Hi @NW1000 ,

For Classic Compute, the best approach is using Databricks Container Services (DCS). This lets you package your exact OS, system libraries, and Python environment into a custom Docker image. You just push that image to a private registry and point your cluster to it. Your version-controlled Dockerfile then acts as the exact audit trail your regulators need. You can find the detailed guide for this here: https://learn.microsoft.com/en-us/azure/databricks/compute/custom-containers

For Serverless Compute, custom Docker images aren't supported because Databricks fully manages the underlying infrastructure. Instead, you replicate the environment by locking in a specific Databricks Runtime (DBR) version and using a strict requirements.txt file to pin your exact package versions. Storing those requirements and your cluster definitions (like JSON or Terraform) in Git gives you the verifiable history required for compliance.

Hope this points you in the right direction!