Cross-Platform Referential Integrity with Deterministic Data Masking

Masking copies of production data is very common in large organizations for several use cases. First is using the data to feed their application development. Data analytics teams also need data to support mission-critical projects and deliverables. The key to data masking is for the data to replicate the format and appearance of real production data, but in a desensitized form.

Only data that replicates production data is effective when creating, modeling, and testing applications to understand operational performance before putting it into production.

Perhaps the most important element of masking is the ability for PK Protect to mask data deterministically. This capability replaces the discrete sensitive data elements with the same masked values, wherever that data element appears across different repositories in the organization. With this approach, you achieve:

• Unmatched accuracy
• Greater efficacy when running data analytics and application testing scenario

The referential integrity provided by deterministic masking also maintains the relationships between data elements, even after anonymizing sensitive values.

PKWARE also supports deterministic masking across numerous platform types, including:

• Oracle
• SQL Server
• Postgres
• DB2
• Hadoop
• AWS
• Azure
• Snowflake
• GCP
• Hadoop
• And more

This breadth of environments ensures security and compliance across your entire data landscape.

Here are some additional resources on data masking.

• Feature Overview
• Use Case: De-Identify Production Data
• Case Study: Customized Data Masking

If you have any questions about our solutions for data masking:

• Contact your representative or our Customer Success team if you’re a current customer.
• Request a demo of the PK Protect platform.