Veysel Kocaman

De-identifying clinical data is essential for research and secondary use, yet current approaches are costly and require constant manual tuning as data distributions change. This session introduces an agentic approach to automating privacy-preserving pipelines on the Databricks Lakehouse.We present an Agentic De-Identification Framework that treats the pipeline itself as an autonomous agent, minimizing human effort while maximizing security and compliance.Attendees will learn how to use Agent Bricks to fit de-identification models and entity resolution logic to customer data; apply agentic de-identification across structured data, clinical notes, and imaging metadata using Mosaic AI Agent framework; and register core de-identification logic as governed Unity Catalog Functions.We conclude by showing how AgentOps is operationalized with MLflow tracing, Unity Catalog governance, and Mosaic AI Gateway Guardrails to deliver auditable, secure PHI protection at scale.