Table of Contents

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  1. Preface
  2. Introduction to Test Data Management
  3. Test Data Manager
  4. Projects
  5. Policies
  6. Data Discovery
  7. Data Subset
  8. Data Masking
  9. Data Masking Techniques and Parameters
  10. Data Generation
  11. Data Generation Techniques and Parameters
  12. Data Sets
  13. Plans and Workflows
  14. Monitor
  15. Reports
  16. ilmcmd
  17. tdwcmd
  18. tdwquery
  19. Data Type Reference
  20. Data Type Reference for Test Data Warehouse
  21. Data Type Reference for Hadoop
  22. Glossary

Data Masking Overview

Data Masking Overview

Use data masking to replace source data in sensitive columns with realistic test data for nonproduction environments. When you create data masking rules, you define the logic to replace sensitive data. To configure the sensitive columns that you want to mask, assign data masking rules to source columns, data domains, and policies.
A policy defines the data masking rules, the data to mask, and the masking parameters for a source. When you assign data masking rules to policies, you can assign multiple source columns to the data masking rules. You can also assign a rule directly to a source column. You can assign data masking rules based on the data type of the source columns.
To implement data masking, create a data masking plan and generate a workflow from the plan. A data masking plan can contain policies and rules. In a data masking plan, select a rule that is assigned to a column. Policies and rules define how to mask sensitive and confidential data in a target database. A data masking plan contains at least one rule or one policy.
When you start the workflow, the PowerCenter Integration Service performs the masking operation.