What is AI Vendor Outsourcing and Third-Party Risk Management?

In practice, this means due diligence before onboarding, contractual controls on data use, model changes, audit rights, incident notification, subcontracting, service levels, and exit arrangements, plus ongoing review of vendor performance and control effectiveness. For AI, third-party risk management also has to cover data leakage, prompt and output handling, training on customer data, intellectual property issues, cross-border transfers, model updates, and whether the vendor’s system supports the buyer’s own compliance duties.

This is especially important for regulated firms that rely on cloud AI services, foundation models, embedded AI features, or outsourced model development, because the buying organization may still be responsible for privacy, security, consumer protection, employment, or financial-services obligations. Relevant frameworks include DORA, NIS2, ISO 27001, ISO/IEC 42001, SOC 2 + AI, and sector-specific supervisory expectations, all of which emphasize supplier oversight, resilience, and documented control of external dependencies.