Free field guide for AI SaaS founders
AI Vendor Security Questionnaire: 25 Questions Enterprise Buyers Ask
Prepare clear, supportable answers before a security review lands in your inbox. This guide shows what buyers need to understand—and what a credible answer should contain.
Before you start
A buyer is evaluating the system you actually operate—not the company you hope to become.
The strongest response is specific, bounded, and traceable to evidence. Avoid absolute claims such as “fully secure,” avoid copying a provider’s controls as if they were yours, and never imply an audit or certification you have not earned.
System scope and ownership
Give the buyer a stable boundary before describing controls.
- 1
What does the AI system do, and what is explicitly outside its intended use?
A credible answer includesThe supported use cases, prohibited uses, primary users, and decisions the system must not make.
- 2
Who owns the product, security, privacy, and model-risk decisions?
A credible answer includesNamed roles, decision rights, and an escalation path—not just “the team.”
- 3
Which parts of the service use AI?
A credible answer includesA plain-language inventory of AI features, including optional features and background processing.
- 4
What level of autonomy does the system have?
A credible answer includesWhether it recommends, drafts, decides, or acts—and where people approve or can reverse an action.
Customer data lifecycle
Trace the data rather than relying on broad privacy language.
- 5
What customer data can enter the AI workflow?
A credible answer includesSpecific data classes, input sources, file types, metadata, and any data the product blocks.
- 6
Where does that data travel?
A credible answer includesYour application, hosting regions, model providers, subprocessors, queues, logs, and support systems.
- 7
Is customer data used to train or improve any model?
A credible answer includesSeparate your own practices from each provider’s settings, contract terms, and optional feedback paths.
- 8
How long are prompts, outputs, files, and logs retained?
A credible answer includesRetention by data type and system, plus what deletion removes and any backup or legal-hold exceptions.
- 9
How are tenants and customer workspaces separated?
A credible answer includesThe actual application and storage boundaries, authorization checks, and relevant testing evidence.
Models and providers
A buyer needs to know which outside systems inherit its data and risk.
- 10
Which models and model providers are in production?
A credible answer includesProvider, model family or version strategy, purpose, hosting pattern, owner, and customer-facing feature.
- 11
How do you evaluate a provider before use?
A credible answer includesSecurity and privacy review, data terms, availability needs, known limitations, and approval record.
- 12
How are model or provider changes reviewed and communicated?
A credible answer includesChange triggers, regression checks, rollback plan, material-change criteria, and customer notice process.
Access, tools, and agent actions
AI risk rises when a model can retrieve private context or take action.
- 13
Who can access prompts, outputs, logs, and configuration?
A credible answer includesRoles, least-privilege rules, privileged access, support access, review frequency, and access logging.
- 14
What internal or customer data can the AI retrieve?
A credible answer includesConnected sources, permission inheritance, retrieval boundaries, filtering, and how stale access is removed.
- 15
What tools or external systems can the AI call?
A credible answer includesEach tool, the allowed operations, credential scope, rate or spend limits, and any prohibited actions.
- 16
What prevents an unsafe or unauthorized action?
A credible answer includesPermission checks, allowlists, input and output validation, confirmation gates, sandboxing, and kill switches.
Testing and human oversight
Describe how you find failures and what happens when the model is uncertain.
- 17
How do you test the AI system before release?
A credible answer includesRepresentative scenarios, failure cases, security tests, acceptance thresholds, reviewers, and stored results.
- 18
How do you address prompt injection and untrusted content?
A credible answer includesThreat model, content boundaries, tool restrictions, output handling, adversarial tests, and known residual risk.
- 19
How do you measure output quality and harmful failure modes?
A credible answer includesTask-specific metrics, test sets, monitoring signals, review cadence, and the limits of those measurements.
- 20
When must a person review or override the system?
A credible answer includesRisk-based review points, reviewer qualifications, user controls, appeal or correction paths, and audit trail.
Monitoring and incidents
Show how the team detects change, contains harm, and learns after release.
- 21
What do you monitor in production?
A credible answer includesSecurity events, abnormal use, quality drift, tool failures, latency or availability, and customer reports.
- 22
How would you respond to an AI-related security or privacy incident?
A credible answer includesTriage owner, containment options, evidence preservation, notification process, recovery, and post-incident review.
- 23
How can a customer report an AI or security concern?
A credible answer includesA monitored channel, expected response path, severity handling, and any status communication available.
Evidence and buyer commitments
A careful answer separates what is true from what can be proved.
- 24
Which documents or records support these answers?
A credible answer includesPolicies, diagrams, configurations, test results, contracts, training records, logs, and dated review records.
- 25
Which claims are implemented, planned, unknown, or not applicable?
A credible answer includesAn honest status, evidence reference, owner, target date when applicable, and buyer-safe explanation of the gap.
An answer that earns the next question
Replace confidence language with evidence language.
“We follow industry best practices and take customer data security very seriously.”
“Production access is limited to the on-call engineering role through SSO and MFA. Access is logged and reviewed quarterly. Evidence: IAM role export dated July 24 and Q3 access-review record. Just-in-time access is planned; owner: CTO; target: September.”
Use frameworks as context, not decoration
This guide is practical preparation—not a substitute for a buyer’s requirements.
The question groups reflect recurring risk-management themes: governance, system context, measurement, monitoring, and response. For authoritative background, review the voluntary NIST AI Risk Management Framework and its Playbook.
For application-security risks specific to generative AI, use the OWASP Top 10 for LLM Applications as a starting point for threat modeling and testing. Start AI Cincy is not affiliated with or endorsed by NIST or OWASP.
From questions to reusable answers
Build the answer base once. Keep it honest. Reuse it.
AI Trust Pack is a local-first workspace for turning your facts into a buyer security brief, model register, data-flow disclosure, reusable answer bank, and evidence-gap plan.
Frequently asked questions
Know what this guide can—and cannot—do.
Is this an official security questionnaire or certification checklist?
No. It is a practical preparation guide for small AI vendors. A buyer may ask different questions based on its industry, data, risk tolerance, and contract.
Do we need SOC 2 before answering a buyer?
Not to document what is true today. If a buyer requires a specific audit or certification, a self-authored answer cannot replace it. State the current status and avoid implying assurance you do not have.
What if we do not have evidence for an answer yet?
Mark the claim as unsupported or planned, assign an owner, and record the next evidence-producing action. A clear gap is safer than an invented control.
Can we paste these answers into every customer questionnaire?
Use them as a reviewed answer base, then adapt each response to the exact wording and scope of the buyer’s question. Recheck facts whenever architecture, providers, or policies change.
What is the fastest way to turn our notes into buyer-ready documents?
AI Trust Pack organizes the same facts into a buyer security brief, model register, data-flow disclosure, reusable answer bank, and evidence-gap plan.