AI Governance for Cloud Data Platforms: From Policy to Practice

As organizations deploy AI and analytics on modern cloud data platforms, governance often remains theoretical, documents exist, but execution breaks down in production. This session focuses on how to operationalize AI governance directly within cloud data platforms so that trust, compliance, and scalability are built into everyday workflows.

The session covers practical patterns for embedding governance into data pipelines and AI workflows, including data quality controls, lineage and metadata, access governance, model oversight, and audit-ready evidence. Attendees will learn why governance fails at scale, how to avoid common pitfalls, and how to align DataOps and MLOps practices to support reliable analytics and AI in the cloud.

This talk is framework-driven, vendor-neutral, and grounded in real-world challenges faced by teams running analytics and AI on cloud platforms.