Implementing Least Privilege Access Control in Databricks

Databricks runs in shared, high-speed environments where users, clusters, notebooks, and jobs mix constantly. Every identity should have only the permissions it needs—nothing more. This is the principle of least privilege.

In Databricks, you implement least privilege by mapping each role to the exact operations it must perform. Use workspace access control lists (ACLs) to limit who can view, edit, or run notebooks. Bind cluster permissions so only approved users can attach to and execute on specific clusters. Set table ACLs in Unity Catalog to restrict queries to sensitive datasets. Assign job permissions to control who can trigger, edit, or delete scheduled workflows.

Combine fine-grained privileges with group-based access. Groups simplify management and reduce mistakes. When a new engineer joins, you drop them into the right group and they get precisely the permissions that group demands—no more, no less.

Least privilege is not a one-time setup. Review every role and permission regularly. Remove stale accounts. Audit permission grants. Check logs to confirm real-world use matches intended privilege. This keeps your Databricks access model clean, fast, and secure.

A well-enforced least privilege model in Databricks means:

• Reduced blast radius for accidents or compromises
• Minimal insider threat exposure
• Predictable and safe operational changes

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