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Enterprise Data Masking

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Product Overview

Enterprise Data Masking is designed to mask sensitive data fields in production data to create realistic test data. It is deployed on z/OS, often within an LPAR to isolate the masking process. The architecture includes components for defining masking rules, executing masking jobs, and generating reports.

It integrates with z/OS security systems such as RACF, ACF2, and Top Secret for authentication and access control. The tool accesses data sources like DB2, IMS, VSAM files, and sequential datasets. Key architectural considerations include ensuring sufficient processing power and storage for data transformation.

The system communicates between components using standard z/OS communication protocols. It may expose APIs (REST or other) for integration with test data management platforms. Alternatives include Informatica Data Masking and IBM InfoSphere Optim, but Enterprise Data Masking specializes in mainframe environments.

Common commands involve defining masking rules, executing masking jobs, and generating reports. Configuration files specify data sources, masking algorithms, and output formats.

Frequently Asked Questions

What does Enterprise Data Masking do?

Enterprise Data Masking is a tool designed to protect sensitive data by replacing it with realistic, but non-sensitive, substitutes. This allows organizations to create safe test environments using production data without exposing confidential information.

Is this a system, application, or tool?

It is a tool set that provides functionalities for masking data. It is not a complete system or application, but rather a set of utilities designed to integrate with existing data management processes.

What types of organizations use this?

Organizations across various industries, especially those handling sensitive customer or financial data, use this. This includes banking, healthcare, insurance, and government sectors. Any organization needing to create realistic test environments from production data while adhering to data privacy regulations can benefit.

When should we consider Enterprise Data Masking?

A company should consider using Enterprise Data Masking when it needs to create test data that accurately reflects production data, but without exposing sensitive information. This is particularly important when complying with data privacy regulations or when outsourcing testing activities.

What are the alternatives to Enterprise Data Masking?

Alternatives include Informatica Data Masking, IBM InfoSphere Optim Test Data Management, and Delphix. Enterprise Data Masking distinguishes itself through its focus on mainframe environments and its ability to handle complex data relationships within those systems.

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