The Soflab G.A.L.L. application is designed to anonymize sensitive data in non-production environments, enabling the generation of high-quality synthetic data that remains consistent with real data and supports reliable testing. At the same time, it ensures full protection of sensitive information, effectively preventing data leaks.
Reduced data breach risk by replacing real data with artificial equivalents and detecting sensitive or erroneous records. Lower legal and financial exposure while protecting customer transactional data. Unified anonymization across non-production systems ensures a consistent data model and preserved production relationships. Synthetic data, generated from key production attributes, maintains statistical consistency for BI and AI. A central test data repository enables controlled reuse, lowers maintenance costs, accelerates deployments (up to 5 days), and supports simulation and reusable scenarios.