Product articles Redgate Test Data Manager

Detecting Data Masking Gaps in a CI Pipeline

If you use Redgate Test Data Manager to anonymize production-derived test data and Flyway to manage schema changes, you need a way to keep masking rules aligned with schema evolution. The most reliable approach is to validate masking coverage in CI: run a masking drift check as part of the Flyway pipeline and fail the build if schema changes introduce sensitive columns that aren’t yet covered by the masking configuration. This article shows how the check works, what it validates, and where it can fit into a typical delivery workflow. Read more

Test Data Management and SOC 2 Compliance

Using live data outside production is one of the fastest ways to create compliance risk, because it quickly becomes harder to control who can access it, how it is handled, and how long it is kept. A Test Data Management (TDM) approach provides exactly the kind of controls SOC 2 auditors look for in this situation: an automated, traceable end-to-end process for protecting, provisioning, and removing customer data so it can be used safely in non-production environments. Read more

Reducing Database Complexity and Size using Subsetting

Getting realistic test data from large production databases can be a challenge, especially when you're trying to keep dev environments lightweight and secure. Redgate Test Data Manager includes a subsetting CLI that simplifies this by letting you generate smaller, fully representative subsets automatically. This article will walk you through getting started: from setup and configuration to running and refining simple subset operations. Read more

Data Masking in Practice

This article takes a strategic look at common data masking and anonymization techniques, and the challenges inherent in protecting certain types of sensitive and personal data, while ensuring that it still looks like the real data, and retains its referential integrity, and distribution characteristics. It also explains, briefly, with references, the tools that one can use to mask different types of data and how to provision development and test machines with these 'de-sensitized' databases, or alternatively to produce fake data that looks like the real thing, but in fact is generated randomly. Read more