Why read this
Read this when AI activity is spreading across teams and the company needs a governance process that makes ownership and evidence visible.
Govern the use case, not only the model
US AI governance should start with a use-case register that names the workflow, users, data, affected parties, decision authority, supplier dependency, and harm path. This makes it easier to connect NIST-style risk management to the work people actually perform.
Evidence: NIST AI Risk Management Framework, NIST Generative AI Profile
Ask for evidence before authority
A policy does not prove that a system is controlled. Keep evidence for evaluation, access, security, human review, user training, supplier claims, incident handling, model changes, and monitoring. The evidence pack should be understandable to business, legal, risk, security, and operational owners.
Evidence: NIST AI Risk Management Framework, CISA AI cybersecurity guidance
Protect against unsupported claims
Buyers should be careful with broad claims that a system is intelligent, unbiased, safe, or fully automated. Governance should require claim evidence, limitations, review obligations, and a correction path before AI output influences people, money, compliance, safety, or customer service.
Evidence: FTC guidance on AI claims
Questions for the buying team
- Which AI use cases are active, planned, blocked, or retired?
- What evidence is required before a system receives more authority?
- Who can pause the workflow when quality, safety, legal, cyber, or customer risk changes?
Local evidence boundary: this guide organises questions and sources. It is not a legal, security, clinical, financial, procurement, or implementation approval.
Sources and further reading
- NIST AI Risk Management Framework standards guidance
- NIST Generative AI Profile standards guidance
- FTC guidance on AI claims standards guidance
- CISA AI cybersecurity guidance standards guidance