AI Vendor Due Diligence Checklist (Legal + IT Friendly)

Every AI vendor claims their product is secure, fair, and compliant. Your job is to make them prove it. Because here’s what I’ve learned: the vendors who actually have their security and compliance story together will welcome the questions. The ones who get vague or defensive are telling you something important.

Third-party AI tools can accelerate your initiatives significantly - but they also introduce risks that don’t show up in a sales demo. Slick presentations rarely reveal a vendor’s actual security posture, data handling practices, or compliance readiness. To protect your business, you need a repeatable evaluation process that cuts through the marketing and surfaces what actually matters.

Leaders are under real pressure to adopt AI tools quickly, and vendors know it. That pressure creates conditions where organizations adopt unvetted technologies that expose sensitive data, propagate bias, and put them in violation of laws they didn’t realize applied to their use case. A thorough questionnaire moves beyond marketing claims by demanding evidence - not promises, but documentation.

Build your questionnaire around four pillars:

Pillar 1: Data privacy and security. Ask vendors to describe their encryption methods for data in transit and at rest, their data retention and deletion policies, and - critically - whether they use customer data to train their models. Confirm compliance with relevant data protection laws and security frameworks. If the vendor can’t answer these questions with specifics, that tells you something important. “We take security seriously” is not an answer.

Pillar 2: Model performance and explainability. Ask vendors to provide documentation of model training, validation, and testing. Do they align with recognized frameworks like ISO 42001 or NIST’s AI Risk Management Framework? Are there documented measures to detect and mitigate algorithmic bias? A vendor who has done this work will have materials ready. One who hasn’t will struggle to answer.

Pillar 3: Compliance, governance, and ethics. Ask how the vendor monitors regulatory changes and ensures their products remain compliant as the legal landscape evolves. Do they have an ethics board or responsible AI committee? Require evidence of policies and processes - not just assurances. The depth of a vendor’s answer here is itself informative. Generic platitudes about “responsible AI” are not the same as documented governance practices.

Pillar 4: Support, implementation, and scalability. Evaluate the vendor’s commitment to ongoing support, their product roadmap transparency, and how their change management process aligns with yours. Ask about integration requirements and what resources they expect you to commit. The implementation reality is often very different from the sales conversation - surface that discrepancy before you sign, not after.

Use open-ended questions rather than yes-or-no prompts - the depth of a vendor’s answers is itself informative. Treat the questionnaire as a living document that evolves as regulations change and your risk tolerance adjusts. Calibrate the depth of your inquiry to the risk level of each tool: a high-risk system warrants exhaustive diligence; a low-risk internal productivity tool may need a lighter touch.

A completed questionnaire is not the finish line. It should be accompanied by contract review, security assessments, and proof-of-concept testing before any vendor is approved. But it’s the essential first step - the mechanism that separates vendors who have genuinely invested in responsible AI from those who have only invested in saying the right things.