WHAT IS DATA MASKING AND WHAT ARE ITS TYPES?

Data masking is a systematic set of methods used by developers and test professionals to work with data in a controlled way without exposing it. In data masking methods, realistic data resembling the original values is used to protect sensitive information. There are basically four types of data masking.

Static Data Masking

Static data masking applies rules to transform sensitive information in a dataset. Masking rules are predefined, ensuring consistent application across multiple environments. The real data is irreversibly altered, so you need to be sure that the original information will not be needed later.

Dynamic Data Masking

Dynamic data masking modifies sensitive information in real time when users query or access the data, but it does not change the original information in the database. To implement this method, role-based access rules must be configured to define which data elements should be masked under what conditions.

Deterministic Data Masking

Deterministic data masking is a method in which a consistent match is maintained between the original data column and the masked values. The same input in the dataset corresponds to the same output value in a parallel way. In this method, the original value completely loses its meaning, but still allows for statistical analysis of the masked dataset as if no changes were made.

On-the-Fly Data Masking

On-the-fly data masking is a method that involves real-time modification of data as it moves between systems and processes. The critical point in this method is that it applies irreversible changes to the entire dataset. On-the-fly data masking dynamically applies masking techniques to block requests from different applications, hide information, and modify it.

Growing Market Share and Corporate Reputation

Data masking is a critical practice not only for legal compliance but also for protecting corporate reputation, customer trust, and system integrity. Regardless of whether static, dynamic, deterministic, or on-the-fly data masking is chosen according to the institution’s needs, the key point is to implement the chosen method in a way that integrates smoothly with the company's security architecture.

The data masking industry, valued at $18.43 billion in 2024, is expected to reach $71.75 billion by 2032. These figures once again demonstrate how extensive the sector is. We can foresee that institutions will be required to implement data masking systems both for regulatory compliance and for protecting their reputation and customers.