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Answering an AI security questionnaire with product evidence

Build a response package where every answer traces to a source. Don’t infer a control from a product feature or copy a licensed framework into a sales document.

AccountMade Research · Updated · 4 sources

Build an evidence ledger first

For each question, create a row for the customer’s wording, the product boundary, approved response, source location, check date, owner and remaining qualification. Answer only the scope asked.

A statement about a hosted application doesn’t answer a question about a model provider, a customer-managed deployment or a subprocessor.

The Cloud Security Alliance publishes AI control and questionnaire resources. Use the current licensed material as the framework reference, then write your own response worklist rather than reproducing the questionnaire.

Sources: Cloud Security Alliance AI Controls Matrix and AI-CAIQ · NIST AI Risk Management Framework

Response pattern

A response is complete when a reviewer can locate the evidence and understand its limits.
Question areaResponse structureEvidence needed
Data useState data type, purpose, retention, and exclusionsData-flow and retention source
AccessState identity, authorization, and review boundaryAccess-control source
Model behaviorState intended use and documented limitsModel / product documentation
Incident handlingState the supported process and ownerCurrent policy or runbook
SubprocessorsState the applicable list or open gapCurrent legal / trust source

Sources: Cloud Security Alliance AI Controls Matrix and AI-CAIQ · NIST AI Risk Management Framework

Illustrative answer

Illustrative example. Names, volumes, dates and outcomes are fictional; replace them with approved evidence before use.

Question: ‘How do you control access to customer files used for AI-assisted retrieval?’ Response: ‘The pilot design permits retrieval only for authenticated users assigned to the approved workspace role. The submitted architecture must identify the file store, retention rule, and audit event. This response does not cover a customer-managed model endpoint until that deployment has its own documented boundary.’ The response is cautious because architecture and product policy, not a generic AI claim, determine the answer.

Sources: Supabase: securing data · OpenAI vector stores API reference

Answers that fail review

  • Do not answer ‘yes’ because a vendor advertises encryption or AI safety.
  • Do not promise certification, legal compliance, or a control that has not been verified for the relevant scope.
  • Do not turn an unanswered question into a sales objection response.
  • Route material gaps to the engineering, security, legal, or privacy owner.

Sources: Cloud Security Alliance AI Controls Matrix and AI-CAIQ · NIST AI Risk Management Framework

Sources and dates

  1. Cloud Security Alliance AI Controls Matrix and AI-CAIQ ↗

    Link to the current licensed edition. Confirm the edition before use.

  2. NIST AI Risk Management Framework ↗

    Primary framework reference for governance and risk discussions.

    Reviewed 2026-09-08
  3. Supabase: securing data ↗

    Official guidance on RLS and server-side access patterns.

    Reviewed 2026-09-08
  4. OpenAI vector stores API reference ↗

    Official reference for file-backed vector stores and search.

    Reviewed 2026-09-08

Found an error or a changed source? Send a correction.

Related resources

TemplateVendor security questionnaire: evidence and response template →TemplateRAG architecture diagram for buyer evaluation →

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