Managing Third-Party AI Risk in Your Supply Chain

Your company might have a mature AI governance program covering every model you build and every agent you deploy. But if your payroll vendor just added an AI feature that processes your employee data, or your customer support platform started using generative AI to draft responses on your behalf, their AI risks are now your AI risks. And you probably did not review those deployments.

Third-party AI risk is not a new category so much as an evolution of existing vendor risk management. The underlying principle is the same: when you outsource a business function, you do not outsource the liability. What changes with AI is the nature of the risks, the difficulty of assessing them, and the speed at which they can materialize.

How Third-Party AI Creates Exposure

Traditional vendor risk focuses on data security, service availability, and regulatory compliance. AI introduces additional dimensions that most vendor assessment frameworks were not designed to evaluate.

Bias and fairness risk: if your hiring platform uses AI to screen candidates and that AI discriminates against a protected class, your company faces the legal and reputational consequences. The fact that a third party built the model is not a defense. You deployed it. You are responsible for its outputs.

Data handling risk: when you share data with an AI-powered vendor, that data may be used to train or improve their models. Your customer records could end up influencing outputs for your competitors who use the same platform. Most organizations do not have clear contractual provisions governing this.

Model reliability risk: AI models degrade over time as the data they were trained on becomes less representative of current conditions. If your fraud detection vendor's model has not been retrained in 18 months, its accuracy may have declined significantly. Unlike traditional software that either works or does not, AI systems fail gradually and silently.

Supply chain depth: your vendor's AI may itself depend on third-party models, cloud AI services, or training data providers. When third-party AI tools are introduced, risk exposure extends deep into the supply chain. Risk cascades downstream as well and can impact clients, regulators, and national infrastructure. A 2025 EY survey found that 64% of organizations now monitor their vendors' vendors, a practice that was previously impossible at scale but is increasingly necessary.

The Assessment Gap

Most vendor assessment questionnaires, the SOC 2 reviews and security questionnaires that procurement teams send to every vendor, do not ask the right questions about AI. They ask about data encryption, access controls, and incident response. They rarely ask about model training practices, bias testing, explainability capabilities, or AI-specific regulatory compliance.

This creates a gap between the risks organizations actually face and the risks they assess for. A vendor can pass every traditional security review with flying colors while running an AI system that trains on your data, produces biased outputs, and cannot explain its decisions. The assessment framework needs to evolve.

A Practical Framework for Third-Party AI Risk

Effective third-party AI risk management requires extending your existing vendor management program with AI-specific evaluation criteria. The following framework provides a starting point.

AI Inventory and Classification

Before you can assess AI risk, you need to know which vendors use AI and how. Survey your vendor portfolio to identify every third party that uses AI in the services they provide to you. Classify each vendor's AI use by risk level based on the sensitivity of the data it processes, the consequentiality of the decisions it influences, and the regulatory requirements that apply.

AI-Specific Due Diligence Questions

Add these categories to your vendor assessment process:

• Data handling: Is your data used to train or improve the vendor's AI models? Can you opt out? What data retention policies apply to AI processing specifically?

• Model governance: How often are models retrained? What testing is performed before updates are deployed? Is there a rollback procedure if a model update causes problems?

• Bias and fairness: Has the AI been tested for bias? What populations were included in testing? How are bias issues identified and remediated?

• Explainability: Can the vendor explain individual AI decisions? What audit trail capabilities are available? Can you access decision logs for compliance purposes?

• Regulatory compliance: Is the vendor compliant with applicable AI regulations (EU AI Act, sector-specific requirements)? Can they provide evidence of compliance?

• Sub-processors: Does the vendor use third-party AI models or services? What governance applies to those sub-processor relationships?

Contractual Provisions

Your vendor agreements need specific clauses addressing AI. Key provisions include prohibitions on using your data for model training without explicit consent, requirements for notification when AI features are added or significantly changed, audit rights specific to AI systems and their outputs, liability allocation for AI-related incidents including bias, data leakage, and regulatory violations, and requirements for the vendor to maintain compliance with applicable AI regulations.

Continuous Monitoring

Point-in-time assessments are insufficient for AI risk. Models change. Data drifts. Regulatory requirements evolve. Implement ongoing monitoring that tracks vendor AI performance, reviews model update notifications, and reassesses risk classification on a regular cadence. Align your monitoring with frameworks like COBIT for AI Governance, the NIST AI Risk Management Framework, and ISO 42001 to maintain a consistent and defensible approach.

Organizational Integration

Third-party AI risk management cannot live solely within procurement or IT. It requires collaboration between procurement (who manages vendor relationships), legal (who structures contracts), compliance (who tracks regulatory requirements), information security (who evaluates technical controls), and the business units that actually use the vendor's AI-powered services.

Each of these functions brings a necessary perspective. Procurement understands the vendor relationship. Legal understands the liability. Compliance understands the regulatory requirements. Security understands the technical risks. And the business unit understands how the AI is actually being used and what would happen if it failed.

The organizations managing this well are the ones that treat third-party AI risk as a cross-functional concern with clear ownership, regular review cycles, and escalation paths for issues that cross functional boundaries. The ones struggling are typically those that have not yet recognized that their vendor's AI deployment is their problem too.

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