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Leading U.S. Cancer Research and Treatment Center Builds their AI Governance Program
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Like many organizations, they has been building its AI governance framework as requests for new AI enabled vendors and internally developed systems and use-cases accelerated. Although the organization had established a governance committee and begun creating policies, its early reviews depended heavily on manual, ad hoc processes involving subjective analysis and professional judgment. The team needed a repeatable process that could scale without slowing responsible innovation – all in accordance with approved internal policies.
External regulations (such as the EU AI Act and Colorado AI Act) offered a starting point, but they needed a healthcare-specific standard that incorporated the Joint Commission-sponsored Coalition for Health AI (CHAI) guidance into an internal framework covering governance, privacy, security, monitoring, safetyevent reporting, risk and bias, and training.
Solutions
This leading health system and cancer research insitute created a vendor evaluation committee spanning legal, compliance, privacy, procurement, Infosec, HR, and AI stakeholders. During reviews, Radar AI Risk now helps the team document and inventory both internally-developed and vendor-delivered AI systems – helping them identify missing information, ask targeted follow-up questions, and assess the proposed system against both external legislation and internal policies. They also aligned its intake form with the platform ’ s initial assessment questions, reducing duplication and creating a more connected workflow.
With Radar AI Risk’ s Custom Policies features, the organization imported its internal policies along with the CHAI framework. AI Risk then created a configurable rubric indicating what criteria an AI system would be evaluated against to determine its level of risk. The team is refining the rubric by use case and developing a tiered model: a fast path for clearly high-value, low-risk tools; a lighter review for medium-risk uses; and a full review for systems closer to clinical, employment, or other consequential decisions. Radar AI Risk supports the analysis; the committee remains in control.
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Healthcare and research institution implements Radar AI Risk for a consistent way to evaluate third-party and internally developed AI systems.
Executive summary
As AI use expanded across the organization, a healthcare and research institution needed a consistent way to evaluate third-party and internally developed AI systems. The organization incorporated Radar AI Risk into its cross-functional review process to guide assessments, classify risk, document evaluated systems, and support defensible decisions, while keeping the final determination with its human governance committee.
“Radar AI Risk has become part of our vendor evaluation process. It helps us assess risk, ask the appropriate questions, and determine whether a tool should be approved.”
- Deputy Compliance Officer and Chief Privacy Officer
The Results
Radar AI Risk is now a critical part of their internal AI evaluation process, helping the organization create a consistent, auditable record of reviewed systems and demonstrate the rationale behind each decision. That documentation supports its AI inventory and gives stakeholders greater confidence that risk is being classified systematically and consistently. The platform has supported reviews of both external vendors and internally developed tools.
The team also found an unexpected use case: applying Radar AI Risk across vendors in an enterprise contract management RFP, making AI risk a structured input into a broader procurement decision.
“When we get a new use case, our senior program manager, Jess, will reach out and say, “Can you run a Radar on this?”. It has become a verb at our company.”
- Deputy Compliance Officer and Chief Privacy Officer
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