Assessing AI Vendor Risks with Questionnaires

AI vendors are everywhere now. They promise faster workflows, smarter insights, and better customer experiences. But they also introduce risks most teams aren't prepared to handle.

Training data sources can expose you to compliance violations. Models can leak or memorize sensitive information. Third-party AI dependencies can fail without warning. According to IBM's 2025 Cost of a Data Breach Report, 13% of organizations reported breaches involving AI models or applications, and 97% of those organizations lacked proper AI access controls.

The numbers get worse. A Secureframe report says supply chain attacks accounted for nearly half (47%) of total affected individuals in the first half of 2025, with third-party vendor and supply chain compromise costing an average of $4.91 million. When your AI vendor gets breached, you inherit that risk.

This guide shows you how to structure AI vendor questionnaires, what questions actually matter, and how to validate responses before signing contracts. You'll learn how to turn generic vendor due diligence into a rigorous AI risk assessment that protects your organization.

Why AI Vendor Risk Is Different From Traditional Vendor Risk

Traditional vendor risk focuses on financial stability, operational uptime, and basic security controls. AI vendors add new layers of complexity.

AI models don't behave predictably. A chatbot that works perfectly in testing can generate biased or harmful outputs in production. A report by Tech Advisors says that 78% of people open AI-generated phishing emails, and 21% click on malicious content inside. Your AI vendor's model could be the source of those attacks.

Training data creates compliance exposure you can't see. If your vendor trained their model on scraped web data, you might be processing personal information without consent. If they used customer data from other clients to improve their models, you're now part of a data sharing arrangement you never agreed to.

Gartner predicts that by 2027, more than 40% of AI-related data breaches will be caused by the improper use of generative AI across borders. AI tools process data in ways that cross jurisdictional boundaries, often without clear documentation or announcement.

Most organizations aren't ready for this. IBM states that 63% of breached organizations either don't have an AI governance policy or are still developing one. CISOs, compliance teams, and TPRM professionals need a structured way to assess these risks before they become incidents.

What Is an AI Vendor Questionnaire?

An AI vendor questionnaire is a structured assessment tool that gathers evidence about how a vendor's AI system works, what data it processes, and what controls exist to prevent misuse.

It differs from a standard vendor due diligence questionnaire in three ways:

• First, it focuses on model-specific risks like training data provenance, output reliability, and algorithmic bias. 
• Second, it requires technical documentation that most vendors don't provide by default. 
• Third, it connects AI capabilities to compliance obligations that may not be obvious from the vendor's marketing materials.

The questionnaire serves as your foundational assessment step. It tells you what level of risk you're accepting and what additional validation you need before integration.

Think of it as translating vendor promises into verifiable claims. When a vendor says their AI is "secure," the questionnaire forces them to explain what that means: encryption at rest and in transit, access controls, audit logging, penetration testing results, and incident response procedures.

AI Vendor Risk Questionnaire Questions

Below are practical questions organized by risk category. Tailor these based on your vendor's tier, the sensitivity of data they'll access, and the criticality of their service.

AI Usage and Scope

1. Does your organization deploy AI or machine learning systems in any capacity related to the services you'll provide to us?

• If yes, specify which systems use AI (e.g., customer support chatbots, fraud detection, data analytics, content generation)
• Indicate whether AI is customer-facing or used internally only

2. Are you planning to introduce AI capabilities in the next 12 months that could affect our engagement?

• Describe planned AI implementations and expected timelines
• Explain how these changes will be communicated to customers

3. Which type of AI models do you use? (Select all that apply)

• Generative AI (LLMs, image generation, etc.)
• Predictive analytics
• Natural language processing
• Computer vision
• Recommendation systems
• Other (please specify)

Data Handling and Privacy

4. Will AI systems process, store, or have access to our proprietary data or customer information?

• Specify data types the AI will access (PII, financial, health, intellectual property)
• Indicate whether data will be used for model training or improvement

5. Do you share data with third-party AI providers (e.g., OpenAI, Anthropic, Google)?

• Name all third-party AI services integrated into your platform
• Describe what data is shared and under what terms

Model Governance and Explainability

6. Can you provide documentation on how your AI models make decisions?

• Share model cards, algorithm descriptions, or technical specifications
• Explain key features and data sources used in model training

7. How do you test AI models for bias, fairness, and accuracy before deployment?

• Describe pre-deployment testing procedures
• Share metrics used to evaluate model performance (false positive rates, fairness scores, etc.)

Security and Access Controls

8. What security measures protect your AI infrastructure from unauthorized access?

• Detail access controls (role-based, multi-factor authentication)
• Describe network segmentation and encryption methods

9. How do you prevent prompt injection, data poisoning, or adversarial attacks on your AI systems?

• Explain input validation and sanitization processes
• Describe monitoring for malicious prompts or anomalous behavior

Compliance and Regulatory

10. Which AI-specific regulations and frameworks do you comply with?

• Check all that apply: EU AI Act, NIST AI RMF, ISO/IEC 42001, sector-specific rules
• Provide evidence of compliance (certifications, audit reports, attestations)

11. Do you maintain records of AI system decisions for audit and regulatory purposes?

• Specify retention periods for AI decision logs
• Confirm ability to produce audit trails on request

Operational Resilience

12. What is your AI system's uptime and performance SLA?

• Provide historical uptime metrics
• Describe failover and redundancy measures

13. What happens if your AI system fails or produces incorrect outputs?

• Explain manual fallback procedures
• Describe liability and remediation processes

Third-Party Dependencies

14. Do you rely on external AI vendors or cloud providers for core AI functionality?

• List all critical third-party AI dependencies
• Explain contingency plans if a provider discontinues service

15. Have your third-party AI providers undergone security and compliance assessments?

• Share SOC 2, ISO 27001, or equivalent reports for AI vendors
• Confirm subprocessor agreements are in place

Key Areas Your AI Vendor Questionnaire Must Cover

Data handling and privacy practices

Start with the basics: where does the training data come from, how long is it retained, and who has access to it.

Ask vendors to document their data segregation practices. If they're processing data from multiple customers, how do they prevent one customer's data from influencing another customer's model outputs? This matters for both privacy and competitive reasons.

Anonymization isn't enough. The IBM report states that one in five organizations reported a breach due to shadow AI, often because employees used AI tools that didn't properly anonymize data before processing it. Your AI vendor questionnaire should verify that anonymization is tested and validated, not just claimed.

Request documentation of data lineage. You need to know where training data originated, how it was collected, and whether the vendor has legal rights to use it. This protects you from IP violations and compliance failures.

Model transparency and governance

AI models fail in ways software doesn't. They can produce outputs that seem correct but are completely wrong. They can memorize training data and leak it in responses. They can exhibit bias that wasn't in the original training set.

Your questionnaire should ask for documentation of model behavior, risks, and limitations. A vendor that can't explain when their model is likely to fail isn't ready for production use.

Auditability matters for compliance. If a regulator asks you to explain an AI decision that affected a customer, you need to be able to trace that decision back to specific model inputs and logic. Ask vendors how they support model audits and what explainability tools they provide.

Security controls and access management

Standard security questionnaire for vendors covers the basics, but AI tools need additional scrutiny.

API access limits prevent abuse. If your vendor's API has no rate limiting, an attacker can use it to extract training data through repeated queries. Ask about throttling, IP allowlisting, and authentication mechanisms.

Encryption protects data in transit and at rest, but AI systems also process data in memory. Ask whether the vendor uses confidential computing or other techniques to protect data during inference.

External testing proves security controls work. According to Tech Advisors, 82.6% of phishing emails now use AI technology in some form. If your vendor hasn't tested their system against AI-powered attacks, they're not ready.

Regulatory and compliance alignment

Compliance requirements vary by use case. A vendor handling healthcare data needs HIPAA alignment. One processing European data needs GDPR compliance. Financial services need SOX controls.

Your questionnaire should map vendor capabilities to your specific compliance obligations. Don't assume a vendor knows which regulations apply to your use case.

GDPR requires data minimization and purpose limitation. If your vendor's model processes more data than necessary for its stated purpose, you're out of compliance. Ask how they enforce these principles.

HIPAA requires business associate agreements and specific security controls. Verify that your vendor understands these requirements and has experience meeting them.

Third-party dependencies and subcontractors

AI vendors rarely build everything in-house. They use foundation models from OpenAI, Anthropic, or Google, rely on cloud providers for computing, and integrate with data brokers for enrichment.

Each dependency introduces risk. Ask vendors to document their entire AI supply chain, including which third parties have access to your data and what controls govern that access.

Consider this hypothetical scenario: Your AI vendor uses a foundation model from a major provider. That provider suffers a data breach. Depending on how your vendor implemented the integration, your data might have been exposed. Without documentation of these dependencies, you won't know until it's too late.

Operational reliability and uptime

SLAs matter for AI systems just like any other service. But AI introduces new failure modes.

Models can degrade over time as data distributions change. Ask vendors how they monitor model performance and when they retrain or update models.

Failover and fail-safe mechanisms prevent total outages. If your vendor's AI service goes down, what happens to your workflows? Do they have backup systems? Can you fall back to manual processes?

SLAs should cover accuracy as well as uptime. A model that's available but producing garbage outputs is worse than no model at all.

How to Assess Responses From AI Vendor Questionnaires

Collecting responses is only half the work. Validation separates serious vendors from those who haven't thought through the implications of their technology.

Validate completeness and consistency

Missing answers are high-risk indicators. If a vendor can't or won't answer a question about data handling, assume they don't have adequate controls in place.

Vague responses deserve follow-up. "We use industry standard security" tells you nothing. Push for specifics: which standards, which controls, which validation processes.

Review evidence, not statements

Statements of capability mean nothing without proof. Request SOC 2 reports, security documentation, privacy policies, and redacted test results.

Look for third-party validation. Has an external auditor reviewed their controls? Do they have relevant certifications? Can they provide customer references who can speak to their security practices?

In 2024, multiple organizations suffered breaches because their vendors claimed to have strong security but never provided evidence. AT&T suffered multiple breaches that exposed customer contact details due to vulnerabilities in third-party vendor systems. The vendor risk assessment template they used didn't require proof of controls.

Compare responses to a baseline vendor risk assessment template

Create a scoring framework that weights responses based on risk impact. A vendor that can't document training data provenance should score lower than one with full data lineage documentation.

Use this vendor risk assessment example: If a vendor handles sensitive financial data, security controls should be weighted heavily. If they only process anonymized analytics data, compliance controls might matter more.

Escalate suspicious patterns

Some responses should trigger immediate escalation to your security team.

Model training on customer data without explicit consent is a red flag. This creates both privacy risks and competitive risks if that data is used to improve services for other customers.

Third-party audits don’t suggest immature security practices. Any vendor processing sensitive data should have regular independent assessments.

No incident response plan means breaches will be handled poorly. Ask for documentation of their incident response procedures and evidence that they've tested them.

Best Practices for Managing Third-Party AI Risk

A questionnaire alone won't protect you. Build these practices into your TPRM program.

• Tier vendors based on AI criticality. Not every AI tool introduces the same level of risk. A chatbot that handles customer service inquiries needs deeper assessment than an internal tool that summarizes documents.
• Require annual reassessment at minimum. AI systems change faster than traditional software. What was safe last year might not be safe today.
• Request model cards or system cards when possible. These documents provide structured information about model capabilities, limitations, training data, and intended use cases. Major AI providers now publish them by default.
• Introduce continuous monitoring for AI anomalies. Track model performance, output quality, and usage patterns. Sudden changes can indicate security issues or model degradation.
• Require documented fallback controls. What happens when the AI fails? Can your business continue operating? Document backup processes before you need them.
• Ensure human-in-the-loop review for critical outcomes. AI should augment decisions, not replace them entirely. Any system that makes decisions affecting customers, employees, or compliance should include human oversight.

Common Red Flags in AI Vendor Questionnaires

Learn to spot warning signs early.

Models trained on unknown or public datasets create legal risk. If your vendor scraped training data from the internet, they might be using copyrighted material or personal information without authorization.

If a vendor cannot explain model behavior, it means they don't understand their own system. This makes debugging impossible and indicates low technical maturity.

No evidence of security testing suggests vulnerabilities haven't been discovered yet, not that they don't exist. Credential phishing attacks increased by 703% in the second half of 2024, largely due to AI-generated phishing kits. Your vendor should be testing against these attacks.

Uses customer data for model improvement without consent violates privacy principles and might violate regulations. Make sure you understand exactly how your data will be used.

No compliance mapping means the vendor hasn't thought through regulatory requirements. They're leaving compliance risk entirely on you.

How ComplyScore® Helps Streamline AI Vendor Assessments

Managing AI vendor risk at scale requires purpose-built tools.

ComplyScore® provides AI vendor-specific questionnaires that cover the technical, security, and compliance areas outlined in this guide. The platform automatically reads uploaded documentation to verify vendor claims, reducing the manual review burden on your team.

Centralized vendor scoring lets you compare AI vendors using consistent criteria. Each vendor gets a risk rating based on their responses, documentation quality, and validation results.

Continuous monitoring extends beyond the initial assessment. ComplyScore® tracks external signals about your AI vendors, including security incidents, compliance violations, and operational issues.

Faster due diligence workflows mean you can assess more vendors without adding headcount. Teams using ComplyScore® report 40-60% reduction in assessment costs while improving coverage across their vendor portfolio.

Get a demo to explore how ComplyScore® can help automate and strengthen AI vendor assessments while reducing the manual burden on your team.

Frequently Asked Questions

What are the biggest risks when working with an AI vendor?

The top risks are data exposure (your information being used to train models for other clients), security vulnerabilities like prompt injection attacks, lack of model explainability, compliance violations with regulations like GDPR or the EU AI Act, and operational failures from model drift.

What should we ask to ensure the vendor protects our data?

Ask three essential questions: "Will our data be used to train your AI models?", "Where is our data processed and stored?", and "What third-party AI services do you use?" Request proof of data isolation, encryption methods, and access controls. Also verify they conduct regular security audits specifically focused on AI systems.

How can we verify the vendor's AI model is trustworthy and free from bias?

Request model cards or documentation explaining training data sources and decision-making logic. Ask for bias testing results across demographic groups and accuracy metrics by customer segment. Confirm they maintain human oversight for high-stakes decisions and keep audit logs you can review. If a vendor can't explain how their model works, consider it a red flag.

What security controls should an AI vendor have?

Beyond standard controls like multi-factor authentication, encryption, and SOC 2 compliance, AI vendors need input validation to prevent prompt injection, output filtering to avoid data leakage, and adversarial testing for model vulnerabilities. They should also maintain model versioning with rollback capabilities and continuous monitoring for anomalous behavior. Ask for evidence of AI-focused penetration testing.

How do we ensure the AI vendor complies with regulations (e.g., GDPR, AI Act, HIPAA)?

Ask which frameworks they follow (EU AI Act, NIST AI RMF, ISO/IEC 42001) and request compliance certifications or third-party audit reports. For GDPR, verify they support data subject rights and have lawful cross-border transfer mechanisms. For HIPAA, confirm they'll sign a BAA and maintain audit logs. Map their controls to your specific regulatory requirements and validate with documentation.