U.S. AI Procurement Checklist: Model Cards, Data Rights, Testing, and Exit Plans

A responsible AI purchase defines the decision being supported, affected people, unacceptable errors, data flows, human review, security controls, and a way to stop using the system. Use the NIST AI Risk Management Framework as a vocabulary, then demand product-specific evidence: evaluation results, limitations, retention, training-use terms, sub-processors, incident response, accessibility, and export or deletion procedures.

A practical workflow

1. Write a use-case and harm statement before comparing vendors.
2. Map input, output, logs, prompts, embeddings, and human decisions.
3. Request documentation for the exact model, version, and deployment mode.
4. Pilot with representative, privacy-safe data and pre-agreed pass/fail criteria.
5. Contract for monitoring, notice of material changes, audit evidence, and exit.

Before you submit, buy, or publish

• Separate vendor marketing from measured performance.
• Test language, disability, geography, and edge-case impacts relevant to users.
• Restrict confidential data until contractual and technical controls are verified.
• Assign an owner for post-launch review.

Caveats that matter

NIST’s framework is voluntary guidance, not a guarantee or a substitute for sector law. AI capabilities, terms, and regulations change quickly.

Frequently asked question

Can a benchmark prove the model is safe for our workflow? No. Task-specific validation and governance are necessary.

Primary source

Use the official source for current eligibility, forms, dates, fees, limits, and policy changes: Open the primary source. Verify the page immediately before acting; this guide is educational and is not legal, tax, immigration, or financial advice.