If you are searching for an AI vendor assessment questionnaire, you probably do not need another high level AI policy article. You need a practical file that helps you decide whether an AI vendor can be approved, approved with conditions, or sent back for more evidence.
This LearnTPRM Excel workbook is built for that exact moment. It gives TPRM analysts a working AI vendor questionnaire with 132 assessment questions across 13 domains, a 10 question triage, vendor profile intake, an evidence tracker, a risk dashboard, and a framework glossary.
AI Vendor Assessment Questionnaire LearnTPRM
The goal is simple. Ask better questions before approval, collect evidence that matters, and give the business a clear view of AI vendor risk.
Why AI Vendor Assessment Is Different
AI risk is not only security risk
A normal vendor security questionnaire may ask about access control, encryption, incident response, and audit reports. Those still matter. AI vendors add more questions around model behavior, training data, output quality, bias, explainability, prompt security, human oversight, intellectual property, and model change notice.
The same vendor can be low risk or high risk by use case
An AI tool that summarizes public web pages has a different risk profile from an AI tool that reviews employee performance, answers customers, handles patient data, changes financial records, or runs agentic actions. That is why the workbook starts with triage before the detailed questionnaire.
Answers must be tied to evidence
For AI vendors, a confident written answer is not enough. Analysts need policy evidence, model inventory details, testing summaries, data processing terms, security reports, incident playbooks, audit records, and contract clauses. The workbook asks what good evidence looks like for each question.
What Is Inside The Excel Download
Start here sheet
The first sheet explains how to use the file in a real review. It gives the analyst a simple path from intake to triage, questionnaire completion, evidence tracking, risk scoring, and final approval notes.
Vendor profile sheet
This captures the vendor name, product, AI use case, data involved, users, business owner, locations, and assessment context. This matters because AI risk cannot be assessed without knowing the actual use case.
Triage and tiering sheet
The workbook includes 10 triage questions to decide review depth. It checks whether the vendor processes personal data, influences decisions about people, acts autonomously, falls into high risk regulatory categories, handles confidential data, supports a critical process, relies on foundation models, exposes customer facing outputs, uses embeddings or fine tuning, or creates legal and intellectual property risk.
Questionnaire sheet
The main questionnaire includes 132 questions. Each row includes the question, analyst guidance, evidence to request, framework mapping, criticality, weight, vendor response, analyst assessment, risk score, comments, red flag flagging, owner, and status.
Risk dashboard sheet
The dashboard calculates domain level risk as the analyst completes the assessment. It helps show which areas are strong, which areas need evidence, and where red flags are concentrated.
Evidence tracker sheet
The evidence tracker helps analysts request and track key artifacts such as SOC 2 reports, data processing terms, model inventory, red team summaries, sub processor lists, AI incident playbooks, bias testing summaries, and exit evidence.
Frameworks and glossary sheet
The glossary explains key frameworks and terms used in the workbook, including NIST AI RMF, ISO/IEC 42001:2023, EU AI Act Regulation 2024/1689, OWASP Top 10 for Large Language Model Applications, MITRE ATLAS, GDPR, India DPDP, SOC 2, and ISO 27001.
The 13 AI Vendor Assessment Domains
Governance and accountability
Check whether the vendor has AI governance ownership, policy approval, leadership oversight, training, audit, and lifecycle accountability. A vendor should be able to explain who owns AI risk and how decisions are escalated.
Use case and model inventory
Ask what models support the product, what each model does, whether models are built or sourced, and where foundation models or APIs are used. A vague answer here usually means the review needs more depth.
Data governance and training data
Review what data is collected, whether customer data is used for training, how prompts and outputs are retained, how embeddings are protected, and how data deletion works.
Privacy and regulatory compliance
Assess lawful basis, data processing terms, cross border transfers, impact assessments, individual rights, and privacy notice support. AI vendors often create privacy risk through hidden logs and model improvement pipelines.
Model validation and bias
Ask how the vendor tests accuracy, fairness, drift, robustness, and harmful output. For consequential use cases, a general claim of accuracy is not enough.
AI security and adversarial risk
Review prompt injection, insecure output handling, sensitive information disclosure, model denial of service, excessive agency, vector database controls, and red team testing.
Infrastructure and traditional security
Baseline controls still matter. Review identity, logging, encryption, vulnerability management, secure development, incident response, cloud controls, and audit reports.
Fourth party and AI supply chain
AI vendors often depend on foundation model providers, cloud platforms, GPU providers, labeling vendors, data brokers, and plug in ecosystems. The assessment should identify those dependencies clearly.
Transparency and explainability
Ask whether users know they are interacting with AI, whether outputs can be explained, and whether the vendor provides model cards, system documentation, or limitations in plain language.
Output integrity and human oversight
Review how outputs are checked before action, who can override AI results, how errors are reported, and whether the system can be paused or rolled back when behavior changes.
Incident response and monitoring
AI incidents may involve data leakage, model abuse, prompt attacks, harmful output, bias, integrity failures, or unauthorized model changes. The vendor should have a playbook that covers AI specific scenarios.
Continuity, exit, and portability
Ask how the customer exits, how data is returned, how embeddings and fine tunes are deleted, how integrations are removed, and what happens if the model provider changes or shuts down.
Legal, contractual, and intellectual property
Review confidentiality, training restrictions, indemnity, generated output ownership, audit rights, incident notice, sub processor notice, data return, deletion, and change notice.
How To Use The Questionnaire In A Real Review
Step 1 complete vendor profile
Start by writing down the actual AI use case. The same tool can carry different risk depending on whether it summarizes notes, touches customer data, writes code, controls workflow, or supports decisions about people.
Step 2 run the triage
Use the 10 triage questions to decide whether the review should be light, standard, or deep. This avoids wasting time on low exposure tools while still protecting the business from high impact AI risk.
Step 3 send only the right sections
For lower risk use cases, you may not need all 132 questions. For high risk or sensitive use cases, use the full questionnaire and require evidence for critical controls.
Step 4 review evidence before scoring
Do not score only from the vendor response. Compare the answer against policies, reports, diagrams, test summaries, contract language, and logs. If the answer is good but evidence is missing, mark it as a gap.
Step 5 use the dashboard for the approval story
The dashboard helps convert many answers into a simple discussion. Which domains are weak. Which gaps are material. Which conditions must be closed before go live. Which risks can be accepted by the business.
Step 6 save the evidence trail
Use the tracker to show what was requested, what was received, who reviewed it, and what conclusion was reached. This is what makes the assessment audit ready later.
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Practical Checklist
- Confirm the exact AI use case and business owner
- Classify data types, user groups, and decision impact
- Ask whether customer data trains, tunes, or improves models
- Request model inventory and foundation model dependencies
- Review prompt security, output handling, and human oversight
- Check bias testing, drift monitoring, and validation evidence
- Confirm incident notice for AI specific events
- Review sub processors and fourth party model providers
- Check exit terms for prompts, logs, embeddings, and fine tunes
- Record approval conditions and residual risk in plain language
Analyst Takeaway
The best AI vendor assessment questionnaire is not the longest one. It is the one that helps the analyst understand the use case, ask evidence based questions, separate claims from proof, and explain residual risk before the business signs or goes live.
AI Vendor Assessment Questionnaire LearnTPRM
FAQ
What is an AI vendor assessment questionnaire
An AI vendor assessment questionnaire is a structured set of questions used to review how an AI vendor governs models, handles data, secures systems, validates outputs, manages incidents, and supports exit.
How many questions should an AI vendor assessment include
The right number depends on the use case. This workbook includes a 10 question triage and 132 detailed questions so analysts can scale review depth by risk instead of asking every vendor the same thing.
What should I ask an AI vendor before approval
Ask about the AI use case, data inputs, training use, model inventory, human oversight, bias testing, prompt security, sub processors, incident response, audit evidence, contract terms, and data deletion.
Can I use this AI vendor questionnaire in Excel
Yes. The blog includes a direct Excel download link. Analysts can use it for intake, due diligence, evidence review, risk scoring, and approval preparation.
