Key Takeaway
AI vendor management policies must include ongoing monitoring requirements, because vendor model updates can change system behavior without any changes to your code.
When to Use This Template
Use this template when establishing or formalizing your AI vendor management practices. AI vendors carry unique risks compared to traditional software vendors: model updates can change behavior without notice, vendor training data practices affect your compliance posture, and shared responsibility for AI outputs creates liability questions. This policy addresses these AI-specific vendor management challenges.
Policy Sections
Define the evaluation process for new AI vendors: security assessment (SOC 2 report review, data handling questionnaire, penetration test results), capability evaluation (proof-of-concept with representative data, performance benchmarking), operational assessment (SLA review, support tier evaluation, incident response capabilities), and commercial evaluation (pricing model analysis, contract flexibility, vendor financial stability). All evaluations must be documented using the AI Vendor Scorecard template.
AI vendor contracts must include: data handling clauses (vendor must not use your data for model training without explicit consent), model change notification (vendor must provide advance notice of model updates that could affect behavior), SLA definitions (availability, latency, throughput with measurement methodology), liability allocation (who is responsible for AI outputs and decisions), IP ownership (clear ownership of fine-tuned models and generated content), and exit provisions (data portability format, transition timeline, post-termination data deletion).
Define monitoring requirements for active AI vendors: performance tracking (model quality metrics, latency, availability against SLA), security posture reviews (annual SOC 2 report review, quarterly data handling audit), cost analysis (monthly spend tracking, per-unit cost trends, budget variance), and behavior change detection (automated quality monitoring that detects model behavior changes from vendor updates). Flag any vendor that modifies model behavior without meeting notification requirements.
Every AI vendor relationship must have a documented exit plan before onboarding completes. The exit plan includes: data portability (how to extract data in usable format), migration timeline (estimated effort to switch to an alternative), alternative vendors (pre-evaluated alternatives that could serve as replacements), and contract termination triggers (conditions that would prompt immediate termination). Conduct an annual vendor review assessing continued fit, market alternatives, contract renegotiation opportunities, and risk posture changes.
Customization Guidance
Adapt the contract requirements and monitoring intensity to your organization's risk tolerance and regulatory environment. Highly regulated industries should require more frequent vendor audits and stricter data handling clauses. Organizations with lower risk profiles may simplify the monitoring requirements while maintaining the core contract protections. Always maintain exit plans regardless of risk profile.
Negotiate model change notification clauses into contracts upfront. Once a contract is signed without this clause, you have no recourse when a vendor updates their model and your application quality changes. This is the single most important AI-specific contract term.
Version History
1.0.0 · 2026-03-01
- • Initial AI vendor management policy template