WHAT IS DATA MASKING AND WHAT ARE ITS TYPES?

 Data masking is a set of methods used to prevent sensitive data from being exposed; it consists of systematized data-hiding techniques that allow developers and test specialists to use data in a controlled manner without revealing it. In data-masking practices, sensitive information is protected by using realistic data that resembles the original.

The purpose of data-masking methods is to safeguard data against unauthorized access, prevent data leaks, and protect it from malicious software. In regulatory compliance and audit processes related to data protection and storage, data masking becomes a natural part of operations. With data breaches increasing in both frequency and scale every year, data-masking techniques have become not merely a preference for businesses, but a necessity.

In Which Industries Is Data Masking Used?

Data masking is essentially a method that should be used in every industry where data privacy is required, and it is widely adopted. Examples of the main sectors where it is used include:

Finance & Banking: Customer identity information is among the most critical types of data that must be protected in the banking sector. At the same time, masking data in test and development environments is extremely important. Compliance with regulations such as BDDK, PCI-DSS, GDPR, and KVKK is mandatory.

Insurance: Personal data belonging to policyholders and health information must be masked. Data must also be protected during claims, risk, and actuarial analysis processes. Compliance with regulations such as KVKK and GDPR is required.

Healthcare: Individuals’ health information, patient records, diagnosis, and treatment details are highly sensitive data that must be masked. Data is anonymized or protected in clinical research and analytics. Along with healthcare-specific regulations such as HIPAA, compliance with KVKK and GDPR is also mandatory.

Telecommunications: Call records, subscription details, and location data are among the types of information that must be protected. Information is secured during big data processing and reporting activities. In the telecommunications sector, companies store very large volumes of data, and data masking methods are used while processing this sensitive information.

The Importance of Data Masking

In addition to strengthening companies’ cybersecurity operations, data-masking methods:

• Facilitate compliance with data protection and storage regulations such as KVKK and GDPR.
• Render data useless for cyberattacks while preserving its functionality and the consistency of the dataset for business purposes.
• Ensure data security in third-party applications and non-production environments.
• Enable testing and analysis without exposing the original data.
• Minimize potential data breaches by reducing fines, reputational damage, and operational disruptions.
• Work in integration with existing databases and application tools without compromising data structure or integrity.

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.