What should an AI vendor due diligence checklist include?
It should include data use, model training, retention, subprocessors, permissions, tool actions, human approval, audit logs, incident support, contract controls, and post-launch ownership.
AI automation resource
AI Vendor Due Diligence Checklist
AI vendor due diligence checklist for reviewing data use, model training, security, permissions, subprocessors, incidents, contracts, and support.
Search intent
Business buyers, security reviewers, and operators checking an AI automation vendor before sharing production data, granting system access, or signing implementation terms.
AI vendor due diligence should make vendor risk visible before the automation touches real workflows. The buyer should review data use, model training, retention, subprocessors, permissions, tool actions, approval controls, audit logs, incident support, contract terms, and post-launch ownership before signing or granting access.
Guide sections
A practical framework for the workflow decision.
These resources support buyers who are still comparing examples, controls, ROI, and implementation readiness.
Data use
Confirm what data is processed, stored, retained, deleted, exported, used for evaluation, or used for model training.
Access scope
Review service accounts, least-privilege access, read-only options, write-back limits, permission expansion, and revocation steps.
Tool actions
List which tools the agent can call, which actions are draft-only, which require approval, and which actions are blocked.
Subprocessors
Request hosting regions, model providers, logging vendors, support systems, data locations, subprocessors, and notification commitments.
Evidence and logs
Require evidence for prompts, source records, tool calls, reviewer decisions, errors, approvals, changed records, and incident timelines.
Incident support
Confirm escalation contacts, response targets, pause authority, rollback help, evidence preservation, and post-incident review ownership.
Contract controls
Move accepted data, access, audit, support, SLA, change-control, and liability expectations into the SOW before work starts.
Buying decision
Use due diligence findings to compare vendors, negotiate terms, limit scope, require remediation, or reject the vendor.
Checklist
What to confirm before moving from research to implementation.
A useful resource page should help the buyer make a better decision before they contact anyone.
- Confirm whether customer data is stored, retained, exported, used for model training, or shared with subprocessors.
- Review service accounts, permissions, tool actions, write-back limits, approval requirements, and revocation steps.
- Require audit logs for inputs, outputs, source evidence, tool calls, reviewer decisions, errors, approvals, and changed records.
- Document incident response, pause authority, rollback support, escalation contacts, and post-incident reporting.
- Move accepted data handling, access, security, support, SLA, and change-control commitments into the contract or SOW.
- Do not approve production data, write permissions, customer-facing actions, or pilot spend until due diligence gaps are closed.
FAQ
Common vendor due diligence questions.
Short answers for teams researching AI workflow automation before choosing a pilot.
When should AI vendor due diligence happen?
Do it before sharing production data, granting system access, approving write permissions, signing an SOW, or allowing the vendor to handle customer-facing workflow actions.
How is vendor due diligence different from a security questionnaire?
A security questionnaire collects vendor answers. Due diligence turns those answers into a buying decision, contract controls, remediation requests, access limits, or a decision not to proceed.
Next step
Turn the guide into a scoped workflow review.
We will help identify the workflow, approval boundary, data sources, and ROI model that make sense for a first pilot.