As a DBA (Database Administrator), one of your main responsibilities is to ensure that sensitive data is properly protected while maintaining the efficiency and accessibility of databases. Data Masking is an essential tool for achieving this balance, allowing teams to work with realistic data without compromising privacy or security.

In this article, we explore the main Data Masking techniques you should know so you can select the best solution according to the needs of your environment.

What is Data Masking?

Data Masking is the process of replacing sensitive data with altered or fictitious versions while maintaining the structure and format of the original data. This allows you to work with realistic test data that is sufficient for necessary testing, without exposing confidential information.

Main Data Masking Techniques:

1. Static Data Masking (SDM)

Static Data Masking creates a static copy of the masked data that can be used in testing or development environments. This technique is ideal when the data does not change constantly and real-time access to the masked data is not required.

• When to use it: It is useful when masked data does not need to reflect changes in the original database, such as in load testing or historical data analysis.


• Advantages: Provides a realistic data set without the risk of exposure, but the data is not dynamically updated.

2. Dynamic Data Masking (DDM)

DDM allows data to be masked in real time based on user access or policies defined in the database. This technique is ideal when users with different permission levels need access to different versions of the data, depending on their roles.

• When to use it: Ideal for production environments where access to real data is needed without compromising user privacy.


• Advantages: Allows users with the appropriate permissions to view full data, while others can only access the masked data.

3. On-the-Fly Data Masking

On-the-Fly Data Masking masks data while it is being queried or transferred between systems, without the need to store it in a masked copy. This technique is useful when handling moving data, such as in data migration or system integration processes.

• When to use it: Useful for securing data during the transfer process or in environments where data changes rapidly. datos cambian rápidamente.


• Advantages: Does not require additional storage of masked data, optimizing resources and making implementation easier in distributed systems.

4. Data Substitution

Data Substitution replaces sensitive data with completely different values but maintains consistency with the original type and format. For example, a real phone number is replaced with another number that has the same length and format but is not assigned to a real person.

• When to use it: Ideal for testing environments where maintaining a direct relationship with the original data is not necessary, but the format must remain the same.


• Advantages: Provides an additional layer of protection since the masked data cannot be reversed or used.

5. Format-Preserving Masking (Enmascarado que mantiene el formato)

This technique ensures that the format and length of the data are maintained while being masked. For example, a credit card number is masked while maintaining its length and format, but with unreal values.

• When to use it: Useful in systems that require the format of the data to be preserved, such as masking credit card numbers, postal codes, email addresses, and others.


• Advantages: Allows the system to continue functioning as expected without altering the structure of the data.

In conclusion, Data Masking is a vital tool for DBAs who seek to protect sensitive data in development and testing environments without compromising database performance or operability.

Knowing the different masking techniques, such as Static Data Masking, Dynamic Data Masking, On-the-Fly Masking, Data Substitution, and Format-Preserving Masking, will allow you to select the most suitable solution according to your organization's needs and privacy requirements.