Generative AI Profile (AI 600-1)
governmentCredibility Rating
5/5
Gold(5)Gold standard. Rigorous peer review, high editorial standards, and strong institutional reputation.
Rating inherited from publication venue: NIST
An official U.S. government framework document from NIST; highly relevant for organizations seeking compliance guidance or structured risk management approaches for generative AI, and a key reference for AI governance discussions in policy and industry contexts.
Metadata
Importance: 72/100guidance documentreference
Summary
NIST AI 600-1 is a specialized profile of the AI Risk Management Framework (AI RMF 1.0) focused on generative AI systems, identifying 12 risk categories unique to or exacerbated by generative AI such as confabulation, CBRN information disclosure, harmful bias, and data privacy. It provides concrete suggested actions for organizations to manage these risks across the AI lifecycle. Developed in response to President Biden's Executive Order on AI, it serves as a practical governance companion to the broader AI RMF.
Key Points
- •Identifies 12 generative AI-specific risk categories including confabulation, data privacy, harmful bias, information integrity, and CBRN information disclosure.
- •Provides suggested actions mapped to AI RMF functions (Govern, Map, Measure, Manage) for operationalizing risk management in generative AI contexts.
- •Developed by NIST under President Biden's Executive Order on Safe, Secure, and Trustworthy AI, giving it official U.S. government policy backing.
- •Covers risks across the full AI lifecycle: design, development, deployment, and operation of generative AI systems.
- •Serves as a practical companion to AI RMF 1.0, helping organizations embed trustworthiness into generative AI products and services.
Cited by 1 page
| Page | Type | Quality |
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| AI Standards Development | Concept | 69.0 |
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NIST Trustworthy and Responsible AI
NIST AI 600-1
Artificial Intelligence Risk Management
Framework: Generative Artificial
Intelligence Profile
This publication is available free of charge from:
https://doi.org/10.6028/NIST.AI.600-1
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NIST Trustworthy and Responsible AI
NIST AI 600-1
Artificial Intelligence Risk Management
Framework: Generative Artificial
Intelligence Profile
This publication is available free of charge from:
https://doi.org/10.6028/NIST.AI.600-1
July 2024
U.S. Department of Commerce
Gina M. Raimondo, Secretary
National Institute of Standards and Technology
Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology
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About AI at NIST: The National Institute of Standards and Technology (NIST) develops measurements,
technology, tools, and standards to advance reliable, safe, transparent, explainable, privacy-enhanced,
and fair artificial intelligence (AI) so that its full commercial and societal benefits can be realized without
harm to people or the planet. NIST, which has conducted both fundamental and applied work on AI for
more than a decade, is also helping to fulfill the 2023 Executive Order on Safe, Secure, and Trustworthy
AI. NIST established the U.S. AI Safety Institute and the companion AI Safety Institute Consortium to
continue the efforts set in motion by the E.O. to build the science necessary for safe, secure, and
trustworthy development and use of AI.
Acknowledgments: This report was accomplished with the many helpful comments and contributions
from the community, including the NIST Generative AI Public Working Group, and NIST staff and guest
researchers: Chloe Autio, Jesse Dunietz, Patrick Hall, Shomik Jain, Kamie Roberts, Reva Schwartz, Martin
Stanley, and Elham Tabassi.
NIST Technical Series Policies
Copyright, Use, and Licensing Statements
NIST Technical Series Publication Identifier Syntax
Publication History
Approved by the NIST Editorial Review Board on 07-25-2024
Contact Information
ai-inquiries@nist.gov
National Institute of Standards and Technology
Attn: NIST AI Innovation Lab, Information Technology Laboratory
100 Bureau Drive (Mail Stop 8900) Gaithersburg, MD 20899-8900
Additional Information
Additional information about this publication and other NIST AI publications are available at
https://airc.nist.gov/Home.
Disclaimer: Certain commercial entities, equipment, or materials may be identified in this document in
order to adequately describe an experimental procedure or concept. Such identification is not intended to
imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it
intended to imply that the entities, materials, or equipment are necessarily the best available for the
purpose. Any mention of commercial, non-profit, academic partners, or their products, or references is
for information only; it is not intended to imply endorsement or recommendation by any U.S.
Government agency.
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