First-party vs third-party AI risks

A recent study by Vals AI found that general-purpose AI models underperform in basic financial tasks, with all tested models averaging below 50% accuracy. This highlights the importance of understanding the risks associated with AI systems, particularly when distinguishing between first-party and third-party AI risks.

“Errors or misuse could lead to reputational damage and loss of customer trust, financial losses, regulatory penalties, and even litigation.” ​MIT Sloan

First-party AI risks refer to potential issues arising from AI systems developed and managed within an organization. These risks include data breaches, algorithmic biases, and system failures that can directly impact the organization’s operations and reputation.​Orrick | A Global Law Firm+4Ncontracts+4shermanhoward.com+4

Third-party AI risks involve the challenges associated with integrating external AI solutions into an organization’s processes. These risks can stem from lack of transparency, data privacy concerns, and dependency on vendors, potentially leading to compliance issues and operational disruptions.​

Why first-party vs third-party AI risks matter

Understanding the distinction between first-party and third-party AI risks is crucial for organizations to implement effective governance and compliance strategies. First-party risks are within the organization’s control and can be managed through internal policies and procedures. In contrast, third-party risks require careful vendor assessment and ongoing monitoring to ensure that external AI solutions align with the organization’s risk appetite and regulatory requirements.​

Real-world examples and practical use-cases

In the financial sector, a bank developing its own AI model for credit scoring (first-party) must ensure the model is free from biases and complies with fair lending regulations. Alternatively, if the bank uses an external AI service for fraud detection (third-party), it must assess the vendor’s data handling practices and model accuracy to mitigate potential risks.​

Best practices

Implementing best practices for managing AI risks involves a structured approach:​

• Risk assessment: Evaluate the potential risks associated with both first-party and third-party AI systems.

• Vendor due diligence: Conduct thorough assessments of third-party AI providers, including their data privacy policies and compliance records.

• Continuous monitoring: Regularly review and update risk management strategies to address evolving AI technologies and associated risks.

• Employee training: Educate staff on the risks and responsibilities associated with using AI systems.​

FAQ

What are the key differences between first-party and third-party AI risks?

First-party AI risks are associated with AI systems developed and managed internally, giving the organization more control over data and processes. Third-party AI risks involve external vendors, where the organization must rely on the vendor’s practices and compliance measures.

How can organizations mitigate third-party AI risks?

Organizations can mitigate third-party AI risks by conducting comprehensive vendor assessments, establishing clear contractual obligations regarding data privacy and compliance, and implementing continuous monitoring of the vendor’s performance and adherence to agreed-upon standards.​

Why is it important to distinguish between first-party and third-party AI risks?

Distinguishing between these risks allows organizations to tailor their risk management strategies appropriately. It ensures that internal controls are effectively applied to first-party AI systems, while appropriate oversight and governance are established for third-party AI solutions.​

How do you assess third-party AI vendor risk?

Assess through security questionnaires, SOC 2 reports, penetration test results, and compliance certifications. Review their AI governance practices, model documentation, and incident response capabilities. Evaluate financial stability and business continuity plans. Consider on-site audits for critical vendors. Ongoing monitoring is essential—initial assessment alone is insufficient.

What contractual provisions should cover third-party AI?

Key provisions include: data handling and security requirements, model performance guarantees, audit rights, incident notification timelines, liability allocation, indemnification clauses, IP ownership clarity, exit provisions with data return, and compliance with applicable AI regulations. Service level agreements should cover model accuracy and uptime.

How do you manage the transition from third-party to first-party AI?

Plan for knowledge transfer, data migration, and capability building. Document all third-party model behaviors and dependencies. Build internal expertise before transition. Test first-party alternatives extensively before cutover. Maintain fallback options during transition. Consider hybrid approaches where first-party handles core functions while third-party covers edge cases.

Summary

Effectively managing AI risks requires organizations to understand and differentiate between first-party and third-party AI risks.

By implementing structured risk assessment processes, conducting thorough vendor due diligence, and maintaining ongoing monitoring, organizations can mitigate potential risks and ensure compliance with regulatory standards.