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. Working with Test Data Warehouse
  13. Analyzing Test Data with Data Coverage
  14. Plans and Workflows
  15. Monitor
  16. Reports
  17. ilmcmd
  18. tdwcmd
  19. tdwquery
  20. Data Type Reference
  21. Data Type Reference for Test Data Warehouse
  22. Data Type Reference for Hadoop
  23. Glossary

Policies Overview

Policies Overview

A policy is a data masking component that describes the methods to maintain the privacy of specific types of source data.
A policy contains data domains. A data domain describes the functional meaning of a column based on the column data or the column name. For example, a Social_Security data domain contains all database columns with numbers in the following format: 999-99-9999. A Salary data domain might include the Salary, Bonus, and Commission columns in a database.
A data domain contains data masking rules. A data masking rule is a data masking technique to mask a specific type of data. For example, you might configure the Substitution data masking technique for First Names and Last Name columns. You configure two Substitution masking rules because each rule contains different parameters.
You can configure data domains, rules, and policies separately. Apply the rules to data domains and add the data domains to a policy. After you define the policy, you can assign the policy to a data source in a project. You can apply a policy to multiple projects.