Test Data Masking for SQL Server
Remove costly compliance risks
Test Data Automation combines automated data profiling with easy-to-configure masking, providing masked data on-the-fly during manual testing, automation, and CI/CD.
You can’t choose between delivery speed and test data compliance
Eye-watering fines and new legislation have introduced greater risks for testers who still depend on production data copies. The dangers of exposing potentially-sensitive information to less-secure non-production environments has led many organisations to forbid this outdated practice. Yet, testing and developing at speed requires constant data refreshes, while removing sensitive data can be slow and complex. Many organisations rely on a complex mix of scripts and manual masking routines, which struggle to retain intricate relationships across data sources. Masking can in turn introduce lengthy delays to data provisioning, while more time is lost as tests fail due to broken and inconsistent test data. For both CI/CD and compliance, a rapid and simplified approach is needed for test data anonymisation.
Intuitive data masking for testing, test automation and CI/CD
Test Data Automation provides automated data profiling and easy-to-configure data masking, rapidly anonymising data to support quick and compliant development. Data scanning automatically finds and tags potentially-sensitive information across tables, using customizable regular expressions, seed lists, and column names. The tags provide a visual map of where sensitive data resides across databases, quickly selecting from global rules to mask every piece of tagged data consistently. Each configured mask then becomes reusable from a central catalogue, from which it can be executed using self-service forms. Parallel testers and developers can self-service the compliant data they need using intuitive drop-downs and inputs, while automation frameworks and CI/CD pipelines can likewise run masks on-the-fly.
Test Data Automation’s masking jobs run using high-performance, parallelized masking, reducing compliance risks while providing on demand test data. Configuring masking rules is likewise quick and simple, using intuitive screens to define and apply masking rules consistently across tables. Generating an optional audit file during masking further supports compliance, verifying that data has been masked successfully. Maintaining a catalogue of reusable data scanning and masking rules further strengthens auditing and accelerates masking, while Test Data Automation additional maintains audit logs of previous masks. Ensuring mandatory test data compliance no longer becomes a blocker to rapid development, anonymising and tracking data on-the-fly during testing, test automation and CI/CD.
Mask your test databases and files at speed
Watch this demo of Test Data Automation masking a SQL Server database, to discover how:
Test Data Automation scans databases to learn where personally and commercially sensitive information resides, using customizable regular expressions, seedlists and column names.
The automated data profiling tags sensitive data in columns and tables, creating a visual map of information to mask when ensuring compliance with data privacy policies.
Masking complex data does not create data provisioning bottlenecks, applying out-of-the-box and customizable data rules globally to mask all tagged data.
Editing and applying masking rules is as quick and simple as using intuitive forms and drop-down menus, replacing the time lost working with hard-to-maintain scripts and routines.
Each configured mask becomes reusable from a central catalogue, from which it can be triggered using self-service forms, automation frameworks, and CI/CD infrastructure.
Parallelised masking ensures performance as data is anonymized, while retaining data relationships and referential integrity to avoid time-consuming test failures caused by data.
An optional “before and after” masking report and in-tool reporting supports test data compliance, creating a clear audit trail and verifying that sensitive data has been removed.