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:
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Risk assessment: Evaluate the potential risks associated with both first-party and third-party AI systems.
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Vendor due diligence: Conduct thorough assessments of third-party AI providers, including their data privacy policies and compliance records.
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Continuous monitoring: Regularly review and update risk management strategies to address evolving AI technologies and associated risks.
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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.
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.
