AI Governance Questionnaire Guide for SaaS & AI Vendors
Enterprise buyers increasingly ask not only whether AI is secure, but who owns AI risk, how systems are inventoried, how changes are approved, how models are evaluated and where humans intervene.
Core AI governance evidence areas
Ownership
Named accountable owner, escalation path and approval responsibilities.
Inventory
List of AI-enabled systems, features, models and external providers.
Risk classification
Process for categorizing AI use cases by risk and required review.
Evaluation
Documented quality, safety, reliability or bias checks where relevant.
Change management
Review process for significant model, provider or product changes.
Human oversight
Where a person can review, override, approve or investigate outputs.
What evidence makes an AI governance answer credible?
Buyers increasingly want to know not only what policy exists, but how it operates. A written AI policy is stronger when it is paired with an inventory of AI systems, named owners, documented approval gates, evaluation records, change-review procedures and an escalation path for incidents or unexpected outputs. The evidence should match the product and deployment the buyer is evaluating rather than describing an unrelated corporate program.
Governance evidence also needs to stay current. Model providers, routing logic and AI-enabled features can change faster than annual policy cycles. A review process should therefore connect product changes back to the governance records used in buyer questionnaires.
Questions your team should be ready to answer
- Who approves production use of new AI models or providers?
- How are AI systems and dependencies inventoried?
- What evidence supports your customer-data training position?
- How are model changes tested before release?
- Where does human review occur?
- How are harmful or unexpected outputs investigated?
- What AI incidents trigger customer notification or escalation?
Use the 36-question vendor template for a broader preparation set.