GoMask.ai
GoMask.ai is an AI-powered platform that transforms sensitive production data into safe, compliant test datasets through automated data masking and synthetic data generation while preserving data relationships.
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Product Information
Updated:Dec 19, 2025
What is GoMask.ai
GoMask.ai is an enterprise-grade solution designed to solve the critical challenge of test data management in software development. It offers both a user interface and code-based approach (GoMask as Code) to help development teams create realistic, compliant test datasets without compromising sensitive information. The platform was created to address the common problem where teams resort to using production data for testing due to the lack of efficient alternatives, which poses significant security and compliance risks.
Key Features of GoMask.ai
GoMask.ai is an AI-powered data masking and synthetic data generation platform that helps teams transform sensitive production data into safe, compliant test datasets. It offers both UI-based and code-based (YAML) approaches, integrates with Git workflows, and automatically detects and masks sensitive information while preserving data relationships. The platform significantly reduces database refresh times from days to minutes while maintaining compliance standards.
Git-Native Test Data Management: Allows teams to manage schema and masking rules in the same Git repository, providing version control and traceability for compliance purposes
AI-Powered Detection: Automatically identifies new PII fields and suggests appropriate masking patterns during development
Dual Interface Options: Offers both visual UI interface and YAML-based configuration (GoMask as Code) to accommodate different team preferences and workflows
CI/CD Integration: Seamlessly integrates with existing CI/CD pipelines and supports 50+ databases and cloud platforms
Use Cases of GoMask.ai
Financial Services Testing: Enable fintech companies to create compliant test datasets for loan application processing and credit risk modeling while protecting sensitive customer information
Healthcare Data Management: Transform clinical trial data and patient records into anonymized datasets for testing while maintaining HIPAA compliance
Educational System Development: Create safe test environments for student information systems while preserving data relationships for accurate testing
Pros
Significantly reduces test data refresh time from days to minutes
Ensures compliance through automated masking and audit trails
Flexible implementation options (UI or code-based) to suit different team needs
Cons
Requires initial setup and configuration of masking rules
May require team training to fully utilize both UI and code-based features
How to Use GoMask.ai
Create an Account: Sign up for a free GoMask.ai account through their website (gomask.ai)
Choose Implementation Method: Decide whether to use the visual UI interface or YAML-based code approach (GoMask as Code) based on your team's preferences
Define Data Rules: Either write masking and synthetic data rules in YAML files alongside your schema, or use the visual interface to define how sensitive data should be masked
Connect Data Sources: Integrate GoMask with your existing databases using their native connectors that support 50+ platforms
Version Control Integration: If using GoMask as Code, commit your YAML rules to Git alongside database changes for version control
CI/CD Setup: Integrate GoMask into your CI/CD pipeline to automatically apply masking rules during deployments
Review & Collaborate: Team members can review data rules through pull requests and collaborate on test data management
Generate Test Data: Use GoMask to automatically transform production data into safe, compliant test datasets within minutes
Monitor Compliance: Track all masking operations through Git history for audit purposes and compliance verification
GoMask.ai FAQs
GoMask.ai is an AI-powered data masking and synthetic data generation platform that helps teams transform sensitive production data into safe, compliant test datasets. It offers both UI-based and code-based (YAML) approaches for defining masking rules.
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