What is Data Masking?

1.1 What is data masking? 

Data masking is a security technique that produces a phony but accurate copy of your organization’s data. In more technical terms, data masking is the act of anonymization, pseudonymization, redaction, scrubbing, or de-identifying sensitive data. In other words, data masking makes characteristically authentic replicas of personally identifiable information or other highly sensitive data. An inauthentic copy of data maintains the attributes and integrity of the original production data and assists companies in minimizing data security concerns. Accordingly, the data masking objective is to secure sensitive data while offering a helpful substitute, as masked data can be used for analytics, training, or testing. 

1.2 Why is data masking so important?  

Here are several reasons data masking is essential for many organizations: 

1.3 Data masking types 

Many different methods of data masking are frequently employed to protect sensitive data. Let’s look at some of them.  

1.4. Data masking techniques 

Let’s go over some typical methods by which businesses conceal sensitive data. IT experts have access to a wide range of methods for data protection. 

Data Encryption 

Data encryption is a way of translating data from plaintext (unencrypted) to ciphertext (encrypted). Users can access encrypted data with encryption and decrypted data with a decryption key.  

Data Scrambling 

Data Scrambling is used to hide or delete sensitive data. Since this process is irreversible, it is impossible to reconstruct the original data from the scrambled data. Data scrambling is only possible during the cloning process. 

Nulling Out 

By assigning a null value to a data column, “nulling out” hides sensitive data so unauthorized users cannot see it.  

Value Variance 

A variance is applied to a number or date field. This approach is often used for masking financial and transaction value and date information.

Data Shuffling 

Data is mixed up using shuffle algorithms, which can also keep logical connections between columns. It shuffles data from a dataset inside an attribute at random. 


According to the GDPR (General Data Protection Regulation), pseudonymization is any technique that ensures that data cannot be used to identify a specific individual. Direct identifiers must be eliminated; ideally, any identifier combinations that c

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