Back to blog
·Qadar AI
AI Vendor Risk Management: An Evaluation Checklist
Vendor RiskAI ProcurementCompliance
Evaluating AI vendors requires moving beyond standard SOC 2 reports to understand how each provider handles model training, data retention, and instructional security. As AI becomes a core component of the enterprise tech stack, procurement teams must implement specialized risk management checks to ensure that AI adoption doesn't compromise corporate security or data privacy.
The Three Dimensions of AI Vendor Risk
- Model Transparency: How is the model trained? Does the vendor use customer data for future model versions? Can you opt-out of data training?
- Data Residency and Sovereignty: Where is the data processed? For companies in the EU, ensuring data stays within the region is often a disqualifying compliance requirement.
- Instructional Security: Does the vendor provide built-in protections against prompt injection and jailbreaking? What is their policy for reporting and patching model-layer vulnerabilities?
The AI Vendor Evaluation Checklist
- Data Processing Agreement (DPA): Does the vendor have a DPA that explicitly covers AI-specific data handling and GDPR requirements?
- Data Training Policy: Is there a clear, enforceable "no-training" guarantee for enterprise API customers?
- Encryption and Access: How is data encrypted in transit and at rest? Who at the vendor has access to raw prompt logs?
- Retention Periods: What is the default log retention period? Can it be configured to meet your organization's compliance needs?
- Threat Monitoring: Does the vendor provide alerts for potential security incidents or model abuses?
Consolidating Governance
Managing risk across multiple vendors is complex. Many enterprises use a security layer like Shield Control to enforce a single, consistent policy across all AI providers, reducing the burden on procurement and compliance teams.
