Effective AI governance begins with trusting the AI products you're considering using, as well as continuously validating models already in use for risks. This can be challenging enough with your own models, but it becomes significantly more complex with third-party AI systems, including foundational GenAI models, and Agentic AI. Here are three ways you can manage your third-party AI vendors through a purpose-built AI governance strategy.
Understanding exactly how vendor AI solutions integrate with your business is the foundation of trust. A comprehensive vendor governance approach strengthens this understanding by:
This approach helps establish transparent ownership boundaries while ensuring thorough governance and risk mitigation. It also helps risk leaders understand their organization's isk exposure across their AI vendors instead of investments, giving you the visibility and structure needed to understand not just what AI solutions you've purchased, but exactly how they're being deployed throughout your organization.
Knowing which questions to ask vendors is a critical first step for assessing third-party AI models. Using AI-specific questionnaires uncovers essential information about your vendors' capabilities. These targeted questions should align with your centralized policies and controls, addressing key areas of responsible AI development, bias mitigation strategies, governance frameworks, and model validation processes.
A unified governance approach that treats internal and external AI systems equally is crucial, by applying the same methodology to both, you create a single, consistent standard across your entire AI ecosystem.
This integrated approach transforms uncertainty into confidence in your vendor evaluation process, allowing you to precisely assess all AI governance practices while ensuring alignment with your organization's privacy, security, and data management protocols. By maintaining a single set of evaluation criteria for both internal and third-party AI solutions, you eliminate governance silos and ensure consistent risk management. This coordinated strategy enables you to more effectively compare capabilities, identify compliance gaps, and make informed decisions that address the full spectrum of risks—regardless of whether the AI was developed in-house or sourced from vendors.
Governing third-party AI providers presents several key challenges:
To overcome these challenges it’s helpful to implement pre-mapped controls that focus specifically on your internal use cases and projects utilizing foundational models.
Pre-mapped controls eliminate redundant documentation and streamline risk management through control inheritance. This approach creates transparency about which teams are responsible for specific aspects of AI governance while reducing duplicative work. When vendors update their controls or documentation, the inheritance model automatically flows these changes to all associated projects—reducing manual effort and ensuring consistency. This transforms uncertainty into an auditable process that balances innovation with accountability, allowing organizations to efficiently track and govern their AI systems.
To learn more, see AI governance for foundation models and generative AI and Top 5 governance considerations for Agentic AI.
Regardless of whether AI is developed in-house or purchased from a third party, the inherent risks associated with its intended use, and consequently, your governance framework, should be consistently proportionate and applied to all AI projects and systems.
Your AI governance program should encompass the following key elements:
While it might be tempting to think these points apply only to internally developed models, this is a big misconception. We have meticulously developed a range of capabilities designed to thoughtfully integrate all vendor AI risks within a robust, enterprise-wide governance program.
For organizations overwhelmed by AI vendors and third-party risk management, Monitaur's vendor governance capabilities within its enterprise Govern package streamline management while establishing the clear ownership boundaries missing in traditional approaches.
We've already helped an insurance company that was hesitant to adopt AI due to visibility concerns. Our team helped their leadership gain confidence in their AI implementation and accelerated their journey from design to delivery in just 90 days. As a result, they tripled their AI projects within six months. A key factor in this success was their use of our targeted third-party questionnaires to evaluate vendor AI capabilities and the seamless integration of vendor management with their existing implementation processes.
Effective AI governance begins with trusting the AI products you're evaluating and continuously validating models already in use. By implementing the three approaches outlined above —mapping AI solution usage throughout your organization, asking targeted questions during vendor evaluation, streamlining third-party governance with pre-mapped controls, and ensuring regulatory alignment—you can build confidence in your AI vendor relationships.
If your organization struggles to reduce risks or establish trust with third-party AI providers, reach out to us. We'll demonstrate how Monitaur helps you maintain continuous oversight of AI vendors, and ensure successful AI system implementation across your business.
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