Databricks Access Control is the first line of defense. Databricks Data Masking is the second. Together, they decide who can see what, and how much of it they can see. Without both, you have gaps: gaps where sensitive fields slip through joins, gaps where test environments look too much like production, gaps where compliance breaks before you know it.
Access Control in Databricks is not just assigning roles. It’s about defining boundaries in notebooks, clusters, tables, and views. It’s fine‑grained. It’s powerful when enforced at the workspace and table level. Unity Catalog makes it cleaner—central policies, governed identities, secure table access. You can give data scientists read‑only access to masked views while letting analysts see only aggregated results. Every permission, every grant, is an intentional choice to limit blast radius.
Databricks Data Masking solves a different but connected problem: exposure. Data masking transforms sensitive values into safe, structured, and usable forms. Columns with names, emails, SSNs, account numbers—masked automatically, either statically in stored tables or dynamically during queries. You can keep production datasets available for development and testing without letting real personal data leak downstream. You protect privacy without breaking pipelines.