⚡ Quick Summary
Published by the National Institute of Standards and Technology, this report is a cross-sectoral profile and companion resource for the AI Risk Management Framework (AI RMF 1.0) focused on generative AI (GAI). Developed pursuant to Executive Order 14110, it is intended for voluntary use by organisations seeking to incorporate trustworthiness considerations into the design, development, use and evaluation of AI products, services and systems. It treats a profile as an implementation of AI RMF functions, categories and subcategories tailored to a technology, setting and user risk tolerance.
The document defines 12 risks that are unique to or exacerbated by GAI, then maps suggested actions to relevant AI RMF subcategories and Trustworthy AI Characteristics. The risks span CBRN information or capabilities, confabulation, harmful content, data privacy, environmental impacts, harmful bias and homogenization, human-AI configuration, information integrity and security, intellectual property, abusive content, and value-chain integration. Its actionable core is organised around the AI RMF’s Govern, Map, Measure and Manage functions. Appendix A develops four primary considerations from the Generative AI Public Working Group: governance, pre-deployment testing, content provenance and incident disclosure.
🧩 What’s Covered
The profile moves from a GAI-specific risk taxonomy to implementation actions aligned with the AI RMF.
- Purpose and scope: The introduction explains that the profile applies the AI RMF to generative foundation models and can support cross-sector activities such as use of large language models, cloud services and acquisition. It notes that implementation varies with lifecycle stage, system characteristics, AI actors, resources and risk tolerance.
- GAI risk overview: Twelve defined risks cover model behaviour, human misuse and ecosystem effects. The discussion distinguishes risks by lifecycle stage, scope, source and timescale, and links each risk to one or more Trustworthy AI Characteristics such as safety, privacy, reliability, accountability and security.
- Govern actions: Suggested actions address legal and regulatory requirements, risk tiers and go/no-go thresholds, inventories, acceptable-use policies, roles, deactivation protocols, stakeholder feedback, incident processes and third-party contracts. Examples include documenting training-data origin, defining incident-reporting criteria and updating procurement diligence for GAI.
- Map actions: The Map tables address intended use, assumptions, knowledge limits, data and content flows, context-specific impacts and system dependencies. They call for documentation of foreseeable misuse, data provenance, third-party intellectual-property and privacy risks, and impacts on individuals, communities and the physical environment.
- Measure actions: The profile recommends test, evaluation, validation and verification (TEVV), including ground-truth comparison, fact-checking, adversarial testing, red-teaming, fairness assessments, content-provenance metrics and monitoring. It also identifies the need to document risks that cannot be quantitatively measured.
- Manage actions: Management measures include monitoring controls, applying risk tolerances to releases and third-party resources, filtering inputs and outputs, tracking provenance, handling incidents and deactivating systems that operate outside defined limits.
- Primary considerations: Appendix A discusses adapting organisational governance, limitations of current pre-deployment testing, structured public feedback methods, provenance metadata and techniques such as watermarking, and incident documentation and disclosure.
💡 Why it matters?
The profile gives governance, development, deployment and assurance teams a shared way to translate general AI RMF activities into GAI-specific practices. Its action IDs and links to AI RMF subcategories make it possible to connect risk registers, lifecycle documentation, evaluation plans and operational controls to particular concerns such as confabulation, prompt injection, data leakage or synthetic-content provenance.
It also makes clear that risk management is not confined to pre-deployment testing. The suggested actions cover supplier diligence, stakeholder engagement, ongoing monitoring, incident response, recovery and decommissioning, helping organisations treat GAI risks as lifecycle and value-chain issues.
❓ What’s Missing
The profile is explicitly selective rather than exhaustive. Its primary considerations were limited to governance, content provenance, pre-deployment testing and incident disclosure, and it states that not every AI RMF subcategory is included. Suggested actions do not all apply to every AI actor or task, and the document leaves organisations to determine applicability according to their particular systems and uses. It does not establish universal risk thresholds, prescribed metrics or mandatory compliance requirements; the AI RMF is described as voluntary. The document also notes that there is no agreed method for estimating GAI environmental impacts and that current pre-deployment approaches may be inadequate, non-systematic or mismatched to deployment contexts.
👥 Best For
Best suited to AI governance and oversight teams adapting AI RMF processes for GAI; developers and TEVV teams designing evaluations, red-teaming and documentation; procurement and legal teams assessing third-party models and data; and operational teams responsible for monitoring, incident response, content provenance and deactivation decisions.
📄 Source Details
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile is NIST AI 600-1, published by the National Institute of Standards and Technology in July 2024. No individual authors are named. The supplied complete PDF contains 64 pages, with internal pagination ending at 60. The publication URL printed in the document is https://doi.org/10.6028/NIST.AI.600-1.
