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

luis_king
Fri, 18 Apr 2025 08:30:00 +0000

I’d like to start a discussion around best practices for implementing database masking in SAP PLM 2022 test environments. With GDPR and increasing data privacy regulations, we can no longer simply copy production data to test/dev environments without proper masking.

Our challenge is balancing data privacy requirements with test data realism. We need masked data that maintains referential integrity, preserves data patterns for realistic testing, but completely protects sensitive information like employee names, supplier contacts, proprietary part specifications, and customer data.

We’re currently evaluating several masking techniques:

Substitution (replacing real values with fake but realistic data)

Shuffling (redistributing values within the same column)

Nulling (replacing sensitive fields with NULL)

Encryption (reversible masking for certain scenarios)

The complexity increases with SAP PLM’s interconnected data model - masking a supplier name in one table needs to be consistent across purchase orders, quality records, and audit trails. We also need to maintain data relationships for testing workflows like change management and approval processes.

What masking strategies have worked well for others in PLM environments? Particularly interested in approaches that maintain test data validity while ensuring compliance with data protection regulations.