Compare IRI FieldShield vs. Soflab G.A.L.L. in 2026

Soflab G.A.L.L.

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Description

IRI FieldShield® is a powerful and affordable data discovery and de-identification package for masking PII, PHI, PAN and other sensitive data in structured and semi-structured sources. Front-ended in a free Eclipse-based design environment, FieldShield jobs classify, profile, scan, and de-identify data at rest (static masking). Use the FieldShield SDK or proxy-based application to secure data in motion (dynamic data masking). The usual method for masking RDB and other flat files (CSV, Excel, LDIF, COBOL, etc.) is to classify it centrally, search for it globally, and automatically mask it in a consistent way using encryption, pseudonymization, redaction or other functions to preserve realism and referential integrity in production or test environments. Use FieldShield to make test data, nullify breaches, or comply with GDPR. HIPAA. PCI, PDPA, PCI-DSS and other laws. Audit through machine- and human-readable search reports, job logs and re-ID risks scores. Optionally mask data when you map it; FieldShield functions can also run in IRI Voracity ETL and federation, migration, replication, subsetting, and analytic jobs. To mask DB clones run FieldShield in Windocks, Actifio or Commvault. Call it from CI/CD pipelines and apps.

Description

The Soflab G.A.L.L. application aims to anonymize sensitive information in non-production settings, facilitating the creation of high-quality synthetic data that mirrors real datasets, thus enabling effective testing processes. As it safeguards sensitive details, the application effectively mitigates the risk of data leaks. By substituting genuine data with artificial counterparts, it reduces the potential for data breaches while identifying sensitive or erroneous entries. This results in decreased legal and financial risks while ensuring the protection of customer transactional data. The application promotes a unified approach to anonymization across various non-production systems, thus maintaining a consistent data model and preserving connections with production data. Additionally, synthetic data generated from essential production attributes retains statistical integrity for business intelligence and artificial intelligence applications. A centralized test data repository allows for controlled data reuse, which not only lowers maintenance expenses and accelerates deployment timelines—up to five days—but also facilitates simulation and reusable scenarios effectively. Overall, the application enhances testing efficiency while prioritizing data security.