Introduction
Data masking, also known as static data masking, is the process of permanently replacing sensitive data with fictitious yet realistic looking data. It helps you generate realistic and fully functional data with similar characteristics as the original data to replace sensitive or confidential information.
Even in non-production environments, you need to protect your sensitive data and stay compliant with data privacy regulations. The recommended solution is to mask your sensitive data before using it in non-production environments. This way, you minimize the sensitive data you have, and thus, reduce the risk and compliance boundary.
Data masking, also known as static data masking, is the process of permanently replacing sensitive data with fictitious yet realistic looking data. It helps you generate realistic and fully functional data with similar characteristics as the original data to replace sensitive or confidential information. Data masking limits sensitive data proliferation by anonymizing sensitive data while enabling you to use production-like data. It ensures that malicious actors cannot benefit from the fictitious data even if they gain access to it.
Data masking is ideal for virtually any situation when confidential or regulated data needs to be shared with non-production users. These users may include internal users, such as application developers or external business partners, such as offshore testing companies, suppliers, and customers. Data masking contrasts with encryption, which simply hides data, and the original data can be retrieved with the appropriate access or key. With data masking, the original sensitive data cannot be retrieved or accessed.
One of the key aspects of data masking is to replace sensitive information with fictitious data, without breaking the semantics and structure of the data. The masked data must be realistic and pass specific checks, such as Luhn validation. For example, a masked credit card number must not only be a valid credit card number, but also a valid Visa, Mastercard, American Express, or Discover card number. Failing to maintain this data integrity may break the corresponding application. The predefined masking formats ensure that the generated data passes common validation checks.
Common Data Masking Requirements
Organizations typically mask data using custom scripts or solutions. While these in-house solutions might work for a few columns, they do not work for large applications with distributed databases and thousands of columns. An enterprise data masking solution should be able to fulfill the following data masking requirements:
- Locate sensitive data in the midst of numerous applications, databases, and environments.
- Correctly mask sensitive data having different shapes and forms such as names, Social Security numbers, email addresses, credit card numbers (Mastercard, Visa, and so on), and blood type.
- Ensure that the masked data is irreversible, that is, one should not be able to retrieve the original data from the masked data.
- Ensure that the masked data is realistic enough to be useful for non-production purposes such as development and analytics.
- Ensure that the applications continue to work with the masked data.