THE SHORT ANSWER

Evaluate the use case first, then assess vendor evidence, model limits, data handling, security, permissions, human controls, monitoring, incidents, changes, subcontractors, service continuity and exit. Verify important claims with your own representative tests and contract terms.

Start with the use, not the leaderboard

A general benchmark may not represent your language, documents, edge cases, latency, cost or harm. Define the task and minimum acceptable performance before comparing providers.

Review the complete service boundary

  • Data use, location, retention and deletion
  • Security, identity and permission controls
  • Model and feature change notifications
  • Evaluation evidence and known limitations
  • Logging, monitoring and incident communication
  • Subprocessors, dependencies and continuity
  • Export, deletion, transition and exit support

Separate evidence from assurance language

Ask what a claim measures, when it was tested, on which population and by whom. Certifications may cover a management system or scope rather than the performance of your configured workflow.

Do not outsource the business decision

The provider controls parts of the technology; the deploying organization controls purpose, configuration, connected data, user access and many consequences. Assign an internal owner, test in context and maintain an exit path.

Pair this review with the existing vendor security risk guide and model-selection guide.

Evidence & context: National Institute of Standards and Technology · International Organization for Standardization

Sources & further reading

  1. Generative Artificial Intelligence Profile (NIST AI 600-1)

    NIST. Risk-management guidance, including confabulation. It does not establish a universal error rate.

  2. Artificial Intelligence Risk Management Framework (AI RMF 1.0)

    National Institute of Standards and Technology. Voluntary, rights-preserving guidance organized around GOVERN, MAP, MEASURE and MANAGE. NIST was revising AI RMF 1.0 when checked on 28 September 2026, so organizations should verify the current version before formal adoption.

  3. ISO/IEC 42001 explained: What it is, why it matters, and how it works

    International Organization for Standardization. Official overview of the AI management-system standard and its continual-improvement approach. Certification scope and a management system do not by themselves prove that a specific AI use is safe, fair or legally compliant.

Examples and exercises are illustrative unless attributed to a source. No independent expert review is claimed.

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