Data Masking: Definition, Techniques, and Benefits for Industry

Data is the most valuable asset yet the greatest liability a company possesses. The need to share data with development teams, system testers (Quality Assurance, QA), and third parties often conflicts with the obligation to maintain privacy.

How do you provide access to real data for operational needs without compromising sensitive customer information? The answer lies in a data protection strategy called data masking.

What is Data Masking?

Data masking is a cybersecurity method designed to create a replica version of an organization’s actual data. This process changes the original data into a format that cannot be read or understood, while retaining its original structure.

The goal is to protect sensitive data such as Personally Identifiable Information (PII) or financial data from unwanted exposure. Although the values are altered, the data format remains authentic so that application systems and databases can process it without error.

In technical terms, this process is often referred to as data obfuscation. This technique ensures that in the event of a data breach or unauthorized access, the stolen information will have no value to cybercriminals.

Why Is Data Masking Important?

Many organizations mistakenly assume that the primary threat comes only from external hackers. In reality, data leakage risks often arise from poor corporate security architecture, and errors occur both intentionally and unintentionally.

Application developers and software testers often need production data to accurately test new features. Giving them original customer data is a hazardous practice that violates compliance standards.

Furthermore, insider threats from employees with excessive access are a strong reason why data masking is mandatory. With masking, employees only see data relevant to their tasks, not sensitive data in its entirety.

How Does Data Masking Work?

The data masking process works by replacing sensitive data with realistic fictitious data. Structural characteristics such as character length, date format, or data type are preserved to remain compatible with the application’s business logic.

Generally, the workflow begins with identifying and classifying sensitive data within your database. Without proper classification, you won’t know which columns actually need protection.

Read also: What is Data Classification in Security? Its Functions, Levels, and How it Works in the Enterprise

After identification, masking algorithms are applied based on predefined rules. The masked data can then be safely distributed to non-production environments (such as testing or development environments).

10 Data Masking Techniques

Selecting a masking technique depends heavily on the data type and usage requirements. Here are the 10 most commonly used techniques in the enterprise industry.

1. Static Data Masking (SDM)

SDM is typically applied to non-production databases. This process creates a copy of the production database, then masks that copy before storing it in the testing environment. Once altered, the data cannot be reverted to its original form, making it very safe to hand over to third-party teams.

2. Dynamic Data Masking (DDM)

Unlike SDM, DDM does not change the data stored in the physical database. Instead, this technique obfuscates data in real-time when a user requests access (query) to the database. The system checks user access rights. If the user lacks full authorization (e.g., a customer service agent), they will only see masked data. This is closely tied to strict access control implementation.

3. On-the-fly Data Masking

This technique occurs during the data transfer process from a production environment to another environment (e.g., during an ETL process). Data is masked in memory before being written to the destination disk. This is highly efficient for organizations practicing Continuous Integration.

4. Scrambling

The scrambling technique randomizes the order of characters or numbers within a data field. For example, employee ID “84521” is scrambled to “12548”. Although the format remains, the content becomes meaningless, though this technique can sometimes be reverse-engineered if the key is simple.

5. Substitution

This method replaces original data values with other values from a prepared list of fake data. For example, the name “Budi Santoso” is randomly replaced with “Ahmad Fauzi” from an anonymous lookup table. This keeps data integrity looking very natural.

6. Shuffling

Shuffling works by swapping values between rows in the same column. The original name from Row A is moved to Row B, and vice versa. The data presented is real data, but it is no longer connected to the correct data subject.

7. Variance

This technique is often used for numeric or financial data. The algorithm changes numbers by a certain variance percentage (e.g., +/- 10%) from the original value. Transaction dates or salary amounts will change, but statistical data trends are preserved for analytical purposes.

8. Nulling Out / Deletion

This is the simplest technique, where sensitive data is deleted and replaced with a NULL (empty) value. This method is effective for preventing access but less ideal for application testing, as it can break input validation logic.

9. Masking Out / Redaction

This technique displays only a small portion of the data and covers the rest with specific characters (usually ‘X’ or asterisks). The most common example is on ATM receipts or credit card displays, where only the last 4 digits are visible (e.g., XXXX-XXXX-XXXX-1234).

10. Tokenization

Tokenization replaces sensitive data with unique identification symbols called “tokens”. These tokens have no mathematical relationship to the original data, making it nearly impossible to revert without access to the token vault. This technique is very popular in payment security standards (PCI DSS).

Data Masking vs. Encryption: What’s the Difference?

Data masking is often conflated with encryption, yet both have distinct functions and mechanisms. Here is a deep comparison to help you determine the right strategy.

In short, data masking protects data from internal misuse without disrupting usage, while encryption protects data from unauthorized access. Both complement each other and should be used according to the context of needs.

Benefits of Implementing Data Masking

Adopting a data masking strategy is not just a technology trend, but a strategic necessity for modern enterprises. Here are the main benefits:

1. Protecting Data Privacy

The clearest benefit is protection against PII (Personally Identifiable Information). By hiding real identities, you drastically reduce the risk of data misuse by internal parties or third-party vendors accessing your system.

2. Regulatory Compliance

Regulations like UU PDP in Indonesia or GDPR in Europe demand Data Minimization principles. Data masking allows your company to fulfill this legal obligation by ensuring sensitive data is not exposed where it shouldn’t be, thus sparing the company from massive fines.

3. Optimizing Data Usage

Without masking, the application development process is often hindered by data access approval bureaucracy. With data masking, DevOps teams can work faster and more agilely using secure yet realistic data, accelerating product Time-to-Market without sacrificing security.

Tips for Choosing a Data Security Solution

Choosing tools or solutions for data management is no trivial matter. Consider the following points before deciding:

• Broad Integration
  Ensure the solution works across various database types (SQL, NoSQL) and cloud or on-premise applications.
• Automated Discovery Capabilities
  Look for tools that can automatically scan and detect sensitive data, as manual data logging is prone to oversight.
• Technique Flexibility
  The solution must support various masking techniques (static and dynamic) according to changing business needs.
• Clear Audit Trail
  The system must record who performed the masking and who accessed the data for compliance audit purposes.

Solution Recommendation: Access and Compliance Synergy

Effective data masking implementation cannot stand alone; it requires a strong access management foundation. This is where Adaptist Prime plays a vital role for enterprise-scale companies.

Adaptist Prime is not just identity management, but also ensures that “the right people get the right access.” Its Conditional Access feature allows you to enforce strict access policies, which is a key prerequisite before implementing Dynamic Data Masking.

With the support of Adaptist Prime, your company can build a digital ecosystem that is secure, time-efficient, and ready to grow without sacrificing data protection or user convenience.

FAQ

1. Does data masking affect application or database performance?

Implementing Dynamic Data Masking (DDM) can add slight latency because the data obfuscation process happens in real-time when a query occurs. However, Static Data Masking (SDM) does not burden production application performance at all because the process is done on a separate database copy (testing environment).

2. Can masked data be returned to its original form?

This depends on the technique used. Techniques like Static Data Masking are generally permanent and one-way (irreversible) for maximum security. Conversely, Tokenization or encryption that preserves format can be reversed (reversible), but requires a special decryption key and strict authorization through an identity management system like Adaptist Prime.

3. Is data masking the same as data anonymization?

Although similar, the goals differ. Anonymization aims to completely remove data links to individuals for public statistics and cannot be traced back. Data masking focuses more on maintaining data formats to remain functional for system testing (usability) while hiding original values.

4. Does the UU PDP mandate the use of data masking?

The UU PDP does not explicitly mention “masking,” but mandates data controllers to implement technical measures to protect personal data from unauthorized access. Data masking is the best technical implementation to fulfill the pseudonymization and data minimization principles mandated by regulation to reduce fine risks.

5. Can data masking be applied to unstructured data?

Yes, data masking can be applied to PDF documents, images, or Excel files; this term is often called Redaction. However, this process requires more advanced tools (often AI-based) compared to regular structured database masking to accurately detect sensitive data locations.