research · 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.

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

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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."
    }
  ],
  "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"
    }
  ]
}

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