Data Security: Azure key Vault in Data bricks

Great article on securing secrets in Databricks! However, I'd like to suggest an even more secure approach: Azure Key Vault-backed secret scopes (https://learn.microsoft.com/en-us/azure/databricks/security/secrets/)

The current approach still requires storing service principal credentials (tenant ID, client ID, and client secret) somewhere in your code or environment variables, which introduces security risks:

• Client secrets in code = security vulnerability
• Environment variables = still exposed and manageable

A better solution: Azure Key Vault-backed secret scopes

With Azure Key Vault-backed secret scopes, you can:

1. Create a secret scope directly linked to your Azure Key Vault
2. Use Databricks' built-in integration with Azure Key Vault
3. Eliminate the need to store any credentials in your notebooks or environment

Usage:

# No credentials needed in code!
# Just reference the secret scope and key name
connection_string = dbutils.secrets.get(scope="my-keyvault-scope", key="db-connection-string")

Advanced Use secrets in Spark config and environment variables (https://learn.microsoft.com/en-us/azure/databricks/security/secrets/secrets-spark-conf-env-var):

You can also reference secrets directly in cluster configuration:

# Spark configuration property
spark.password {{secrets/my-keyvault-scope/db-password}}

# Environment variable
SPARKPASSWORD={{secrets/my-keyvault-scope/db-password}}

Then access them in your code:

# From Spark config
password = spark.conf.get("spark.password")

# From environment variable (in init scripts)
password = os.environ.get("SPARKPASSWORD")

Benefits:

• No service principal credentials in code or environment
• Databricks authenticates to Key Vault using managed identity
• Secrets are never exposed in notebook output
• Fine-grained access control via Databricks secret scope ACLs
• Secrets automatically redacted from logs
• Works seamlessly with cluster-scoped init scripts