Security and Compliance
A global biopharmaceutical company had already begun using AI but lacked a mature governance model, creating gaps in how projects were reviewed for bias, privacy, third-party risk and regulatory readiness at a time when AI-specific regulation was quickly emerging.
Reported impact
The biopharma company gained a more operational AI-governance model with clearer minimum requirements, stronger third-party oversight and better readiness for regulatory scrutiny, reducing the risk that AI projects would drift away from stated ethical principles.
EY used its global Responsible AI framework to evaluate and strengthen the biopharma company's AI-governance operating model. The engagement started with the client's own draft governance framework and responsible-AI principles, then overlaid EY's methodology through interviews, documentation reviews and project-level assessments. Multidisciplinary teams of digital ethicists, IT-risk practitioners, data scientists and subject-matter experts examined how AI risks were handled across the lifecycle-from problem identification through modeling, deployment and monitoring-and reviewed live projects such as forecasting, adverse-event tracking and early disease detection. The review exposed gaps in project-level controls and led to recommended changes including stronger third-party AI assessment and creation of a central AI inventory. The result was a more operational compliance structure the company could use to align teams and prepare for expanding regulation.
AI projects and supporting documentation are reviewed against the global Responsible AI framework, with findings feeding changes to the client's governance framework, project requirements and inventory controls. That governance loop then applies back to AI systems under development, deployment and monitoring across the organization.
"EY teams used the global responsible AI framework to help a biopharma optimize AI governance"
"The most pressing was the absence of an AI governance framework."
"These included improved third-party AI risk assessment and a new central AI inventory"
"allowing us to set minimum requirements for business teams working with AI"
"we also evaluated a sample of key AI projects, including forecasting, adverse event tracking and early disease detection"
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