--- title: "AI Vendor Questionnaire: How to Prepare Answers and Evidence" url: "https://www.arphie.ai/blog/ai-vendor-questionnaire" collection: blog lastUpdated: 2026-08-08T18:05:20.499Z --- # AI Vendor Questionnaire: How to Prepare Answers and Evidence An AI vendor questionnaire can decide whether an enterprise opportunity advances to contract or stalls in security and legal review. Buyers use it to examine how your AI works, what happens to their data, how you manage model risk, and whether your claims have proof. For an AI vendor, the job is to produce a clear, consistent response without pulling the same experts into every deal. ## What Is an AI Vendor Questionnaire? An AI vendor questionnaire is a structured due diligence document that a buyer sends to an AI provider before a purchase, renewal, or expanded use. It asks about the AI system, customer-data flows, model dependencies, security, testing, governance, legal terms, and operations. It often supplements a general security due diligence questionnaire (DDQ). We built [Arphie for security questionnaire response](https://www.arphie.ai/security-teams) on the vendor side of this process. With Arphie, you can import the buyer's questionnaire, draft answers from connected company sources, review the source and confidence level behind each draft, and route questions to security, legal, product, and engineering reviewers. Your experts can focus on exceptions and final sign-off instead of finding and rewriting recurring facts. Questionnaire depth depends on what the buyer plans to do with the product. An internal summarization tool that receives no sensitive data faces a different review from an agent that can take actions in a regulated workflow. The Financial Services Information Sharing and Analysis Center's [FS-ISAC assessment model](https://www.fsisac.com/hubfs/Knowledge/AI/FSISAC_GenerativeAI-VendorEvaluation%26QualitativeRiskAssessment.pdf) tiers due diligence using your organization's use case, business integration, use of confidential data, business resiliency, and potential for exposure. That variation makes a static bank of generic answers fragile. A reusable response needs a stable core claim plus clear scope, current evidence, and buyer-specific qualifications. ## The Question Categories Your Response Must Cover Buyers are trying to establish what your system does, how you control the associated risk, and what proves the control works. Pair each common question with evidence and an accountable owner. | Question category | What buyers commonly ask | Proof buyers expect | Primary answer owner | | --- | --- | --- | --- | | Product scope and intended use | Which features use AI? Is AI optional? What models, deployments, and uses are in scope? | AI feature inventory, system or model card, architecture, data flow, acceptable-use guidance. | Product or AI governance, with sales engineering for deal context. | | Customer data and privacy | What inputs and outputs are collected? Where are they processed? How long are they kept? Are they used for training or improvement? | Data-flow map, retention schedule, privacy impact assessment, Data Processing Addendum, deletion procedure. | Privacy, legal, and security engineering. | | Training data and intellectual property | Where did development and fine-tuning data come from? What rights govern its use? How are opt-outs, removal requests, and output ownership handled? | Data provenance statement, dataset documentation, licensing records, model card, approved contract language. | Legal, data governance, and machine learning engineering. | | AI supply chain | Which foundation models, hosting providers, tools, and subprocessors support the service? How are changes assessed? | Model and subprocessor inventory, risk reviews, dependency diagram, change log, contingency plan. | Product, vendor risk, security, and legal. | | AI security and abuse resistance | How do you address prompt injection, sensitive-data disclosure, insecure output handling, model theft, excessive agency, and access abuse? | AI threat model, secure development records, red-team or adversarial-test summary, penetration-test summary, access and logging design. | Application security, security engineering, and machine learning engineering. | | Performance, safety, and fairness | How is performance measured? What are the limitations? How do you evaluate hallucination, harmful output, or bias? | Evaluation method and results, model or system card, safety assessment, monitoring records. | Machine learning, product, responsible AI, and risk. | | Governance and compliance | Who is accountable for the system? How is risk classified and approved? Which frameworks or legal requirements guide the program? | AI policy, system inventory, risk or impact assessment, control mapping, governance approvals, applicable certifications. | AI governance, compliance, privacy, and legal. | | Human oversight and user controls | What decisions require a person? Can administrators disable AI, restrict tools, review outputs, and report problems? | Permission matrix, configuration guide, review procedure, escalation path, audit-log sample. | Product, security, and customer success. | | Operations, change, and incidents | How do you monitor behavior, approve updates, roll back changes, maintain service, and notify customers? | Change procedure, monitoring report, incident response and continuity plans, service-level terms. | Site reliability engineering, product operations, and security. | | Contract and exit terms | Who owns inputs and outputs? What audit, notice, liability, support, deletion, export, and termination terms apply? | Master agreement, AI addendum, Data Processing Addendum, service-level agreement, support and exit procedures. | Legal, finance, customer success, and product. | Current frameworks reinforce this evidence-first structure. The National Institute of Standards and Technology's [AI RMF 1.0](https://airc.nist.gov/airmf-resources/airmf/5-sec-core/) organizes AI risk work into Govern, Map, Measure, and Manage. For large language model (LLM) applications, the Open Worldwide Application Security Project's [LLM Top 10](https://genai.owasp.org/llm-top-10/) gives reviewers a shared vocabulary for risks such as prompt injection, sensitive information disclosure, supply-chain weaknesses, and excessive agency. The Cloud Security Alliance has also made AI assurance more questionnaire-shaped. Its AI Consensus Assessments Initiative Questionnaire, [AI-CAIQ v1.1](https://cloudsecurityalliance.org/artifacts/ai-consensus-assessments-initiative-questionnaire-ai-caiq-v1-1), includes self-assessment questions and fields to justify answers with evidence. The related [AI Controls Matrix v1.1](https://cloudsecurityalliance.org/artifacts/ai-controls-matrix-v1-1) contains 247 control objectives across 18 domains. Buyers can ask for control-level proof even when their wording differs. ## What a Defensible AI Questionnaire Answer Looks Like A useful answer lets a reviewer reach a decision without guessing. It contains six parts: - **Direct position.** Start with Yes, No, Partially, or Not applicable when the form requests it, then answer the question in one sentence. - **Scope.** Name the product, deployment, data type, environment, or customer configuration covered by the answer. - **Control.** Explain the policy, technical mechanism, or operating process that makes the statement true. - **Evidence.** Point to a specific document, report, diagram, contract section, test result, or system record. - **Boundary.** State relevant exclusions, dependencies, customer responsibilities, and exceptions. - **Authority.** Record the person who approved the customer-facing claim and the event that should trigger another review. For example, consider the question, "Will you use our data to train an AI model?" A weak answer says, "No, we never train on customer data." A stronger response defines which customer content and service are covered, distinguishes production inference from any separately contracted tuning service, describes the controls applied to downstream model providers, and cites the approved data-flow and contract terms. The actual answer will vary by product. The structure remains useful because it makes the scope and proof explicit. Published questionnaires ask for that linkage. The American Civil Liberties Union (ACLU) instructs respondents in its [generative AI questionnaire](https://assets.aclu.org/live/uploads/2025/06/October-2025-ACLU-Vendor-Generative-AI-General-Questionnaire.pdf) to describe the supporting control and cross-reference evidence. It also asks for model documentation, performance analyses, training-data information, leakage testing, red-team analyses, fairness evaluations, and safeguards. ### Explain Every Not-Applicable Answer "Not applicable" is a claim about scope. Pair it with the fact that makes the question irrelevant. If a buyer asks about fine-tuning controls and your service performs inference only, say that the in-scope service does not fine-tune models on customer data. A bare N/A can look like an omission and generate another review cycle. ### Separate Current Facts, Contractual Commitments, and Roadmap Plans These answer types require different approval paths. Current behavior should trace to product and control evidence. A customer-specific promise should use legal-approved language and appear in the governing agreement. A roadmap item should be labeled as planned, with no suggestion that it already exists. Mixing the three is a common source of contradictions between a questionnaire, a contract, and a later renewal review. ### Classify Evidence Before You Share It Use three disclosure levels so a fast response does not create a security or confidentiality problem: - **Public evidence.** Trust pages, public policies, certificates, model cards, and product documentation that can be linked directly. - **Controlled evidence.** Audit reports, penetration-test summaries, architecture diagrams, and detailed policies shared under a nondisclosure agreement or through a controlled portal. - **Restricted evidence.** Raw logs, source code, complete test findings, confidential supplier terms, and sensitive control details that usually require a summary or live review instead of a file attachment. The response can name stronger evidence without attaching the sensitive artifact. State what exists, who assessed it, its relevant scope, and how an authorized buyer can review it. ## AI Vendor Due Diligence Checklist for Your Evidence Pack Build the evidence pack before the questionnaire arrives. Each item should have a named owner, an approved customer-facing version, a disclosure level, and a change trigger. | Evidence-pack item | What it should establish | | --- | --- | | AI system inventory and documentation | Products, features, models, deployments, owners, intended uses, limitations, and architecture. | | Customer-data lifecycle map | Data collected at each step, purpose, location, access, retention, deletion, and training status. | | Training-data and IP statement | Provenance, licenses, curation, opt-out or removal processes, and rights in inputs and outputs. | | Model, tool, and subprocessor inventory | External models, hosting, agents, tools, data recipients, risk reviews, and substitution process. | | AI risk or impact assessment | Intended use, affected parties, risk classification, harms considered, controls, residual risks, and approval. | | AI security threat model | Trust boundaries, abuse cases, prompt and output controls, access, secrets, logging, and response measures. | | Test and evaluation summaries | Security, performance, safety, fairness, privacy, and resilience methods, results, limits, and remediation. | | Governance and assurance record | Owners, policy, review bodies, control mappings, audit reports, certifications, and remediation status. | | Operational procedures | Release approval, model-change review, drift monitoring, rollback, incident handling, continuity, and customer notice. | | Customer terms | Security and privacy terms, AI-specific terms, service levels, data rights, audit rights, support, termination, and deletion. | Treat this checklist as a maintained index. A one-time deal folder quickly becomes stale. With [our integrations](https://www.arphie.ai/integrations), you can connect the workflow to sources such as Vanta, Google Drive, SharePoint, Confluence, and company web pages, so drafts use current approved material where it already lives. ## A Reusable AI Vendor Questionnaire Workflow The operating goal is a closed loop: every questionnaire starts from approved company knowledge, and every reviewed exception improves the next response. ![AI vendor questionnaire response workflow from intake through evidence, drafting, review, and reuse](https://cdn.prod.website-files.com/672fc2345132970736914b73/6a7662d03cb0d0877a2de45d_55e80f9c-9784-4c0e-892e-addef5115102.png) - **Capture the buyer's use case.** Record the AI feature, intended users, decisions supported, data types, integrations, geography, deployment, and planned level of autonomy. This context determines whether an existing answer applies and which evidence the buyer needs. - **Import and normalize the questionnaire.** Preserve the question ID, answer type, attachments, conditional logic, and file format. In Arphie, you can import [Word and Excel questionnaires](https://www.arphie.ai/features), detect questions and sections, and return the response in the original file. - **Retrieve facts from approved sources.** Match each question to live policies, control evidence, product documentation, approved answers, and customer terms. In Arphie, you can review the sources and confidence level behind each first draft, which separates reusable answers from gaps. - **Draft the direct answer, scope, proof, and boundary.** Follow the requested Yes, No, N/A, or narrative format. Keep the claim as narrow as the supporting source. Generic AI prose has no place in a due diligence answer when company evidence is missing. - **Route exceptions to the accountable owner.** Security reviews technical controls, privacy owns data handling, legal owns rights and commitments, product and machine learning teams own system behavior and evaluations, and operations owns resilience. In Arphie, you can assign [writer, reviewer, and owner roles](https://www.arphie.ai/features), collaborate at the question level, track deadlines, and [record sign-off](https://www.arphie.ai/security-teams). - **Run a cross-answer quality review.** Compare all statements about training, retention, deletion, subprocessors, residency, model changes, incident notice, and human oversight. Resolve conflicts, qualify N/A responses, remove unsupported claims, and gate sensitive attachments. A human owner gives final approval. - **Promote reviewed knowledge for reuse.** Save the answer with its intent, scope tags, sources, disclosure level, owner, approval date, and change triggers. Record customer-specific commitments separately. With Arphie, you can [reuse the organizational answer](https://www.arphie.ai/blog/proposal-content-library) while preserving context for review. This workflow also helps with questions that arrive before or after the spreadsheet. Sales engineering and security can use our [Quick-Ask AI](https://www.arphie.ai/features) in Arphie or through [our Slack integration](https://www.arphie.ai/integrations/slack) to retrieve answers from the same approved knowledge base. The response still follows the same rule: source first, accountable review for any new claim or commitment. ## Quality Checks Before Submission Use a final control check that focuses on decision quality rather than wordsmithing: - **Scope matches the deal.** Every answer reflects the buyer's product, data, deployment, and use case. - **Material claims have sources.** A reviewer can open the supporting evidence without hunting through folders or messages. - **Structured and narrative fields agree.** Yes, No, and N/A selections do not conflict with comments or attachments. - **Repeated facts are consistent.** Training, retention, deletion, hosting, subprocessors, and notice periods match throughout the response. - **Exceptions are explicit.** Dependencies, optional configurations, customer duties, and separate services are visible. - **Evidence is shared safely.** Public, controlled, and restricted material follows its approved disclosure path. - **Commitments and sign-off have authority.** Legal and product owners approve promises, and the response owner approves the submission version. ## Make AI Due Diligence a Repeatable Revenue Workflow An AI vendor questionnaire is easier to answer when evidence, owners, and approval boundaries already exist. A mature response operation reuses stable company facts while rechecking product scope, buyer context, sensitive disclosures, and contractual commitments for every deal. Buyers get clearer proof, and your security, legal, engineering, and sales engineering teams get more time for questions that need judgment. Our AI agents turn approved knowledge and live evidence into [source-backed first drafts](https://www.arphie.ai/features), then keep [accountable reviewers in control](https://www.arphie.ai/security-teams) through final sign-off. [Book an Arphie demo](https://www.arphie.ai/contact) to build a faster, defensible questionnaire response workflow.