As organizations rapidly adopt artificial intelligence, many are overlooking a critical exposure point: third-party AI risk.

Companies are not just deploying AI internally—they are increasingly relying on vendors, platforms, and service providers that embed AI into their offerings. From SaaS providers using generative AI to analytics vendors deploying machine learning models, AI risk is now embedded across the third-party ecosystem.

This creates a fundamental shift in compliance expectations. Traditional vendor due diligence frameworks—focused on data security, financial stability, and legal compliance—are no longer sufficient.

Third-party risk management programs must now explicitly address AI-related risks as part of onboarding, contracting, and ongoing monitoring.

The Expanding Third-Party AI Risk Surface

Third-party AI risk is not confined to obvious vendors. It often exists several layers deep within a company’s operations.

Common sources of third-party AI risk include:

• SaaS providers integrating generative AI into existing platforms
• Vendors using AI for customer support, analytics, or decision-making
• Subprocessors and subcontractors leveraging AI tools
• API integrations that connect directly to external AI models

In many cases, organizations do not even realize that AI is being used within a vendor’s service delivery model.

Core Risk Areas in Third-Party AI Relationships

A best-in-class approach begins with understanding the specific risk categories introduced by third-party AI use.

1. Data Exposure and Confidentiality Risk

Vendors may input company or customer data into AI systems—sometimes public models—without adequate safeguards. This raises risks of:

• Data leakage
• Loss of confidentiality
• Violation of privacy laws and contractual obligations

2. Lack of Transparency (“Black Box” Risk)

Many AI systems operate without clear explainability. Vendors may be unable—or unwilling—to explain:

• How outputs are generated
• What data was used to train models
• Whether outputs are reliable or biased

This creates audit, regulatory, and reputational risk.

3. Bias and Discrimination Risk

If a vendor uses AI in hiring, lending, healthcare, or customer interactions, biased outputs can expose your organization—not just the vendor—to liability.

4. Regulatory and Enforcement Exposur

Regulators are increasingly focused on accountability across the value chain. Companies cannot outsource responsibility for:

• Consumer protection violations
• Employment discrimination
• Unfair or deceptive practices

5. Intellectual Property and Ownership Risk

AI-generated outputs raise complex questions:

• Who owns the output?
• Is the output infringing on third-party rights?
• Can it be used commercially?

Without contractual clarity, companies face downstream disputes.

Due Diligence Must Be Re-Engineered for AI

Traditional vendor questionnaires are not designed to capture AI risk. Organizations should expand due diligence to include AI-specific inquiries.

Key questions should include:

• Does the vendor use AI in delivering its services? If so, how?
• What types of data are processed by AI systems?
• Are public or proprietary models used?
• What controls exist to prevent data leakage or misuse?
• How are AI outputs validated and monitored?
• Has the vendor conducted bias or fairness testing?
• What governance structure oversees AI use internally?

This information should be documented, reviewed, and risk-rated before onboarding.

Contractual Protections Are Essential

Due diligence alone is not enough. AI risk must be addressed contractually.

Key provisions to consider include:

• Restrictions on data use (e.g., no use of company data for model training without consent)
• Confidentiality and security obligations specific to AI processing
• Audit rights related to AI systems and controls
• Representations and warranties regarding compliance with applicable laws
• Indemnification for AI-related harms, including IP infringement and regulatory violations
• Disclosure obligations if AI use changes during the contract term

Contracts should evolve alongside technology—not lag behind it.

Ongoing Monitoring: A Critical Missing Piece

One of the biggest gaps in third-party risk management is the lack of continuous monitoring.

AI use is dynamic. A vendor that does not use AI today may deploy it tomorrow.

Organizations should:

• Require periodic AI use certifications from vendors
• Monitor for changes in vendor technology and practices
• Reassess high-risk vendors on a recurring basis
• Integrate AI risk into existing third-party audit and review processes

Align AI Risk with Existing Compliance Frameworks

Third-party AI risk should not be treated as a standalone issue. It intersects with multiple compliance domains:

A coordinated, cross-functional approach is essential.

Practical Steps for Compliance Programs

To operationalize third-party AI risk management, companies should:

1. Update third-party risk policies to explicitly address AI
2. Enhance due diligence questionnaires with AI-specific questions
3. Train procurement, legal, and compliance teams on AI risks
4. Revise contract templates to include AI-related protections
5. Identify high-risk vendors for prioritized review
6. Establish governance oversight for AI-related vendor risk

These are practical, achievable steps that can significantly reduce exposure.

The Bottom Line

Third-party AI risk is not a future issue—it is a current and expanding compliance challenge.

Companies that fail to account for AI use within their vendor ecosystem risk inheriting liabilities they do not fully understand—and cannot easily control.

At the same time, organizations that proactively integrate AI risk into their third-party due diligence and onboarding processes will be better positioned to:

• Protect sensitive data
• Maintain regulatory compliance
• Preserve trust with customers and stakeholders

In today’s environment, effective compliance requires a simple shift in mindset:
You are responsible not only for how you use AI—but for how your vendors use it on your behalf.