AI Governance Guide
AI Vendor Risk Management Guide
Manage AI vendor risk across data use, model training policy, subprocessors, security evidence, audit logs, contractual controls, exit plans, and renewal review.
Use this as a planning and buyer research structure, not legal advice. Confirm legal, regulatory, contractual, and industry-specific requirements with qualified legal, compliance, and security owners.
Discovery questions
Clarify governance scope before approval
Data processing scope
Vendor risk depends on whether the vendor receives prompts, files, customer data, logs, metadata, or source system access.
What data does the vendor process, store, learn from, or pass to subprocessors?
Model training policy
Buyers need to know whether customer data can be used for training, evaluation, debugging, support, or product improvement.
Can the vendor use company data to train or improve models, and can that be disabled contractually?
Security and audit evidence
AI vendor review should include security posture, access control, audit logs, retention, incident response, and compliance evidence.
What evidence can security, privacy, and compliance teams review before approval?
Exit and portability
AI workflows can create prompts, embeddings, fine-tuning data, histories, generated assets, and integrations that are hard to move.
How can the buyer export data, remove access, and replace the vendor if needed?
Control areas
Compare risk controls by evidence
Contract controls
Contracts should address data use, retention, deletion, subprocessors, security commitments, support access, and notification duties.
Which vendor promises are enforceable in the contract rather than only sales language?
Access and integration controls
Review SSO, role permissions, audit logs, API scopes, admin rights, and service account ownership.
Can the vendor or its integration access more data than the workflow requires?
Evidence refresh
Vendor risk is not one-time; product changes, new models, new subprocessors, and renewal terms can change risk.
When must the vendor submit updated security, privacy, and model evidence?
Business continuity
AI vendors can affect customer support, operations, sales, finance, legal, or analytics workflows.
What is the fallback if the vendor degrades, changes terms, loses a model, or has an incident?
Decision steps
- 1Classify the vendor by data sensitivity, workflow criticality, integration depth, and automation level.
- 2Use a security questionnaire and contract review before sharing sensitive data.
- 3Require model training, retention, deletion, and subprocessor answers in writing.
- 4Attach risk controls to the business case so approval is not only a feature decision.
- 5Refresh vendor evidence before renewal, expansion, or major product changes.
Evidence artifacts
- AI vendor questionnaire with data use, training, retention, deletion, subprocessors, security, and audit answers.
- Security evidence package such as reports, customer security guide, penetration test summary, and incident process.
- Contract summary covering data rights, model training restrictions, subprocessors, retention, and deletion.
- Integration access review for identity, APIs, scopes, service accounts, and administrative permissions.
- Renewal review packet with adoption, incidents, cost, risk changes, and exit options.
Operating models
Choose the right governance depth
Procurement-led review
Teams buying AI subscriptions or SaaS copilots.
Vendor questionnaire, contract review, pricing terms, owner, and renewal date.
Watch out: Procurement review should route sensitive data and automation cases to security.
Security-led review
Tools that process sensitive data, connect to systems, or trigger actions.
Security questionnaire, SOC or ISO evidence, DPA, audit logging, access model, and incident plan.
Watch out: Security approval alone does not prove business value or adoption.
Vendor risk register
Organizations managing many AI vendors across teams.
Risk tier, owner, data class, contract status, evidence freshness, and renewal decision.
Watch out: A register becomes stale unless renewal and product-change triggers are enforced.
Related governance guides
FAQ
What is AI vendor risk management?
AI vendor risk management reviews how an AI vendor handles data, models, training, subprocessors, security, access, audit logs, incidents, contracts, continuity, and exit before purchase and renewal.
What should I ask an AI vendor before buying?
Ask how data is used, retained, deleted, protected, logged, shared with subprocessors, used for model training, accessed by support teams, monitored for incidents, exported, and covered by contract terms.
Related buyer paths
Turn governance work into a buying packet
AI Governance Readiness Checker
Score governance readiness across use cases, data rules, vendor review, human oversight, monitoring, policy, and incident response.
AI Software Buyer Guides
Compare AI software categories after governance owners, data risk, and workflow controls are clear.
AI Buying Templates
Use RFP, scorecard, security questionnaire, POC, business case, and governance policy templates.
AI Buying Checklists
Run vendor due diligence, security review, implementation readiness, and governance readiness checks.
AI Cost Guides
Estimate governance, monitoring, implementation, reviewer, audit, and support cost before approval.
AI ROI Guides
Connect governance cost to risk reduction, faster approval, controlled rollout, and renewal evidence.
AI Services Buyer Guides
Evaluate consultants, implementation partners, and enterprise AI advisors when governance work needs outside help.
AI Vendor Scorecard Template
Compare vendors across value, workflow fit, security, governance, pricing, and pilot evidence.
AI Vendor Scorecard Calculator
Score shortlisted AI vendors before procurement approval.