Data Masking Procedure

1. Introduction

This document outlines the procedure for data masking to protect sensitive information while maintaining data usability.

2. Purpose

The purpose of this procedure is to ensure that sensitive data is adequately protected from unauthorized access while still allowing for necessary data analysis and processing.

3. Scope

This procedure applies to all employees and contractors who handle sensitive data within the organization.

4. Definitions

• Data Masking: The process of obscuring specific data within a database to protect it.

• Sensitive Data: Any information that must be protected from unauthorized access due to its confidential nature.

5. Procedure

5.1 Identify Sensitive Data

Identify the types of sensitive data that require masking, such as:

• Personal Identifiable Information (PII)

• Financial Information

• Health Records

5.2 Choose Masking Techniques

Select appropriate data masking techniques based on the data type and use case. Examples include:

• Substitution: Replacing sensitive data with fictitious data.

• Shuffling: Randomly rearranging data within the same column.

• Nulling: Replacing sensitive data with null values.

5.3 Implement Data Masking

Implement the chosen data masking techniques in the database or application.

5.4 Validate Masked Data

Ensure that the masked data meets the required standards and that sensitive information is adequately protected.

6. Best Practices

Follow these best practices for effective data masking:

• Regularly review and update masking procedures.

• Train employees on data protection policies.

• Conduct audits to ensure compliance with data masking standards.

7. Use Cases

Data masking is commonly used in:

• Software development and testing environments.

• Data analytics and reporting.

• Compliance with data protection regulations.

8. Industry Applications

Industries that benefit from data masking include:

• Healthcare

• Finance

• Retail