How do you secure data in Databricks?

Securing data in Databricks involves multiple layers. First, workspace and cluster security: enable Azure Active Directory or AWS IAM integration, enforce role‑based access control (RBAC) so only authorized users can create or attach to clusters. Second, data access: use Unity Catalog to manage tables, views, and files with fine‑grained permissions (GRANT/REVOKE). Unity Catalog also supports column‑level security and row‑level filtering. Third, encryption: data at rest is encrypted by default using platform‑managed keys; you can also provide customer‑managed keys via Azure Key Vault or AWS KMS. Data in transit is protected with TLS. Fourth, secrets management: store passwords, tokens, and certificates in Databricks Secrets or external vaults, and reference them in notebooks via the secrets API. Finally, audit and monitoring: enable audit logs, use Databricks’ native monitoring or integrate with SIEM tools. By combining RBAC, Unity Catalog permissions, encryption, secrets, and logging, you create a robust security posture for Databricks workloads.