Blog
Third-party AI risk: A holistic approach to vendor assessment
Businesses are increasingly relying on third-party vendors to integrate AI systems into their operations. Learn how you can adapt your third-party risk management processes to account for AI-related risks
Marco Barone
Senior Counsel Data Privacy, CIPP/E, CIPP/US, CIPM, FIP
February 6, 2024
While AI is becoming a backbone of modern business operations for many organizations, fewer of them are creating the AI systems themselves. More often, organizations are relying on third-party vendors to integrate AI solutions into their daily operations.
This shift to third-party solutions necessitates a fundamental transformation in how companies assess and manage the risks associated with AI use in their organization.
In this article, we’ll dive into why it’s so imperative to adopt a holistic approach to vendor assessments, and explain how AI governance can play a pivotal role in mitigating risks and fostering responsible AI practices.
The paradigm shift in vendor assessment
Most companies have third-party risk management (TPRM) strategies in practice for other vendors, but these traditional TPRM workflows need to evolve to keep pace with the dynamic integration of AI.
We’ve seen how a siloed approach to more traditional dimensions of third-party risk (e.g. privacy, security, ethics, business continuity and resilience, etc.) doesn’t play well anymore for organizations. This holds true for AI adoption by third-party vendors as well, underscoring the need for organizations to redefine their approach to vendor assessments to be more holistic in order to account for the use of AI.
The technical tapestry: Assessing AI systems and components
Understanding the technical intricacies of AI systems deployed by vendors is the foundation of a robust assessment. Scrutinising the underlying technology reveals potential risks associated with AI components in third-party solutions, including aspects such as:
- Dataset attributes
AI systems require an excess of data, and you need to have a clear understanding of what attributes those datasets have. Your assessments should give you clarity into the data quality, training data sources, data ownership, data versioning and traceability, and so on.
- Model attributes
Once you have transparency into the datasets you’re going to be using, you need to gain similar clarify into the model itself. Is the model you’re using a foundational model? What learning method does it use? What biases may be present, and what is the demographic parity ratio? What is the autonomy level of the model, and how much human oversight is required?
This technical tapestry of dataset and model attributes forms the first layer of a comprehensive evaluation of your third-party AI system.
Though you may not be developing or providing the system yourself, as a deployer of a third-party AI system you’ll still have obligations and responsibilities regarding the data and models you use – so it’s important to have the answers to these questions well documented.
AI governance frameworks: Navigating compliance and requirements
AI governance practices are a critical component in responsible AI usage, and not just your own internal AI governance. It’s becoming more and more important for you to also evaluate your vendor’s AI governance framework; doing so will give you more insight into the compliance and legal and ethical dimensions of their AI practices.
Global frameworks, such as the EU AI Act, are increasingly introducing specific requirements on responsible actors, depending on their role, such as conformity assessments by high-risk AI system providers.
Though companies using third-party AI won’t fall into this provider category, organizations still need to be aware of their responsibilities as deployers of these systems and still must align with these frameworks to ensure compliance and responsible AI practices from their vendors.
Operationalizing a holistic assessment approach
The benefits of a holistic assessment approach extend beyond mere compliance. Avoiding legal and ethical pitfalls associated with AI systems used by third parties is crucial for building trust and maintaining transparency in the vendor ecosystem.
It may sound overwhelming to have to evaluate vendors in addition to your own AI governance responsibilities, but this process doesn’t have to start from scratch. Operationalizing AI governance assessments involves smaller, practical steps, such as integrating AI governance into existing TPRM workflows.
Tools like OneTrust's AI Governance and Third-Party Risk Management solutions support organizations in streamlining this process, ensuring a more comprehensive view of initiatives related to onboarding AI components across your business.
Managing third-party risk to build trust
As the landscape of third-party AI risks continues to evolve, organizations must embrace a holistic approach to vendor assessments.
Incorporating AI risk into these assessments is a strategic imperative and a proactive step towards third-party risk management practices and responsible AI usage.
Building a comprehensive framework not only supports mitigating risks associated with third-party AI adoption but also fosters an ecosystem of transparency, trust, and innovation.
Organizations must adapt their TPRM workflows to navigate the intricacies of AI, ensuring a future where AI is harnessed responsibly for sustainable business growth.
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