Managing third-party AI risks means identifying and controlling risks that arise when organizations use external AI systems or services. This includes AI models, APIs, tools, or platforms developed by other vendors. These risks can affect compliance, security, fairness, and overall business performance.
Managing third-party AI risks matters because companies no longer build every AI tool internally. External partnerships introduce new layers of risk that are harder to see or manage. AI governance, compliance, and risk teams must focus on third-party risks to meet obligations under standards like ISO/IEC 42001 and regulations such as the EU AI Act.
The hidden risks of third-party AI
A recent Gartner study predicts that 45% of organizations will suffer some type of AI-driven data or model failure by 2025. Many of these failures will come from third-party tools that were trusted without enough review.
Vendors may not disclose full model details, data sources, or risks. Companies that blindly adopt external AI tools expose themselves to hidden biases, security gaps, or legal liabilities. Managing these risks early protects not only customers but also internal operations and brand reputation.
Key areas of third-party AI risk
Third-party AI risks usually fall into several important categories:
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Model bias: Vendors may use biased training data, leading to unfair or discriminatory outcomes.
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Lack of transparency: Many AI vendors treat models as black boxes, offering limited visibility into how decisions are made.
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Security vulnerabilities: External AI systems may introduce new entry points for cyberattacks or data breaches.
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Regulatory compliance gaps: Vendors may not comply with regulations such as the EU AI Act or sector-specific rules.
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Performance instability: Models that work well in one context may fail in another if vendors do not regularly validate and monitor performance.
Each risk type requires different controls, but the first step is knowing that they exist.
Best practices for managing third-party AI risks
Good risk management starts with strong vendor oversight. Assume that every external AI system carries inherent risks and plan accordingly.
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Vendor assessments: Perform thorough reviews of AI vendors before signing contracts. Assess their security practices, model validation processes, and regulatory compliance efforts.
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Contractual protections: Include specific terms in contracts that require transparency, audit rights, and immediate reporting of incidents.
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Ongoing monitoring: Do not treat vendor selection as a one-time event. Regularly monitor third-party AI systems for performance, fairness, and security issues.
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Transparency requirements: Push vendors to provide explainability reports, model cards, or validation summaries.
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Incident response plans: Have a clear plan for responding if a third-party AI tool causes harm or fails unexpectedly.
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Alignment with standards: Choose vendors who align with standards like ISO/IEC 42001 for managing AI responsibly.
Following these practices improves resilience and makes third-party AI risks manageable instead of mysterious.
FAQ
What makes third-party AI risks harder to control?
Third-party risks are harder to control because organizations have less visibility and influence over how external AI systems are built, maintained, and updated.
Can contracts really reduce third-party AI risks?
Yes. Contracts can force vendors to meet certain transparency, auditability, and accountability requirements. Strong legal terms create leverage if something goes wrong.
How can companies monitor third-party AI tools after adoption?
Companies can monitor third-party tools by tracking key performance indicators, fairness metrics, and security incidents. Asking vendors for regular updates and validation reports is also critical.
Is using open-source AI safer than using third-party commercial AI?
Open-source AI gives more visibility into code and model behavior, but it still carries risks. Companies must validate open-source models just as thoroughly as commercial ones.
What should be included in a vendor risk assessment?
Vendor risk assessments should evaluate the vendor’s model development practices, security posture, bias mitigation efforts, regulatory compliance, incident history, and transparency levels.
Summary
Managing third-party AI risks is critical as companies increasingly rely on external tools to power their operations. Clear assessment processes, strong contracts, and continuous oversight help minimize these risks and protect organizations from ethical, legal, and operational harm. Building a risk-aware approach to third-party AI use is no longer optional for any serious AI governance program.
