Five evidence requirements for a public-sector AI proposal

01. What the sources establish

NIST's Generative AI Profile is voluntary guidance for identifying and managing risks specific to generative AI.

02. Our interpretation and proposed approach

Our proposal checklist: representative evaluation documents; data location and rights; access controls, logs and incident response; total hardware and maintenance costs; correction and human approval. This is a planning aid, not a certification of procurement or legal compliance.

03. Questions for your own workflow

These are proposed review questions. They do not establish requirements for a specific institution.

Agree evaluation data and reference answers in advance
Include hardware, maintenance and model-update costs
Retain citations and correction records
Define actions that can run without human approval

Source documents

Check each issuer and publication date. Media reporting is explicitly labeled.

Official source Artificial Intelligence Risk Management Framework: Generative AI Profile ↗NIST · 2024-07-26

Sources reviewed: 2026-11-05. For a correction, provide this page URL and the relevant source through SEJONG-AI.COM.

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Define the AI project scope
and the evidence for acceptance.

Use representative documents, priority tasks and operating constraints to make the review concrete. Discuss local LLM architecture, evaluation and handover scope.

Institution-specific requirements and acceptance conditions are agreed for the actual project.

Prepare your inquiry

We are reviewing a public-sector AI project. Target document formats are [formats], priority tasks are [tasks], and security and operating constraints are [conditions]. We would like to clarify scope, evaluation data, acceptance criteria and operating responsibilities.