AI Vendor Security Evaluation
Related Templates
What This Requires
Assess all AI vendors and third-party AI service providers against AI-specific security criteria before onboarding and on an annual renewal cycle. The evaluation must address whether vendors use customer data for model training, data residency and sovereignty commitments, model provenance and supply chain integrity, incident notification obligations, and contractual rights to audit. Evaluation results must be documented and factor into procurement decisions.
Why It Matters
AI vendors present unique risks beyond traditional SaaS suppliers: customer prompts and data may be used to train future model versions, model updates can silently change behavior and introduce new risks, and supply chain attacks can propagate through shared model weights or training data. Standard vendor security questionnaires do not address these AI-specific concerns. Without targeted evaluation, organizations unknowingly accept risks including loss of intellectual property through training data ingestion, regulatory violations from undisclosed cross-border data processing, and dependency on opaque model supply chains.
How To Implement
Develop AI-Specific Vendor Questionnaire
Extend the organization's existing vendor security assessment with AI-specific sections covering: data usage policies (does the vendor use customer inputs/outputs for model training, fine-tuning, or evaluation?), data residency (where are prompts processed and stored, and can geographic restrictions be enforced?), model provenance (what base models are used, are they open-source or proprietary, what is the training data lineage?), model update practices (how are customers notified of model changes, is rollback available?), incident response (what are vendor SLAs for AI-specific incidents, what forensic data is available?), and subprocessor transparency (does the vendor use third-party model providers?).
Establish Risk-Based Evaluation Tiers
Not all AI vendors require the same depth of evaluation. Define tiers based on data sensitivity and criticality: Tier 1 (processes Confidential or Restricted data, or makes automated decisions) requires full questionnaire, evidence review, and contractual negotiations. Tier 2 (processes Internal data for productivity use cases) requires the AI-specific questionnaire and standard security assessment. Tier 3 (no organizational data processed, general information queries only) requires a lightweight checklist.
Negotiate AI-Specific Contract Terms
For Tier 1 and Tier 2 vendors, negotiate contractual provisions including: explicit prohibition on using customer data for model training without opt-in consent, data deletion upon contract termination with certification, right-to-audit clause covering AI-specific controls, model change notification with minimum advance notice period, incident notification SLAs specific to AI events, and indemnification for AI-related regulatory penalties.
Monitor Vendor Compliance Continuously
Do not treat vendor evaluation as a point-in-time exercise. Subscribe to vendor security advisories and AI-specific release notes. Monitor vendor compliance with contractual commitments through periodic evidence requests (SOC 2 reports, data processing records, model update changelogs). Re-evaluate vendors when they announce significant model changes, acquisitions, or policy updates. Maintain a vendor risk register that tracks evaluation status, open findings, and next review dates.
Evidence & Audit
- AI-specific vendor security questionnaire template
- Completed vendor evaluation records for each AI vendor with risk tier classification
- Contract excerpts showing AI-specific provisions (data training prohibition, right to audit, incident SLAs)
- Vendor risk register with evaluation status, findings, and next review dates
- Evidence of vendor compliance monitoring (advisory subscriptions, periodic evidence requests)
- Procurement approval records showing evaluation completion as a prerequisite
- Re-evaluation records triggered by material vendor changes