The technique of data masking provides an organization with a lot of flexibility with how its data can be utilized. Data masking basically switches the authentic, sensitive business data in documents with dummy values that make little sense to allow those with restricted access to still view the remaining contents of the document.

As Mordor Intelligence highlights, organizations exceedingly prefer this method, and the market for data masking is predicted to grow at a 13.69% CAGR between 2021 and 2026.

Let’s understand data masking in more detail.

What is Data Masking?

Data masking is a method that obscures, scrambles, or completely jumbles up certain portions of a dataset such that they cannot be comprehended or deciphered by the readers. This technique is adopted predominantly to restrain the breach of sensitive information within an organization.

Essentially, data masking helps keep the business data functional and circulation-friendly by obscuring selective information that the administration deems available only to authorized users. This technique is being increasingly deployed in training, testing, and demonstration drives at organizations. Today, data masking is so secure that it cannot be reverse-engineered – ensuring the absolute safety of restricted information.

Reasons to Practice Data Masking

Data masking provides a great way to restrict data access; it also has certain other benefits, as mentioned below:

  • Data masking is a great barrier that checks for data exfiltration, snooping, and information compromise.
  • It is a great option to ensure data security when Clouds are involved.
  • In case a breach does happen, the leaked/compromised data would be unusable to the attacker due to the dummy values contained in it by masking.
  • It gives organizations control over data sharing and exposure.
  • It works better than data deletion – a process in which data can be recovered – because data masking obscures the information without reversal.

Types of Data Masking

Data masking can be accomplished using various techniques. Let’s see what some of these are.

Static Data Masking

The process of static data masking involves creating a clone of the existing database to create a sanitized version that can be shared. It works by first creating a copy of the database and then deleting the unnecessary information that need not be shared. The masking process is performed, and this newly sanitized database is then sent to the destination.

Deterministic Data Masking

This technique involves determining the same types of datasets and using the same dummy value across all locations for this identified dataset. For example, for masking the name “Jill Johnson,” the technique will employ using the dummy name “Jane Doe” everywhere “Jill Johnson” appears. This method isn’t very secure, though.

On-the-Fly Data Masking

This method of data masking essentially deals with smaller pockets of data. It is an “as and when required” approach. Data that must be circulated is masked right then and there before being broadcast to the target location. This is especially helpful where there is no time to create database backups first – like in software deployments.

Dynamic Data Masking

This technique is the same as on-the-fly masking; however, while on-the-fly stores a database copy on a production base, dynamic data masking does not. The information is constantly streamed across systems.

Techniques of Data Masking

Different types of data masking apply different techniques. Some of them are as follows:

  • Data Encryption. The most popular type of data masking is to encrypt it so that the actual data is replaced by meaningless values. The encrypted data can’t be deciphered unless the user has the key.
  • Data Scrambling. Scrambling does exactly what one might think – it completely jumbles existing characters in such a manner that they make no sense. However, it is less secure and limiting.
  • Nulling Out. Wherever sensitive data is not intended to be seen by unauthorized people, the fields are populated with “Null” or “Missing,” thus making the data useless unless it is for simulation.
  • Value Variance. In situations where some data is required to run tests successfully, the actual values are replaced by the maximum or minimum differences of the same so that operations can still continue with the data.
  • Data Substitution. This is a spin-off of the value variance technique, where the data in question is replaced by another random value of the same nature so that operations can be carried out with the new value, which is a dummy.
  • Data Shuffling. Like the word suggests, this method of masking shuffles the similar data amongst itself, changing the order and replacing it with a randomized sequence of the same values, which can’t be misused.
  • Pseudonymization. This is the latest technique prescribed by the GDPR, where citizens’ personal identifiers are replaced by pseudonyms or dummy values that can’t be linked to the owner of the data. This protects user privacy.

Best Practices for Data Masking

Data masking can be used most effectively when certain best practices are involved. By ensuring the following checks are in place, you can make the most of data masking.

  • It is important to determine the scope of masking in terms of authorization of use, values to be masked, permitted applications, storage, and transfer of the data.
  • Integrity between departments needs to be maintained when employing different masking tools. Synchronization that allows effective communication must exist.
  • Data masking makes the target data safe; however, the algorithm that masks the data also needs to be secure for this method to be truly effective. Ensure that data masking algorithms are safe, protected, and secure.


Protection of data can be performed in many ways. Data masking allows for the safe and uncompromised usage of business data in testing, training, and demo programs which help improve consumer experience and business bottomline. It also helps keep prying eyes at bay.