Best practices for database masking techniques to protect sensitive data in test environments

kathleen_ops
Sun, 11 May 2025 01:24:49 +0000

Automation and version control for your masking rules is essential. We store all masking configurations in Git and treat them like infrastructure-as-code. Every masking rule change goes through code review and testing before being applied to test environments. We use Liquibase-style scripts that define masking transformations declaratively, which can be version-controlled and audited. This also enables us to have different masking profiles for different test environments - our integration test environment gets heavily masked data, while our performance test environment uses partially masked data to preserve realistic data volumes and distributions.