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: 

• – It eliminates several serious vulnerabilities, including data exfiltration, insider threats, compromised accounts, and unsecured external-system interactions. 
• – It reduces the risks associated with migrating to the cloud. 

• – It retains many functional characteristics that make data helpful while rendering it useless to attackers. 
• – It allows the sharing of data with authorized people without exposing production data. 

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.  

• • – Static Data masking (SDM): replacing sensitive data with a plausible fictional substitute to prevent unauthorized disclosure. SDM is a vital data protection layer that can cover an organization’s expansive range of data. 

• – Deterministic Data Masking: repeatably replacing a value in a column with the same value across instances, servers, and database types, as well as in the same row, table, database, or schema.  
• – On-the-Fly Data Masking: this is employed when specific development-related requirements call for data to be masked without a staging environment, such as when there isn’t enough space or there has to be real-time data transfer. 
• – Dynamic Data Masking (DDM): DDM modifies the data stream without physically altering the original production data to prevent the data requester from accessing the sensitive data. 

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. 

Pseudonymization 

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