De-Identify Dev/Test Data
PK Protect: secure and compliant data usage
Minimize risk and exposure with secure, compliant data for testing and development.
Trusted by leading organizations for over 40 years
De-identify production data for safe development
PK Protect keeps sensitive data secure while maintaining its usability for critical business needs like development and testing. Use it to de-identify data to provide developers with safe datasets.
Why PK Protect
Data security for lower-level environments
Reduced exposure risk
Data masking transforms sensitive alphanumeric characters into randomized values that retain the original format. The data appears normal but is sanitized. Masking reduces risk by ensuring testing is only performed on anonymized data. Even if data is compromised, it remains protected.
Compliant dev/test data
By concealing sensitive information, masking ensures regulatory requirements are met. It allows Test Data Management teams to work with the data they need without risking compliance violations.
Secure, realistic datasets
PK Protect safeguards data in lower-level environments without compromising operational needs. It maintains relationships between data elements to ensure realistic datasets for development and testing. This preserves data utility while eliminating exposure.
Test data masking for compliance
After seeing PKWARE used for test data masking at two different Fortune 100 companies, I can confidently say that it works well for meeting compliance requirements.
What you get
Comprehensive data governance
Enterprise-wide data de-identification
PK Protect can de-identify sensitive data across hundreds of platforms. Customers leverage it to secure data on Oracle, SQL Server, Postgres, DB2, Hadoop, AWS, Azure, Snowflake, Salesforce, and more.
Centralized policies for consistent protection
With PK Protect, you can enforce policies uniformly across all repositories to de-identify data. Simplify governance and administration with centralized control.
Referential integrity in test environments
Even after sensitive data is anonymized with PK Protect, relationships between records remain intact. This ensures development and QA datasets behave as they would in production for realistic, secure testing.
