statements ยท statement-BATCH-2026-006-005
source-BATCH-2026-005-002
Organizations should document where training and generated data came from and how those data evolved, while accounting for proprietary constraints.
Statement context
- Statement type
- recommendation
- Exact source locator
- PDF file p. 18 (document p. 14), GOVERN 1.2, Action GV-1.2-001, July 2024 version
- Source date
- Unknown
- Role at source time
- institutional author of the NIST AI RMF profile
- Evidence character
- normative recommendation
- Scope
- Voluntary cross-sector generative-AI risk management.
Recorded uncertainty: NIST does not evaluate the effectiveness of this action in the profile.
Indexed source: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
Evidence lineage and transparency
| Relationship field | Linked identifiers |
|---|---|
| topic ids | topic-ai-agents, topic-ai-governance, topic-cybersecurity, topic-non-human-identity |
Machine review: machine checked. Human review: approved. Workflow: published.
Complete structured record
- statement id
- statement-BATCH-2026-006-005
- source id
- source-BATCH-2026-005-002
- person id
- Unknown
- institutional author
- National Institute of Standards and Technology
- book edition id
- Unknown
- speaker role at source time
- institutional author of the NIST AI RMF profile
- organization at source time
- National Institute of Standards and Technology
- statement type
- recommendation
- neutral paraphrase
- Organizations should document where training and generated data came from and how those data evolved, while accounting for proprietary constraints.
- direct quote
- Unknown
- direct quote rights note
- Unknown
- exact locator
- PDF file p. 18 (document p. 14), GOVERN 1.2, Action GV-1.2-001, July 2024 version
- locator type
- pdf_page
- source date
- Unknown
- topic ids
- topic-ai-agents, topic-ai-governance, topic-cybersecurity, topic-non-human-identity
- executive role context
- role-ciso, role-cio, role-cto, role-general-counsel, role-board-director
- industry context
- cross-sector
- geographic context
- United States, cross-sectoral global applicability
- evidence character
- normative_recommendation
- factual verification status
- not_applicable
- statement scope
- Voluntary cross-sector generative-AI risk management.
- uncertainty
- NIST does not evaluate the effectiveness of this action in the profile.
- extraction method
- Machine-assisted close reading of the exact verified source version; original neutral paraphrase; human review pending.
- machine extraction confidence
- 0.94
- independent agent review status
- not_started
- 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": "Exact verified source version inherited from BATCH-2026-005 and statement-level close reading",
"exact_locator": "PDF file p. 18 (document p. 14), GOVERN 1.2, Action GV-1.2-001, July 2024 version",
"content_hash": "6e73620ab6b64e90ef2c04bf0e0d6246185a2f4b1b13cab0df494496cff89b6a",
"batch_id": "BATCH-2026-006",
"prompt_id": "OEII-STATEMENT-CODING",
"prompt_version": "2.0",
"notes": "Source eligibility inherited from source-BATCH-2026-005-002; human attribution and publication 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-2026-006"
}
]
}