Vendor Assurance for AI and Cloud-Based GxP Systems: What You Can Leverage, Challenge, and Independently Verify

AI-enabled and cloud-based GxP systems increasingly depend on vendors for infrastructure, software development, security, data management, AI models, monitoring, and ongoing change. This creates a practical validation question: which vendor evidence can be leveraged, which claims must be challenged, and which controls or outcomes must be independently verified by the regulated company.

This recorded session presents a risk-based approach to vendor assurance that goes beyond collecting certificates and supplier questionnaires. It addresses how to evaluate vendor documentation, audit evidence, validation packages, cloud controls, AI lifecycle information, data governance, service-level commitments, and change-management practices. Particular attention is given to defining the regulated company's responsibilities under shared-responsibility models and to maintaining sufficient control over GxP-relevant data and decisions.

The traditional supplier-qualification approach is often not sufficient for modern cloud and AI-enabled GxP systems. Vendors may provide extensive evidence, but regulated companies remain accountable for determining whether that evidence is relevant, current, complete, and appropriate for the intended use. Participants will learn how to distinguish evidence that can reasonably be leveraged from evidence that requires challenge or independent verification.

The recording provides a practical framework for assessing vendor assurance without unnecessarily duplicating vendor activities. It also addresses common gaps such as unclear shared responsibilities, insufficient transparency of AI models and changes, weak data-governance controls, and over-reliance on certifications. The result is a more efficient, defensible, and inspection-ready approach to supplier and system assurance.

Code: FDB1331