The systematic process of identifying, assessing, and mitigating the risks associated with AI systems acquired or used from external vendors or service providers. This is a critical component of AI Risk Management and Due Diligence required of Deployers under the EU AI Act.

This process acknowledges that most organizations will deploy AI systems they did not build internally, yet they retain full legal and operational accountability for the outcomes. It extends traditional vendor risk management to address the unique, dynamic hazards of AI, such as algorithmic bias, model drift, and transparency deficits. Effective third-party AI risk management shifts the deployer’s role from passive consumer to active steward, requiring rigorous due diligence before procurement, continuous oversight during use, and clear contractual mechanisms to ensure the provider supports ongoing compliance and incident response.

Implementing third-party AI risk management involves a lifecycle approach:

Pre-Procurement Due Diligence: Conducting thorough assessments of potential vendors, demanding and reviewing their technical documentation, conformity assessment evidence (CE marking), and the results of any red-teaming or bias audits.

Contractual Safeguards: Embedding specific clauses in agreements that mandate the provider’s ongoing support for compliance (e.g., providing updates to maintain conformity, assisting with incident reporting), grant access to logs and documentation for audits, and allocate liability for failures attributable to the provider’s system.

Ongoing Monitoring & Validation: Continuously verifying that the third-party system performs as expected in the deployer’s specific context, using post-market monitoring to detect drift, and ensuring the provider communicates any substantial modifications or discovered vulnerabilities.

Exit & Transition Planning: Establishing protocols for responsibly decommissioning or transitioning away from a third-party AI system, including data retrieval, model archiving, and ensuring business continuity.

Regulatory Context: The EU AI Act places explicit obligations on deployers of high-risk AI systems (Article 26), regardless of whether the system is developed in-house or procured. This includes duties to ensure human oversight, monitor operation, and maintain logs. These obligations inherently require deployers to manage their third-party providers effectively. Furthermore, the Act holds importers and distributors accountable for verifying a provider’s conformity (Articles 24 & 25), reinforcing the supply chain risk management concept.

Supply Chain Accountability: Robust third-party AI risk management is essential for a deployer to fulfill its legal "duty of care." Failure to adequately vet and oversee vendors can lead to direct regulatory penalties, legal liability for harms, and severe reputational damage. It transforms vendor relationships from purely transactional to collaborative partnerships focused on shared responsibility for trustworthy AI outcomes.

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