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Security Policy

Supported Versions

Version Supported
1.0.x ✅

Reporting a Vulnerability

If you discover a security vulnerability, please report it by:

  1. Do not open a public GitHub issue
  2. Email security concerns to the maintainers
  3. Include details about the vulnerability
  4. Allow time for a fix before public disclosure

Security Considerations

This repository contains educational examples for governing data platforms and ML systems on Databricks. When using these examples:

  • Credentials: Never commit Databricks tokens, cloud credentials, or API keys to version control
  • Secret Scopes: Always use Databricks secret scopes (or Azure Key Vault / AWS Secrets Manager) rather than hardcoding secrets in notebooks or jobs
  • Permissions: Apply the principle of least privilege — grant only the permissions a user or service principal actually needs
  • Audit Logs: Enable Unity Catalog audit logging to track who accesses sensitive data
  • Network Security: Configure IP access lists and private link for production workspaces

Best Practices

  1. Use dbutils.secrets.get() for all credential retrieval in notebooks and jobs
  2. Rotate service principal credentials regularly and revoke unused tokens
  3. Review GRANT statements carefully — prefer granting to groups rather than individual users
  4. Keep Databricks runtime and cluster libraries updated
  5. Use Unity Catalog column masks and row filters for PII protection