Use Cases and Top Techniques of Data Masking

Use Cases and Top Techniques of Data Masking

Data is a valuable asset in today’s world; however, with the increasing number of data breaches and cyber-attacks, it is crucial to protect sensitive information.

Each year, data breaches worldwide expose millions of people’s sensitive data causing many business organizations to lose millions. According to the report, Cost of a data breach 2022: A million-dollar race to detect and respond, by IBM,  the average data breach cost was $4.24 million in 2022. Among all the compromised data types, Personally Identifiable Information (PII) is the costliest data type. Data masking techniques are one of the most effective ways to protect data from breaches.

Data masking is hiding or obscuring sensitive data to protect it from unauthorized access. For example, a social security number can be replaced with a fake one that does not belong to anyone. Data masking aims to make sensitive data unreadable or unusable to people not authorized to view it.

Data masking techniques involve replacing sensitive data with a non-sensitive equivalent or a fake value that looks like the original data but does not contain the actual sensitive information. This process helps protect data privacy and prevent breaches, allowing authorized users to access and use the data for legitimate purposes.

Data masking is commonly used in industries that handle sensitive information, such as finance, healthcare, and government.

Data masking is an effective technique that organizations can use to protect sensitive data from unauthorized access. Here are a few of the use cases of data masking:

Use Cases of Data Masking

Protecting Data Privacy: Data masking can help protect data privacy by ensuring that sensitive data is not visible to unauthorized users. This is particularly essential in industries such as healthcare, where patient information must be protected by law.

Testing and Development: Organizations can use data masking to create safe and secure environments for testing and development. By masking sensitive data in test databases, organizations can ensure that developers and testers work with realistic data without risking sensitive information.

Compliance: Data masking can help organizations comply with regulations and standards such as PCI-DSS, HIPAA, and GDPR. By using suitable data masking techniques, organizations can ensure that they protect customer information per these regulations.

Outsourcing: When outsourcing work to third-party vendors, organizations can use data masking to ensure that sensitive data is not accessible to the vendor. Organizations can maintain control over the information by masking sensitive data while allowing the vendor to perform the necessary work.

Analytics and Reporting: Organizations can use data masking to protect sensitive data when conducting analytics and creating reports, to ensure that only authorized users can access the information, by masking sensitive data.

Data Masking Techniques

Here are various data masking techniques that can be used to safeguard sensitive data:


Tokenization uses non-sensitive equivalents to replace sensitive data. The non-sensitive equivalent is called a token. The token looks like the original data but does not contain sensitive information. For example, a credit card number can be replaced with a token, making the data useless to anyone who does not have access to the tokenization system. Tokenization is widely used in the payment industry to protect credit card information.


Encryption transforms data into a secret code that can only be deciphered with a key. Encryption ensures that sensitive data is not readable by unauthorized users. Encryption techniques include symmetric and asymmetric encryption. The same key is utilized for encrypting and decrypting data in symmetric encryption. In asymmetric encryption, two keys are used, one for encryption and another for decryption.

Data Subsetting

Data sub setting is creating a subset of the original data that contains only the necessary information. For example, a customer information database may have sensitive data, including social security numbers and credit card information. Data subsetting can create a subset of the database that only contains customer names, addresses, and phone numbers, making it less vulnerable to data breaches.


Anonymization data masking technique removes any identifying information from the data. Anonymization techniques include deleting personally identifiable information such as names, addresses, and social security numbers. Anonymization ensures that sensitive data is not linked to any individual, making it less vulnerable to data breaches.

Data Substitution

Data Substitution is known as the process of disguising data. In this process, data is replaced with another value. For preserving the data’s original look and feel, this is one of the most successful data masking techniques. The substitution technique can be used to protect a variety of data types from breaches. Disguising customer names using a random lookup file is an example of this data masking technique. It is an effective method of preventing data breaches, although this can be tough to implement.

Also Read: Robust Cyber Hygiene Practices Businesses Need to Follow


Salting is adding random data to the original data before encrypting it. The added data is called salt. The salt ensures that the same original data always produces different encrypted values, making it difficult for hackers to use brute force attacks to crack the encryption. Salting is commonly used in password hashing.

Although many database security solutions make plenty of sense, data masking is chief among them. This is because they are at the heart of an organization and contain a potential goldmine for employees and hackers willing to get malicious for turning a profit on the black market. Data masking is just one of the steps companies must take to avoid becoming the subject of class action lawsuits, negative press, and cautionary tales for years to come.

Data masking techniques protect sensitive data from data breaches and cyber-attacks. Tokenization, encryption, data subsetting, anonymization, data substitution, and salting are just a few data masking techniques available. Companies should assess their data protection needs and choose the appropriate data masking techniques to ensure the security of their sensitive information. CSOs in companies can include these data masking techniques in their data security strategies to effectively get away and avoid data breaches.

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