class=\"fn-key\">Measure 2 alone holds 13 of the 18.<\/div>\n          <\/div>\n        <\/div>\n        <div class=\"fn\">\n          <div class=\"fn-band\" style=\"background:#7a3fb5;\"><\/div>\n          <div class=\"fn-in\">\n            <div class=\"fn-name\" style=\"color:#612f92;\">MANAGE<\/div>\n            <div class=\"fn-count\">4 categories &middot; 13 subcategories<\/div>\n            <div class=\"fn-duty\">Prioritise and treat risk: mitigate, transfer, avoid or accept. Then document and monitor.<\/div>\n            <div class=\"fn-key\">Manage 1.4 documents negative residual risk.<\/div>\n          <\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n\n    <div class=\"totals\">\n      <div class=\"tot\"><div class=\"tot-n\">4<\/div><div class=\"tot-l\">functions<\/div><\/div>\n      <div class=\"tot\"><div class=\"tot-n\">19<\/div><div class=\"tot-l\">categories<\/div><\/div>\n      <div class=\"tot\"><div class=\"tot-n\">72<\/div><div class=\"tot-l\">subcategories<\/div><\/div>\n      <div class=\"tot\"><div class=\"tot-n\">7<\/div><div class=\"tot-l\">trustworthy traits<\/div><\/div>\n    <\/div>\n\n    <div class=\"aside\">\n      <div class=\"aside-h\">Seven characteristics of trustworthy AI<\/div>\n      <div class=\"aside-t\">Valid and reliable &middot; safe &middot; secure and resilient &middot; accountable and transparent &middot; explainable and interpretable &middot; privacy-enhanced &middot; fair with harmful bias managed. NIST states that trade-offs between them are usually involved and must be resolved transparently.<\/div>\n    <\/div>\n\n    <div class=\"note\">Profiles, not version numbers, are how the framework is implemented and extended: use-case, temporal (current and target), and cross-sectoral. The Generative AI Profile, NIST AI 600-1, July 2024, is the cross-sectoral example.<\/div>\n  <\/div>\n  <div class=\"brand\">\n    <div class=\"src\">Source: NIST AI 100-1, AI Risk Management Framework 1.0, January 2023, and the AI RMF Core at the NIST Trustworthy and Responsible AI Resource Center.<\/div>\n    <img decoding=\"async\" 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\" alt=\"SkillArbitrage\">\n  <\/div>\n<\/div>\n<\/div>\n<\/figure>\n\n<a id=\"h2-2\"><\/a><\/p>\n<h2 id=\"applying-the-nist-ai-risk-management-framework-through-profiles\">Applying the NIST AI Risk Management Framework through profiles<\/h2>\n<p>Applying the NIST AI Risk Management Framework happens through profiles, which are the unit of implementation and the part most explainers skip entirely. NIST defines a <a href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/6-sec-profile\/\" target=\"_blank\" rel=\"noopener\">use-case profile<\/a> as an implementation of the framework&#8217;s functions, categories and subcategories &#8220;for a specific setting or application based on the requirements, risk tolerance, and resources of the Framework user.&#8221; The framework supplies the vocabulary. A profile supplies the answers.<\/p>\n<p>There are two other kinds. Temporal profiles describe either the current state or the desired target state of risk management activities in a given sector, organisation or application, and they split into a current profile, which records how AI is being managed now, and a target profile, which records the outcomes needed. Cross-sectoral profiles cover risks of models or applications used across many use cases, such as large language models or cloud-based services.<\/p>\n<p>The method follows from the definitions. Comparing a current profile against a target profile reveals the gaps, and the gaps become an action plan. NIST leaves the prioritisation to the organisation, and it&#8217;s explicit about why: &#8220;While the AI RMF can be used to prioritize risk, it does not prescribe risk tolerance.&#8221; Risk tolerance, the framework says, is highly contextual and can be shaped by legal or regulatory requirements the framework itself doesn&#8217;t impose.<\/p>\n<p>What that pair of profiles looks like for a small firm is short and unglamorous. <em>MAP 1.1, current state: the firm runs a commercial large language model over client contracts to produce first-draft summaries. The intended purpose is named in the service agreement, but the deployment context, the categories of client data in scope, and the known limits of the summaries are recorded nowhere, and no individual owns the decision to widen the tool&#8217;s use. Target state: intended purpose, document categories in scope, categories explicitly out of scope, the named reviewer who signs every summary before it leaves the firm, and the review interval are recorded in one dated, version-controlled document. Gap: one page, one owner, one review date.<\/em><\/p>\n<p>That entry is thin enough to write in an afternoon and specific enough to answer a client questionnaire, which is roughly the standard the framework aims at. The discipline is the same one behind a <a href=\"https:\/\/skillarbitra.ge\/blog\/privacy-impact-assessment-steps-and-template\/\" target=\"_blank\" rel=\"noopener\">privacy impact assessment<\/a>, where a document describing an idealised system rather than the shipped one is worth nothing. And the failure mode is identical too: a profile written from the product roadmap instead of the running deployment fails the first question an assessor asks.<\/p>\n<p>The <a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ai\/NIST.AI.600-1.pdf\" target=\"_blank\" rel=\"noopener\">Generative AI Profile<\/a>, published as NIST AI 600-1 in July 2024, is the reference example of a cross-sectoral profile. It identifies twelve risks unique to or exacerbated by generative AI: CBRN information or capabilities, confabulation, dangerous violent or hateful content, data privacy, environmental impacts, harmful bias or homogenisation, human-AI configuration, information integrity, information security, intellectual property, obscene degrading or abusive content, and value chain and component integration.<\/p>\n<p>But several of those names are doing precise work. NIST defines confabulation as a phenomenon in which systems &#8220;generate and confidently present erroneous or false content in response to prompts,&#8221; and notes it also covers outputs that contradict earlier statements in the same context. The colloquial terms are hallucination and fabrication. The framework prefers confabulation because the behaviour follows from how the models are built, predicting the next token against the statistical distribution of training data, rather than from a malfunction.<\/p>\n<p>The profile&#8217;s second half is a table of suggested actions keyed to individual subcategories. Each action carries an ID whose prefix names the function: GV for govern, MP for map, MS for measure, MG for manage. GV-1.1-001 is the first suggested action for Govern 1.1, GV-1.1-002 the second. And NIST states directly that not every subcategory of the framework appears, because the profile was scoped to four considerations the Generative AI Public Working Group focused on, namely governance, content provenance, pre-deployment testing and incident disclosure.<\/p>\n<p>The profile mechanism is still in active use. On 7 April 2026 NIST published a concept note for an AI RMF Profile on Trustworthy AI in Critical Infrastructure, aimed at operators bringing AI-enabled capabilities into that sector. For everything not covered by a published profile, the <a href=\"https:\/\/airc.nist.gov\/airmf-resources\/playbook\" target=\"_blank\" rel=\"noopener\">AI RMF Playbook<\/a> at the Trustworthy and Responsible AI Resource Center holds the suggested actions, references and documentation prompts for each subcategory, and NIST reviews comments on it on a semi-annual basis.<\/p>\n<p>But a profile nobody audits raises an obvious question about what the whole exercise is worth.<\/p>\n<hr>\n<a id=\"h2-3\"><\/a>\n<h2 id=\"self-assessment-revisions-and-the-iso-42001-crosswalk\">Self-assessment, revisions and the ISO 42001 crosswalk<\/h2>\n<p>Self-assessment is the framework&#8217;s default and its limit, because no NIST certification against the AI RMF exists. NIST publishes the framework, the playbook and the profiles. It doesn&#8217;t accredit auditors, doesn&#8217;t run a certification scheme, and doesn&#8217;t issue any document attesting that an organisation conforms. A vendor page advertising itself as NIST AI RMF certified is describing an attestation that vendor arranged with a private assessor, which is a different claim from the one the wording implies.<\/p>\n<p>The certifiable neighbour is <a href=\"https:\/\/www.iso.org\/standard\/81230.html\" target=\"_blank\" rel=\"noopener\">ISO\/IEC 42001<\/a>, the international management-system standard for artificial intelligence. An external body audits against it and issues a certificate, and the bodies doing that auditing are themselves governed by ISO\/IEC 42006:2025, which sets the requirements for certification bodies working to 42001. That is the structural difference in one line: the ISO standard is a management system that can be certified, and the NIST framework is a risk methodology that can only be adopted. Practitioners who already hold an <a href=\"https:\/\/skillarbitra.ge\/blog\/iso-27001-lead-auditor-course-career-path\/\" target=\"_blank\" rel=\"noopener\">ISO 27001 lead auditor<\/a> qualification will find the management-system half of that pairing familiar, since 42001 follows the same harmonised structure.<\/p>\n<p>The two aren&#8217;t rivals, and NIST has never treated them that way. NIST&#8217;s first published crosswalks map the AI RMF to ISO\/IEC 23894 on AI risk management, and illustrate how the trustworthiness characteristics relate to the OECD Recommendation on AI, the EU AI Act and several other documents. Further crosswalks submitted by the user community are listed at the <a href=\"https:\/\/airc.nist.gov\/AI_RMF_Knowledge_Base\/Crosswalks\" target=\"_blank\" rel=\"noopener\">AIRC crosswalk page<\/a>, on conditions NIST states openly: the mapped resource must be freely available online, the mapping must accurately reflect the framework, and it must not constitute an advertisement.<\/p>\n<p>The framework&#8217;s own status is less settled than it was. Version 1.0 remains the only finalised release, and the <a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" target=\"_blank\" rel=\"noopener\">NIST programme page<\/a> now states that the AI RMF 1.0 is being revised as part of the White House AI Action Plan. That plan, <a href=\"https:\/\/www.whitehouse.gov\/wp-content\/uploads\/2025\/07\/Americas-AI-Action-Plan.pdf\" target=\"_blank\" rel=\"noopener\">Winning the Race: America&#8217;s AI Action Plan<\/a>, was published on 23 July 2025 and directed NIST to remove references to misinformation, diversity, equity and inclusion, and climate change from the framework. As of September 2026 no version 1.1 or 2.0 has been published.<\/p>\n<p>The practical consequence is narrow but real. Organisations that built controls on the framework&#8217;s treatment of those three subjects will need an independent basis for them once the revision lands, because a removed reference is no longer something the framework supports. Everything else in the Core, and the structural argument about profiles above, is unaffected by the change.<\/p>\n<p>Anyone tracking that revision should watch the profiles as closely as the Core. Profiles are where NIST has done nearly all of its substantive work since 2023, and a profile can be issued, amended or withdrawn without touching the version number of the framework it sits under. Based on what the past three years show, the next material change to how the framework is used in practice is more likely to arrive as a new profile than as a version 2.0.<\/p>\n<p>A related sequence is worth keeping straight. The Generative AI Profile was produced under Executive Order 14110, which was revoked in January 2025. The profile itself is a NIST publication and stands on its own footing, so the revocation of the order that commissioned it doesn&#8217;t withdraw it.<\/p>\n<p>For a professional in India the map has three layers rather than one. The NIST framework is voluntary and arrives contractually, the EU AI Act is binding and arrives by jurisdiction, and India&#8217;s own <a href=\"https:\/\/static.pib.gov.in\/WriteReadData\/specificdocs\/documents\/2025\/nov\/doc2025115685601.pdf\" target=\"_blank\" rel=\"noopener\">AI Governance Guidelines<\/a>, released by the Ministry of Electronics and Information Technology on 5 November 2025, are voluntary for now and built on seven guiding principles the document calls sutras: trust, people first, innovation over restraint, fairness and equity, accountability, understandable by design, and safety, resilience and sustainability. MeitY flagged a compliance schedule to follow within nine to twelve months, so the voluntary period has a stated horizon. None of this displaces the <a href=\"https:\/\/skillarbitra.ge\/blog\/dpdp-act-compliance-checklist\/\" target=\"_blank\" rel=\"noopener\">DPDP Act<\/a>, which already governs the personal data flowing through most of these systems.<\/p>\n<p>Across all three layers, the artefact with genuine lead time is the current profile. Writing down what an organisation already does with AI, honestly and in one place, is the input every framework asks for first, and it&#8217;s the only part that can&#8217;t be bought in.<\/p>\n<hr>\n<a id=\"h2-4\"><\/a>\n<h2 id=\"frequently-asked-questions\">Frequently asked questions<\/h2>\n<p><strong>Does the NIST AI Risk Management Framework apply to organisations outside the United States?<\/strong><\/p>\n<p>The framework is intended to be use-case agnostic and voluntary, so organisations outside the United States can also adopt it. In practice it reaches non-US firms through contracts, because US clients name it in vendor questionnaires, making it commercial rather than legal.<\/p>\n<p><strong>Is the Generative AI Profile a separate framework from the AI RMF?<\/strong><\/p>\n<p>NIST AI 600-1 is a companion resource to AI RMF 1.0, not a replacement. It uses the same four functions and the same subcategory numbering, adding twelve generative-AI risks and suggested actions keyed to existing subcategories. Its own text notes that not every subcategory is addressed.<\/p>\n<p><strong>What does the AI RMF say about how much AI risk is acceptable?<\/strong><\/p>\n<p>The framework can be used to prioritise risk but, in its own words, &#8220;does not prescribe risk tolerance&#8221;. Acceptable risk is treated as highly contextual, shaped by an organisation&#8217;s own policies and by whatever law applies. Setting the threshold is left to the organisation.<\/p>\n<p><strong>Which existing roles transfer most directly into AI RMF work?<\/strong><\/p>\n<p>Privacy, compliance, internal audit and information security roles transfer most cleanly, because govern and manage are largely policy and third-party disciplines those teams already run. Recruiting firm Axial Search puts NIST frameworks among the skills US AI governance postings name most often.<\/p>\n<hr>\n<a id=\"h2-5\"><\/a>\n<h2 id=\"references\">References<\/h2>\n<ol>\n<li>NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0), National Institute of Standards and Technology, January 2023. https:\/\/nvlpubs.nist.gov\/nistpubs\/ai\/NIST.AI.100-1.pdf<\/li>\n<li>NIST AI 600-1, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, National Institute of Standards and Technology, July 2024. https:\/\/nvlpubs.nist.gov\/nistpubs\/ai\/NIST.AI.600-1.pdf<\/li>\n<li>AI Risk Management Framework, programme page, National Institute of Standards and Technology. https:\/\/www.nist.gov\/itl\/ai-risk-management-framework<\/li>\n<li>AI RMF Core and AI RMF Profiles, Trustworthy and Responsible AI Resource Center, NIST. https:\/\/airc.nist.gov\/airmf-resources\/airmf\/5-sec-core\/ and https:\/\/airc.nist.gov\/airmf-resources\/airmf\/6-sec-profile\/<\/li>\n<li>AI RMF Crosswalks, Trustworthy and Responsible AI Resource Center, NIST. https:\/\/airc.nist.gov\/AI_RMF_Knowledge_Base\/Crosswalks<\/li>\n<li>Winning the Race: America&#8217;s AI Action Plan, Executive Office of the President, 23 July 2025. https:\/\/www.whitehouse.gov\/wp-content\/uploads\/2025\/07\/Americas-AI-Action-Plan.pdf<\/li>\n<li>India AI Governance Guidelines, Ministry of Electronics and Information Technology, 5 November 2025. https:\/\/static.pib.gov.in\/WriteReadData\/specificdocs\/documents\/2025\/nov\/doc2025115685601.pdf<\/li>\n<li>ISO\/IEC 42001:2023, Information technology, Artificial intelligence, Management system, International Organization for Standardization. https:\/\/www.iso.org\/standard\/81230.html<\/li>\n<\/ol>\n<hr>\n<p><em>This article is general educational information about a voluntary risk management framework, current as of 8 September 2026. It is not legal, compliance or professional advice on any specific AI system, product or contract. Whether a particular framework, standard or regulation applies to an organisation depends on facts this article cannot see, and a qualified professional should review that question before any commitment is made.<\/em><\/p>\n\n<!-- \/wp:post-content -->\n\n<style>.ls-cta-br{display:none;}@media(max-width:768px){#ls-floating-cta{padding:8px 12px !important;}#ls-floating-cta .ls-wrap{flex-direction:column !important;align-items:center !important;gap:8px !important;}#ls-floating-cta a{font-size:11px !important;padding:8px 16px !important;white-space:normal !important;text-align:center !important;max-width:90vw !important;}.ls-cta-br{display:block !important;}}<\/style><div id=\"ls-floating-cta\" style=\"position:fixed;bottom:0;left:0;right:0;z-index:9999;background:#0f0f0f;border-top:3px solid #2941BA;padding:12px 20px;box-shadow:0 -4px 20px rgba(0,0,0,0.3);\"><div class=\"ls-wrap\" style=\"display:flex;align-items:center;justify-content:center;gap:24px;\"><div style=\"display:flex;align-items:center;gap:10px;\"><a href=\"https:\/\/growthx.lawsikho.com\/f\/14may-id-30day-lpcore1?p_source=id2_blog_sa&#038;p_cta=sa-id-nist-ai-risk-management-framework\" onclick=\"gtag(&#039;event&#039;,&#039;cta_click&#039;,{send_to:&#039;G-B23VVGPQ92&#039;,p_source:&#039;id2_blog_sa&#039;,p_cta:&#039;sa-id-nist-ai-risk-management-framework&#039;});\" target=\"_blank\" rel=\"noopener\" style=\"display:inline-block;background:#2941BA;color:#fff;padding:11px 20px;border-radius:7px;font-size:13px;font-weight:700;text-decoration:none;white-space:nowrap;\">Become a board-ready Independent Director in 30 days \u2014<br class=\"ls-cta-br\"> Rs. 100 \u2192<\/a><button onclick=\"document.getElementById('ls-floating-cta').style.display='none'\" style=\"background:none;border:none;color:#555;font-size:18px;cursor:pointer;padding:4px;line-height:1;position:absolute;right:16px;\">\u2715<\/button><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The NIST AI Risk Management Framework is a voluntary US framework of four functions, govern, map, measure and manage, across 19 categories and 72 subcategories<\/p>\n","protected":false},"author":35,"featured_media":4994,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[1576,1352,1726,1727,1724,1731,1732,1729,1725,1728,1730,1721,1722,1723],"class_list":["post-4993","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","tag-ai-compliance","tag-ai-governance","tag-ai-governance-framework","tag-ai-risk-assessment","tag-ai-risk-management","tag-ai-risk-management-framework","tag-ai-risk-management-standards","tag-generative-ai-profile","tag-iso-42001","tag-nist-ai-100-1","tag-nist-ai-600-1","tag-nist-ai-risk-management-framework","tag-nist-ai-rmf","tag-responsible-ai"],"_links":{"self":[{"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/posts\/4993","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/users\/35"}],"replies":[{"embeddable":true,"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/comments?post=4993"}],"version-history":[{"count":2,"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/posts\/4993\/revisions"}],"predecessor-version":[{"id":5003,"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/posts\/4993\/revisions\/5003"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/media\/4994"}],"wp:attachment":[{"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/media?parent=4993"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/categories?post=4993"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/old.skillarbitra.ge\/blog\/wp-json\/wp\/v2\/tags?post=4993"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}