AI Security Questionnaire Completion Service
Prepare evidence-backed drafts for the AI-specific questions enterprise buyers now add to security and procurement reviews.
AI reviews create new evidence questions
Traditional security questionnaires ask about controls such as access, encryption and incident response. AI-focused reviews add questions about model providers, training use, customer prompts and outputs, retention, evaluation, human oversight, governance, automated decision-making and subprocessors.
Training and data use
Whether customer content is used for training, fine-tuning or shared model improvement, based only on supplied evidence.
Model and provider chain
Which models or external AI providers are used and what responsibilities remain with the vendor.
AI governance
Ownership, risk review, evaluation, change management and human oversight where documented.
Privacy and retention
How AI-related inputs and outputs interact with retention, deletion, access and data-location practices.
Designed to expose unsupported claims
AI questionnaires frequently ask for policies or governance controls that young companies have not formally documented. ProcureDeal does not treat a plausible answer as proof. Unsupported items are intended to remain visible for customer review instead of being converted into confident but unverified claims.
For framework-specific help, see the AI-CAIQ completion guide.
Typical buyer questions in an AI review
Buyers commonly want to know which AI providers are used, whether customer prompts or outputs are used for training, what retention rules apply, how access is controlled, how model changes are evaluated, whether humans review high-impact outputs, and which subprocessors can receive customer information. The correct answer must come from the vendor's actual product and policy evidence.
For recurring AI procurement reviews
If AI questionnaires appear repeatedly across enterprise deals, the Pipeline plan is designed to reuse approved evidence across up to four questionnaires per month while keeping unsupported claims visible for review.