Allocating responsibilities for third-parties

ISO/IEC 42001 Annex A.10.2 requires organizations to explicitly define and document the allocation of responsibilities across the AI system life cycle among themselves, their partners, suppliers, customers, and any other third parties. This ensures clear accountability for all AI-related tasks.

To define a shared responsibility model for AI services, organizations must analyze the AI supply chain to document all intervening parties and explicitly assign roles such as providing data, supplying algorithms, managing infrastructure, and performing human oversight, often visualized using a formal matrix.

Even when using a vendor AI model, the organization typically retains responsibility for evaluating the model's suitability for the intended use, enforcing acceptable use internally, obtaining necessary user consents, securing the application interface, and conducting final human oversight over AI-generated decisions.

Contracts should include strict third-party AI contract clauses regarding accountability for model accuracy, bias testing, incident reporting timelines, intellectual property rights, data sovereignty, and service level agreements (SLAs) for system uptime and performance support.

Accountability is dictated by the documented allocation of responsibilities and contractual agreements. While vendors may be accountable for underlying model flaws or infrastructure breaches, the organization deploying the AI often remains ultimately responsible to its end-users and regulators for the final output and operational impact.

AI roles should be documented using a formal matrix, such as RACI (Responsible, Accountable, Consulted, Informed), that maps specific AI life cycle stages—like data preparation, model training, evaluation, and operational monitoring—to specific internal teams, external vendors, and data providers. Tools like WatchDog Security's Policy Management can store these matrices as controlled documents, route approvals, and track acknowledgements when responsibilities are updated.

To verify third-party AI risk management responsibilities, auditors will look for documented vendor security reviews, signed Master Services Agreements (MSAs), Data Processing Agreements (DPAs), explicit shared responsibility documentation, and comprehensive third-party management policies. Tools like WatchDog Security's Compliance Center can map Annex A.10.2 to expected evidence and collection workflows, while WatchDog Security's Vendor Risk Management can retain vendor assessments, findings, and remediation actions over time.

Responsibilities for model updates and change control must be explicitly defined in vendor contracts. This includes specifying whether the customer or the vendor triggers retraining, how data drift is managed, and the required notification protocols when the vendor deploys an updated base model.

When processed data includes PII, responsibilities are split between data controllers and data processors. This requires a formal Data Processing Agreement (DPA) that outlines exactly who secures the data, manages access controls, handles data subject rights, and ensures compliance with privacy frameworks like ISO/IEC 27701.

Organizations assess and monitor these risks by conducting initial vendor risk assessments, integrating third-party components into internal AI impact assessments, enforcing right-to-audit clauses, and establishing continuous monitoring of vendor SLAs, security posture, and AI performance metrics. Tools like WatchDog Security's Vendor Risk Management can schedule recurring reviews and track supplier obligations, and WatchDog Security's Risk Register can link third-party findings to risk owners, treatment plans, and reporting.

Allocating responsibilities is easiest when roles, obligations, and evidence are tracked in one place so gaps are visible. Tools like WatchDog Security's Vendor Risk Management can maintain a vendor catalog with risk-tiering and assessment outcomes, while WatchDog Security's Policy Management can version-control shared responsibility matrices (e.g., RACI) and track approvals when responsibilities change.

Evidence sharing should enforce least-privilege access, provide an audit trail, and avoid emailing sensitive documents. Tools like WatchDog Security's Trust Center can publish approved third-party assurance artifacts in a controlled portal, and WatchDog Security's Secure File Sharing can support encrypted, time-bound sharing with verification and audit logs.