Third-party AI risks happen when organisations rely on external vendors for AI, platforms, or data. Many AI capabilities are delivered through cloud services, software vendors, or embedded functionality that sits outside direct organisational control. This creates visibility and accountability challenges that differ from traditional supplier risk. This article explains why third-party AI risk matters, what risks should be assessed, and how organisations can manage vendor-related AI exposure.
Why does third-party AI risk require special attention?
AI supplied by third parties often operates as a black box. Organisations may have limited insight into how data is used, how models are trained, or how risks are managed.
Key third-party AI risks include:
- Limited transparency into model behaviour and decision logic
- Unclear data handling and retention practices
- Inherited bias or data quality issues
- Security weaknesses within vendor environments
- Dependency on vendor availability and change management
These risks are transferred, not removed, when AI is sourced externally.
What should organisations assess in third-party AI providers?
Traditional supplier assessments must include AI-specific considerations to understand real risk exposure.
Key assessment areas include:
- Data access, storage, and processing arrangements
- Compliance with privacy and data protection obligations
- Governance and risk management practices for AI
- Security controls and incident response capabilities
- Model lifecycle management and update practices
Assessments should be proportionate to the impact and sensitivity of the AI use case.
How can contracts support third-party AI risk management?
Contracts help manage third-party AI risk and are a tool for accountability when technical transparency is limited.
Effective contractual controls often address:
- Data ownership, use, and retention limitations
- Privacy and security obligations
- Notification and cooperation during incidents
- Audit and assurance rights
- Responsibilities for regulatory compliance
Contracts should reflect the risk profile of the AI use rather than rely on standard procurement terms.
How does third-party AI risk fit into governance?
Third-party AI risk should be managed through existing governance, risk, and compliance processes rather than treated as an isolated issue.
Integration approaches include:
- Including AI vendors in third-party risk management programs
- Recording third-party AI risks in enterprise risk registers
- Linking vendor incidents to internal response processes
- Applying ongoing monitoring rather than one-off assessments
Standards such as ISO 42001 support this by requiring oversight across the AI supply chain.
Third-party AI risk management is essential where AI capability is sourced externally. Without structured assessment and oversight, organisations may inherit risks they cannot see or control. Integrating third-party AI risk into governance and supplier management processes supports accountability and reduces exposure across the AI lifecycle.