The term "data masking" refers to a technique for protecting sensitive data by replacing it with realistic but fictitious values or by obscuring it. The aim of data masking is to enable the use of personal or confidential information in testing, development, or analytics environments without exposing real identities or business data. This is particularly relevant in the context of data privacy regulations such as the GDPR.
Static Data Masking: Irreversibly altering sensitive data in non-production environments, e.g., by overwriting with placeholders or dummy values.
Dynamic Data Masking: Real-time masking of sensitive data at the point of access for specific users or roles, without altering the original data in the database.
Masking Rules and Policies: Defining which data fields should be masked and how—e.g., name, account number, social security number.
Role-Based Access: Varying masking levels depending on user roles (e.g., developer, tester, analyst).
Logging and Auditing: Traceability of masking activities and access to sensitive data.
Format-Preserving Masking: Masking sensitive data while maintaining the original format (e.g., phone number structure).
Integration with Test Data Management: Combination with tools for test data generation and provisioning in development environments.
Customer names and email addresses in a test database are replaced with randomly generated, but format-compliant, values.
A developer sees only masked credit card numbers in a database query tool due to access restrictions.
A company generates a test data set for a new CRM system in which all sensitive fields are anonymized while retaining their structure.
Salaries are dynamically obscured in a reporting solution based on the user's role.