sources · source-BATCH-2026-005-002

Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

Accepted discovery candidate candidate-BATCH-2026-001-007; contributes government standards profile evidence or normative context to governed AI-agent identity.

Open the canonical original source

Source at a glance

Source type: public policy document
Publisher: National Institute of Standards and Technology
Source or access date: 2026-08-15
Analysis depth: deeply analyzed
Rights treatment: link and paraphrase
Human review: Murray Newlands · 2026-08-16

What the source reports or argues

The profile organizes suggested actions across governance, mapping, measurement, and management functions.
It explicitly warns that pre-deployment tests and benchmark results may not generalize to real-world contexts.

Important limitations

No quantitative sample or outcome evaluation.
Public-input synthesis method and denominator are not disclosed.

Source-located statements

5 reviewed statements are indexed from this source.

NIST frames this publication as a voluntary, cross-sector companion to the AI Risk Management Framework for generative-AI risk decisions. (PDF file p. 5 (document p. 1), Section 1 "Introduction", July 2024 version)
Organizations should document where training and generated data came from and how those data evolved, while accounting for proprietary constraints. (PDF file p. 18 (document p. 14), GOVERN 1.2, Action GV-1.2-001, July 2024 version)
Organizations should assess risk-relevant generative-AI capabilities and the robustness of safeguards before deployment and repeatedly afterward. (PDF file p. 18 (document p. 14), GOVERN 1.2, Action GV-1.2-002, July 2024 version)
NIST cautions that laboratory tests and benchmark datasets may not transfer to heterogeneous real deployment conditions or measure broader impacts. (PDF file p. 53 (document p. 49), Appendix A.1.4 "Limitations of Current Pre-deployment Test Approaches", July 2024 version)
Organizations can use structured public feedback to test whether a system behaves as intended and to inform lifecycle decisions. (PDF file p. 53 (document p. 49), Appendix A.1.5 "Structured Public Feedback", July 2024 version)

Evidence lineage and transparency

No additional published relationship records are attached.

Machine review: machine checked. Human review: approved. Workflow: published.

Complete structured record

source id: source-BATCH-2026-005-002
canonical title: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
alternate titles
source type: public_policy_document
series or parent source: NIST AI 600-1
publisher: National Institute of Standards and Technology
channel: Unknown
speaker ids
author ids
institutional author: National Institute of Standards and Technology
organization references
recorded at: Unknown
event date: Unknown
published at: Unknown
updated at: Unknown
duration seconds: Unknown
language: lang-en
translated title: Unknown
translation method: Unknown
geography of speaker
geography of organization
geography discussed: United States, cross-sectoral global applicability
study geography: United States, cross-sectoral global applicability
original url: https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
canonical url: https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
archived url: Unknown
embed url: Unknown
doi: 10.6028/NIST.AI.600-1
canonical identity status: verified
repost status: original_or_authoritative_rendition
original source id: Unknown
rights status: link_and_paraphrase
ownership status: third_party
relationship to off: none_identified; Executive AI Research snapshot contains no matching production records
transcript status: Unknown
transcript source: Unknown
transcript republication permission: Unknown
chapter markers
analysis basis: Complete official 64-page PDF reviewed; governance action tables and Appendix A limitation text visually inspected.
topics: topic-ai-agents, topic-ai-governance, topic-cybersecurity, topic-non-human-identity
executive roles: role-ciso, role-cio, role-cto, role-general-counsel, role-board-director
original abstract: Unknown
inclusion rationale: Accepted discovery candidate candidate-BATCH-2026-001-007; contributes government standards profile evidence or normative context to governed AI-agent identity.
source quality dimensions: {"attribution_strength":"high","methodological_transparency":"moderate_for_consensus_process","independence":"government_publisher","bibliographic_stability":"high_doi_resolved","evidence_strength":"strong_normative_reference_not_empirical_outcome_evidence","unresolved":"PDF metadata shows a 2025 modification date without a visible new edition statement."}
methodology quality: {"design":"non-empirical cross-sectoral risk-management profile informed by multistakeholder public working-group feedback and public comments","transparency":"moderate_for_consensus_process","human_review_required":true}
study design: non-empirical cross-sectoral risk-management profile informed by multistakeholder public working-group feedback and public comments
sample: {"size":"not applicable for a normative profile; contributor and commenter counts not reported","sampling_method":"open multistakeholder process; selection details not reported"}
population: organizations designing, developing, deploying, or using generative AI
date range: {"fieldwork":"not reported","version":"July 2024; Editorial Review Board approval 2024-07-25; retrieved PDF SHA-256 recorded"}
funding: U.S. Department of Commerce/NIST publication; separate research funding not reported
sponsor: National Institute of Standards and Technology
peer review status: NIST Editorial Review Board; not a peer-reviewed academic study
findings: The profile organizes suggested actions across governance, mapping, measurement, and management functions., It explicitly warns that pre-deployment tests and benchmark results may not generalize to real-world contexts.
limitations: No quantitative sample or outcome evaluation., Public-input synthesis method and denominator are not disclosed.
correction ids
retraction status: none_identified_in_pdf_or_official_doi_resolution_as_of_2026-08-15
content hash: 6e73620ab6b64e90ef2c04bf0e0d6246185a2f4b1b13cab0df494496cff89b6a
accessed at: 2026-08-15
verification status: metadata_content_and_version_machine_verified_human_approved
source depth: deeply_analyzed
publication status: published
workflow status: published
machine review status: machine_checked
human review status: approved
reviewed by: Murray Newlands
reviewed at: 2026-08-16T23:17:22Z

Provenance and revision history

{
  "provenance": [
    {
      "source_url": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf",
      "accessed_at": "2026-08-15",
      "retrieval_method": "official source retrieval and machine-assisted review",
      "exact_locator": "July 2024; Editorial Review Board approval 2024-07-25; retrieved PDF SHA-256 recorded",
      "content_hash": "6e73620ab6b64e90ef2c04bf0e0d6246185a2f4b1b13cab0df494496cff89b6a",
      "batch_id": "BATCH-2026-005",
      "prompt_id": "OEII-EVIDENCE-RESEARCH",
      "prompt_version": "2.0",
      "notes": "Human review pending."
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  "revision_history": [
    {
      "changed_at": "2026-08-16T23:17:22Z",
      "changed_by": "Murray Newlands",
      "summary": "Approved for the governed-identities pilot release under the exact scope, exclusions, rights treatment, and limitations recorded in issue #18.",
      "batch_id": "BATCH-2026-005"
    }
  ]
}

Open machine-readable record