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Security Reviews

How to Answer AI Security Questionnaires (With Templates)

By Ryan V. (Founder) May 28, 2026 9 min read
Disclaimer:The template responses provided below are for general informational purposes only. All answers must be carefully tailored to represent your company's actual software architecture, hosting setups, data processing agreements, and security guardrails. Have your responses reviewed by qualified legal or security counsel before submitting them to customers.

TL;DR — Key Takeaways

  • Evolving Review Checklist: Enterprise buyers are no longer satisfied with general SOC 2 reports. They now send specialized AI annexes asking about model training and data isolation.
  • Do Not Hallucinate: Hallucinating compliance answers is a high-risk security hazard. Ground every answer in approved policies or active subprocessor agreements.
  • Template Answers: We provide 5 reusable baseline templates covering training data policies, subprocessor isolation, customer opt-outs, and human-in-the-loop oversight.
  • Wedge Feature: Use Govarna's free interactive tool to extract and draft answers in seconds without manual copy-paste overhead.

1. Why AI Security Questionnaires are Spiking

In the last 18 months, B2B SaaS sales teams have faced a new blocker in procurement: the AI Security Questionnaire (often delivered as an addendum to the standard SIG-Lite, CAIQ, or VSA questionnaires).

Buyers want to make sure your product isn't leaking their proprietary corporate data or violating copyright regulations. Because traditional SOC 2 trust reports were designed before the LLM boom, they do not include specific criteria for AI model hosting, hallucination liability, or input retention. As a result, enterprise compliance officers now verify these criteria manually.

2. The Anatomy of an AI Questionnaire

While questions vary, most revolve around four core concern areas:

  • Data Governance & Training: Are customer inputs used to train foundation models? How is customer data isolated from other tenants?
  • Model Selection & Hosting: Do you host models on your own servers or send data to third-party APIs (like OpenAI or Anthropic)? Are zero-data-retention APIs configured?
  • Human-in-the-Loop Oversight: Are AI outputs audited by human reviewers before being shown to users, or do they trigger actions automatically?
  • Risk Vetting: Do you have a formal AI Acceptable Use Policy for employees and an active inventory of AI subsystems?

3. Reusable Templates for Key Questions

Below are 5 common questions with secure, realistic template answers that B2B SaaS teams can adopt.

Question 1: Do you train AI models on customer data?

No. [Company Name] does not use customer production data, inputs, or queries to train proprietary, open-source, or third-party AI models. Any integrations with AI providers utilize API channels that explicitly prohibit the use of customer payloads for model training under applicable developer terms.

Why this works: It gives a clear, direct binary answer first, then outlines the architectural mechanism that guarantees it.

Question 2: What AI subprocessors or API vendors are integrated into your product?

We integrate with [e.g., OpenAI API / Anthropic API] to provide specific features. All API calls are executed using developer accounts under enterprise-tier contracts. Data processed through these APIs is subject to a 30-day zero-retention or transient caching policy for abuse monitoring, and is never written to persistent store.

Why this works: It names the vendor transparently while reassuring the buyer that your enterprise contract terms override public consumer terms.

Question 3: How do you prevent and audit hallucinations or incorrect AI outputs?

We apply structural prompt engineering constraints, Retrieval-Augmented Generation (RAG) restricted to vetted evidence databases, and low temperature settings to minimize hallucinations. Where AI output supports critical workflows, the interface includes a required human-in-the-loop review step before execution.

Why this works: It demonstrates engineering rigor (RAG, low temperature) and outlines user interface safeguards (human review).

Question 4: Can customers opt out of your AI features?

Yes. Customers can request complete deactivation of AI-powered features for their organization. When deactivated, all associated UI modules are hidden, and no organization data is sent to AI subprocessors.

Why this works: Opt-out capabilities are a frequent requirement for legal departments in financial or healthcare sectors.

Question 5: How are customer data inputs isolated when sent to AI models?

Data payloads sent to our AI sub-processors are isolated in transit using TLS 1.3 encryption. Payloads contain only the context required for the specific query, anonymized where possible, and tagged with tenant-specific IDs to prevent cross-tenant exposure.

Why this works: It addresses concerns about data leakage between tenants in shared cloud environments.

4. Best Practices to Accelerate Security Reviews

  • Create an AI Registry: Do not guess what AI libraries your codebase contains. Maintain a central registry of model versions, hosting settings, and security parameters.
  • Establish an Answer Bank: Re-using approved compliance wording ensures consistency and prevents sales representatives from making incorrect claims.
  • Publish an AI Trust Page: Proactively display your AI data policies on your website (e.g., your trust center). This often preempts and eliminates the need for a manual questionnaire.

Want to automate these questionnaires?

Use our free questionnaire responder to draft answers from standard compliance baselines. Copy, paste, and pass audits in minutes.

Go to Free Tool