Third-Party AI Risk Management

Third-party AI risk management is the discipline of identifying, assessing, and controlling the risks that come from external AI vendors, model providers, agent platforms, data processors, and integration services. It goes beyond procurement. The core question is not only whether a provider looks suitable before signing a contract, but how that provider behaves throughout production use: what data it receives, which subprocessors it relies on, how its models change, what security evidence it can provide, and how quickly the business can switch away if cost, availability, compliance, or trust changes. AI makes third-party risk more dynamic than traditional SaaS risk. A vendor can change model behavior, terms, pricing, retention settings, API semantics, or safety controls while the customer’s application code stays the same. Agentic systems add another layer because agents may call external tools on behalf of users and multiply data flows across services. Strong third-party AI risk management combines contract review, data classification, technical access controls, model provenance checks, exit planning, ongoing audits, and clear human-approval thresholds. The goal is to make external AI dependencies visible, testable, and replaceable before they become operational or regulatory liabilities.