AI Buying Checklist

AI Vendor Due Diligence Checklist for Procurement and Security Review

Use this AI vendor due diligence checklist to review security, data handling, integrations, governance, pricing, support, pilot proof, and rollout risk before approving an AI software vendor.

Updated 2026-06-243 buying gates5 red flags

Business and workflow fit

Confirm the vendor solves a named workflow instead of selling a generic AI demo.

Name the workflow owner, current baseline, approval budget, expected volume, and measurable success metric.
Ask the vendor to show the exact user path from intake to review, action, escalation, and reporting.
Require proof from a similar workflow, industry, data type, or pilot dataset.

Security and data review

AI due diligence fails if data rights and model behavior are reviewed after the shortlist is chosen.

Review data retention, model training use, subprocessors, encryption, access controls, SSO, audit logs, and region controls.
Confirm whether prompts, files, embeddings, outputs, telemetry, and human feedback can be exported or deleted.
Ask how the vendor detects unsafe outputs, hallucinations, prompt injection, data leakage, and unauthorized actions.

Commercial and rollout evidence

Compare the total operating model, not only license cost or demo quality.

Model seats, usage, premium models, implementation services, support tier, integrations, overage, renewal, and exit cost.
Require a pilot plan with historical examples, reviewer feedback, acceptance thresholds, and rollback criteria.
Assign adoption, measurement, support, governance, and renewal owners before approval.

Red flags

The vendor cannot explain whether customer data trains models.
The security review is limited to a generic PDF without workflow-specific controls.
Pricing depends on usage or premium models but the vendor will not model realistic volume.
The pilot uses only demo data and cannot be repeated on your historical cases.
Human review, audit logs, export, and rollback are unclear for high-impact actions.

Evidence to collect

Security documentation, SOC 2 or ISO evidence, subprocessors, data retention policy, and incident process.
Architecture diagram, integration plan, implementation timeline, API documentation, and admin role model.
Pilot dataset, acceptance criteria, reviewer notes, error examples, time saved, cost model, and rollout owner list.

How to use it

Turn the checklist into a buying decision

Step 1
Complete the checklist before final vendor demos.
Step 2
Send missing evidence requests to every shortlisted vendor.
Step 3
Feed the results into the AI vendor scorecard template and calculator.
Step 4
Require procurement, security, finance, and the business owner to approve the same evidence packet.

Related buyer paths

Use the next artifact

AI Software Buyer Guides

Open commercial AI software categories after the checklist identifies the workflow and owner.

AI Buying Templates

Turn checklist answers into an RFP, scorecard, security questionnaire, POC plan, or business case.

AI Governance Guides

Plan governance frameworks, risk assessments, vendor risk, model risk, compliance automation, and policy management.

AI Cost Guides

Estimate AI software, implementation, RAG, agent, chatbot, and document automation cost before approval.

AI ROI Guides

Calculate ROI, payback, automation savings, chatbot savings, agent ROI, and AI business case readiness.

AI Services Buyer Guides

Evaluate consultants, implementation partners, automation agencies, integration services, and enterprise AI advisors.

AI Vendor Scorecard Calculator

Convert evidence, risk, fit, and pilot results into a weighted vendor decision.

AI Security Questionnaire

Use deeper AI data, model, privacy, access, audit, and incident questions during vendor review.

AI Vendor Scorecard Template

Turn due diligence evidence into a scored shortlist that finance and security can review.

What should AI vendor due diligence include?

AI vendor due diligence should include business fit, security controls, data handling, model training policy, integrations, auditability, pricing, support, pilot evidence, rollout ownership, and exit risk.

Who should approve an AI vendor due diligence checklist?

The business owner, IT owner, security reviewer, procurement lead, finance approver, and legal or compliance reviewer should approve the evidence packet before purchase.