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Turn agent activity into NIST AI RMF evidence

Prefactor observes every agent action, evaluates quality and risk, and acts on policy at runtime — generating the evidence NIST AI RMF actually asks for, from real production data.

Last updated 29 June 2026 Key dates: Published Jan 2023; voluntary

NIST AI Risk Management Framework, issued by US National Institute of Standards and Technology, applies to us federal ai deployments; broadly adopted in industry. This page covers what affects AI agent teams specifically and how to map controls to it.

Source: official NIST AI RMF reference. This page is practical guidance — confirm interpretation with your counsel.

Voluntary framework United States (global use)

Any organisation that designs, develops, deploys or uses AI — adoption is voluntary.

Where it applies
🇪🇺European Union🇬🇧United Kingdom🇺🇸United States🇨🇦Canada🇧🇷Brazil🇰🇷South Korea🇯🇵Japan🇦🇺Australia🇸🇬Singapore
Evaluate
Govern

What it requiresAccountability, policies and culture for AI risk

How Prefactor addresses itPer-agent ownership and registry, policy-as-code, and versioned eval and policy history make governance accountable and documented.

Evaluate
Map

What it requiresIdentify context and where risk arises

How Prefactor addresses itTrace data maps every action, tool call and decision, and surfaces where an agent has drifted from its design.

Evaluate
Measure

What it requiresAnalyse, benchmark and track AI risks

How Prefactor addresses itEval suites score quality, groundedness and cost on real traffic, with benchmarks and regression detection.

Act
Manage

What it requiresPrioritise and act on risks

How Prefactor addresses itRuntime guardrails block, throttle or route high-risk actions, with human-in-the-loop and full incident traces.

Evaluate
Continuous monitoring

What it requiresTrack performance after deployment

How Prefactor addresses itDrift detection and alerting on quality, cost and behaviour — a live feed, not a periodic check.

Evaluate
Transparency & documentation

What it requiresProvide evidence and disclosure

How Prefactor addresses itEval results, trace samples and policy history per agent version, exportable as dated technical documentation.

Frequently asked questions

Does using a 'compliant' provider make us compliant?
No. Deployers have independent obligations under most frameworks.
Can Prefactor make us compliant?
Prefactor provides the technical and operational layer. Full compliance requires legal, organizational, and product decisions too.
Key provisions for AI agents
  • Four functions: Govern, Map, Measure, Manage
  • Trustworthy AI characteristics
  • Lifecycle approach
  • GenAI Profile (NIST AI 600-1) for generative AI
Who is affected

US federal AI deployments; broadly adopted in industry

Evidence collection

Auditors and reviewers typically expect:

  • Continuous, dated evidence — not point-in-time snapshots
  • Override and intervention records — proof humans actually retained control
  • Eval results tied to specific agent versions
  • Risk decisions tied to changes
  • Incident records, even minor ones
  • Plain-language documentation
Common gaps in NIST AI RMF for AI agents

1. Logs not tamper-evident — application database isn't audit evidence.

2. Human oversight is theoretical — system allows override but nobody uses it.

3. Post-market monitoring is reactive — only investigated when something breaks.

4. No change management — prompts edited in production with no record.

5. Retrieval corpus not in scope of data governance — only training data is considered.

Implementation timeline

30 days: Inventory agents in scope. Begin technical documentation. Enable comprehensive tamper-evident logging.

90 days: Operate risk management. Stand up human oversight. Establish post-market monitoring cadence. First self-assessment.

180 days: Complete documentation. Pre-conformity review. Incident reporting workflow. Full readiness.

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