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Standards guide · NIST AI 600-1

NIST Generative AI Profile for production agentic systems

Generative and agentic systems introduce or intensify risks involving confabulation, data, human reliance, information integrity, cybersecurity, model dependencies, and scale. A profile helps organize those risks; it does not automatically validate an implementation.

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What is NIST AI 600-1?

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NIST AI 600-1 is the Generative Artificial Intelligence Profile for the AI RMF. It is a cross-sectoral companion resource that helps organizations identify risks that are distinctive to or intensified by generative AI and select actions aligned with their context and priorities. It is not a complete implementation plan or certification.

  • Use the profile with the AI RMF Core, not instead of it.
  • Select risks and actions based on the actual use, system boundary, affected parties, and consequence.
  • Connect selected actions to evaluation scenarios and production evidence.

Source basis: reviewed official references are listed on this page and in the source ledger. Interpretation and implementation guidance retain the stated assurance boundary.

Agentic focus

Where the profile becomes operational

Agentic systems compound model-level risk through memory, tools, handoffs, authority, and repeated execution.

Information integrity

Outputs become inputs

A plausible but wrong output can be stored, delegated, or executed downstream. Test provenance, contradiction handling, and semantic handoffs.

Cybersecurity

Natural language reaches tools

Prompt injection, tool misuse, dependency risk, and unexpected execution require identity, isolation, authorization, and adversarial scenarios.

Human factors

Confidence can mislead operators

Design authority thresholds, explanations, workload, and escalation to prevent automation bias and approval fatigue.

Data and privacy

Context can expose or retain data

Document collection, retrieval, retention, redaction, cross-session memory, and model-provider handling.

Measurement

Static benchmarks are insufficient

Evaluate representative workflows, intermediate steps, failures, cost, latency, and change—not only final-answer accuracy.

Ecosystem risk

Models and tools are dependencies

Track providers, versions, prompts, connectors, libraries, endpoints, permissions, and exit paths in an AIBOM.

Evidence workflow

Apply the profile without creating a paper exercise

Use the profile to choose testable, owned actions.

  1. Bound the system

    Define the business objective, decisions, users, models, data, tools, memory, environments, and authority.

  2. Select relevant risks

    Choose profile risks based on intended use, foreseeable misuse, affected parties, and operational consequence.

  3. Design controls and scenarios

    Connect each risk to preventive, detective, responsive, and recovery controls plus representative tests.

  4. Set evidence thresholds

    Define what must be true before release and what signals trigger escalation, rollback, or suspension.

  5. Review after change

    Re-run relevant tests when models, prompts, data, tools, permissions, or workflow topology change.

Primary-source basis

Official references

Use the named primary sources for current definitions, dates, versions, and scope.

National Institute of Standards and Technology · Official Profile

NIST AI 600-1: Generative AI Profile

A cross-sectoral companion profile for applying the AI RMF to risks that are distinctive to or intensified by generative AI.

Status
Current
Published
2024-07-26
Reviewed
2026-08-01

Use boundary: Use as a companion profile, not as proof that an implementation is compliant or safe.

Open official source

National Institute of Standards and Technology · Official Framework Hub

NIST AI Risk Management Framework

NIST's voluntary framework for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI systems.

Status
Current With Revision Underway
Published
2023-01-26
Reviewed
2026-08-01

Use boundary: Use as risk-management guidance; do not describe alignment as certification or legal compliance.

Open official source

National Institute of Standards and Technology · Official Resource Center

NIST AI Resource Center

NIST's resource center for the AI RMF, profiles, crosswalks, playbook material, use cases, and revision notices.

Status
Current
Reviewed
2026-08-01

Use boundary: Use this hub to confirm freshness before publishing time-sensitive NIST statements.

Open official source

Official-source citations establish provenance and scope. They do not establish LongTermIntelligence.com certification, endorsement, legal advice, client outcomes, or a guarantee that a control is effective.

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