Data Masking Software: Privacy without losing data utility
In a 30-minute technical session, we validate your architecture and demonstrate how Gigantics applies masking rules to your current schemas. Get functional datasets with guaranteed referential integrity and comply with GDPR, NIS2, and DORA by eliminating the use of real data in all your non-production environments.
In this technical validation, you will be able to verify how:
- Execute consistent masking: Maintain referential integrity and functional consistency between tables in an automated way.
- Provision environments: Deliver secure datasets with predictable response times and standardized processes.
- Establish rule governance: Control who modifies policies, when and why, maintaining full traceability.
- Mitigate operational risk: Eliminate the use of production copies in non-production environments and centralize provisioning.
- Ensure interoperability: Connect with On-Prem, Cloud, and Hybrid architectures without requiring structural changes.
- Generate compliance evidence: Obtain technical documentation ready for cybersecurity audits and legal reviews.
Schedule your 30-minute technical validation
Real impact on your operations
Up to 10× more visibility and control
Over sensitive data in non-production environments (Dev/QA/UAT/Analytics).
2× more operational efficiency
In the delivery of protected and usable datasets; less manual work, more repeatability.
Tangible ROI from the first sprint
Fewer rework cycles due to inconsistent data, less friction with Security/Legal, and less time provisioning data.
How does Gigantics work?
Connectivity and PII discovery
Connect sources in Cloud, On-Prem, or Hybrid architectures. Compatibility with Oracle, SQL Server, Snowflake, PostgreSQL, etc., analyzing the schema to map risk and sensitive fields.
Centralized rule definition
Define and apply anonymization policies, synthetic data generation, masking, and other operations. Adapt transformations to each use case, target system, and regulatory requirements.
Transformation with cross-referential integrity
Generate protected datasets maintaining intact relationships between systems. A change in `Customer_ID` is replicated across all related systems.
Automated provisioning (Data-as-a-Service)
Automate delivery in CI/CD pipelines with predictable timelines, reducing bottlenecks and accelerating releases.
Use cases
Dev / QA / UAT
Protected data provisioning for CI/CD and regression testing. Eliminate false positives from inconsistent data.
Modernization / migrations
Validate integrity in Cloud migrations with secure comparisons between source and destination without exposing production.
Analytics and data platforms
Secure democratization: protected datasets for Data Science/BI complying with privacy without degrading analytical quality.
Rules are managed centrally with role-based permissions and change traceability (what was changed, when, and by whom).
Records of applied rules, configurations, execution, and resulting datasets are kept, facilitating reviews with Security/Compliance.
Yes. The approach is to integrate into your current architecture, connecting to your sources and environments without re-architecture.
Designed to be lightweight and scalable. It optimizes transformations to not impact maintenance windows or add overhead to DBA/SRE teams.
The appropriate technique is selected based on the use case and the internal/regulatory requirement. Not every case requires the same approach.
Gigantics makes compliance operational. It implements technical controls for data minimization and the traceability required by DORA and NIS2.
Just context: source types, target environments, priority use case, and restrictions (security, access, deadlines).