100 Days of Data Engineering on Databricks Day 91: Governance and Security with Unity Catalog

Securing ML + GenAI Workflows on Databricks

As enterprises scale ML and GenAI applications built on SAP and Customer 360 data, data governance and security become mission-critical. Day 91 focuses on securing the full lifecycle of insights — from ingestion to GenAI generation — using Unity Catalog, the unified governance layer on Databricks.

As a Databricks Solution Architect, my job is to ensure the assistant we built in previous days (spanning SQL, ML, and GenAI outputs) is:

• Secure against unauthorized access
• Auditable for every model and data change
• Compliant with enterprise policies

Today, we cover how Unity Catalog secures structured data, ML models, GenAI prompts, and dashboard access — and how to implement role-based access and lineage at scale.

Why Unity Catalog Matters

Unity Catalog is not just a governance layer — it’s the backbone of secure and governed AI development on Databricks. It…