Multi-Cloud Platform Databricks Access Control means defining who can do what, on which workspace, in which cloud, and making those rules unbreakable. Databricks runs on AWS, Azure, and Google Cloud. That means three sets of IAM policies, network rules, and native permissions—plus Databricks’ own role-based access control (RBAC) and cluster policies. Without a unified plan, complexity wins.
Start at the identity layer. Centralize authentication using a single identity provider (IdP). Map cloud roles to Databricks groups. Use service principals for automation and block password-based access entirely. Apply least privilege at the workspace level; no engineer should have more rights than needed, even for testing.
Then lock down data paths. In multi-cloud setups, storage lives in S3 buckets, Azure Blob, or GCS—each with its own ACLs and encryption settings. Align those directly with Databricks table ACLs and Unity Catalog permissions. A role that can run a job should not automatically read raw data unless it is required for that job.
Network control is next. Restrict Databricks access to private subnets. Use VPC peering or Private Link for each cloud provider. Block public IP access to clusters. Enforce firewall rules that cover ingress and egress between Databricks and external systems.