Using an external AI service does not remove the need to understand how the service fits into the organization’s use case. A vendor review record gives teams a durable place to capture what they learned, what remains unknown, and what conditions apply to use.
Start with the actual use case
Record which product or service is being considered, the intended purpose, users, data exchanged, outputs, integrations, automated actions, and internal owner. Vendor-wide claims are less useful than evidence tied to the planned use.
Capture relevant provider information
Document available information about data handling, retention, model or service changes, security features, administrative controls, logging, subprocessors where relevant, incident communication, and support boundaries. Record the source and date reviewed.
Separate evidence from assumptions
Mark unanswered questions, contractual dependencies, technical limitations, and features that were not independently verified. Do not turn marketing language into an internal assurance claim.
Define change and exit triggers
Record what provider changes require reassessment and how the organization would restrict or exit the service if it no longer meets operational needs.
Keep procurement and regulated decisions with qualified owners
This record can support review but does not decide contract terms, data-protection compliance, legal permissibility, clinical suitability, financial appropriateness, or other regulated requirements.
Related knowledge
Use this with AI Use-Case Inventories, AI Data Lineage Records, AI Model Change Reviews, and AI Governance Evidence Packs.
References
This resource supports operational governance. Procurement, legal, regulatory, privacy, clinical, financial, employment, safety, accessibility, and other professional determinations require qualified review.