Data Masking – A Comprehensive Guide In 4 Points


Data Masking in simple word can be defined as making a copy of the original data so that the data remains secured and remains free from risk. 

  1. What is Data Masking
  2. Why is Data Masking Important?
  3. Data Masking Techniques
  4. Data Masking Best Practices

1) What is Data Masking

Data Masking is the procedure of concealing the real data into an upgraded content. The main purpose of doing masking is to secure data that is sensitive in nature. A test can be conducted through Data Masking. It is commonly used in the enterprise for taking data from the production system to fill the data components.

Different types of Data Masking are as follows:

  • Static: It helps in creating duplicate data. This process changes all-important data until it can be shared securely.  Firstly, a copy of the original database is created and saved, transferring it to a different secured place and minimizing the unnecessary information.
  • Deterministic: This type of Data Masking involves directing sets of similar data, in a manner that the value is substituted by other value. This type of Data Masking is suitable for most cases, but it involves comparatively high risk.
  • On the Fly: This type of Data Masking means masking data when the data from the production system gets transferred to research systems before saving the data. These are mainly used in organizations which uses software and are unable to create a copy for their support, and that is why they go for Data Masking. On the fly, masking helps in sending masked data which are of small subsets whenever required.
  • Dynamic data masking: This type of masking is almost the same as previous data masking type, but here, unlike on the fly data Masking, secondary data is not used for keeping the records of tests. It directly streams from the production system. Dynamic Data Masking is used for creating a  fake network between the application which is being used and the database. It catches the SQL server, and after selecting the request, the rewrite is applied. This is only used by developers as they use the select request, but it is not used for storing purpose.

2) Why is Data Masking Important?

Data masking are essential for organizations. Following are some of the reasons:

  • Data Masking helps in removing several threats which are linked with data leaking. This occurs because the data goes in the wrong hands. 
  • It helps in reducing the risk of data which are linked with cloud storing.
  • Data Masking are fake data which are of no use for the attackers, this helps in making the data secured, and the purpose for which data masking is done is fulfilled.
  • Data Masking helps share data with the authorities only, and no third parties can get access to the secured data.
  • Data Masking is used for purifying the data, deleting the normal files’ results in leaving traces, while it helps by replacing the traces with the masked ones.

3) Data Masking Techniques

Data Masking is used by IT professionals in organizations. They are as follows:

  • Data Encryption: It is of no use until the viewer of the data has the decryption key when data is encrypted. These data are masked with the help of encryption of the algorithm. It is one of the most secured forms, also challenging to execute because it needs to be managed, and sharing of an encrypted key is required. It also involves complex technology.
  • Data Scrambling: In this technique, characters are recognized in a default manner, substituting the main information. For example, a word such as ‘percentage’ in an accounts database, could be substituted by symbol ‘%’  in attest database.
  • Value Variance: In this technique, the values of the original data are substituted by a variance, such as the difference in a higher and lower value in a database.
  • Data Substitution: In this technique, data are substituted by realistic alike alternative values which are fake. For example, the default selection of customer names is different for different customers. 
  • Data Shuffling: Data Shuffling is similar to the substitution technique, but the same database is used for switching data. Here, different columns are used in a default manner for arranging the data. The arranged data is similar to the original information, though the real information is not shown.

4) Data Masking Best Practices 

Following are the purposes for practising Data Masking:

  • Helps in Determining the Scope of the Project: Companies should have an idea about the information which needs to be secured, only then Data Masking can be effectively used.
  • It ensures Referential Integrity: This term means that all reference which is provided is valid. This implies that data coming from a company must be genuine and original.
  • It Secures the Data Masking Algorithms: It is essential to consider how the data is protected by making algorithms which help in making it more secure. 


Data Masking helps protect the confidential and important data safe as these data are not provided while doing a product demonstration, sales training, etc. Data Masking provides data for doing these tasks. Data Masking should be used by all the companies because it will help them secure their important data and prevent them from going into wrong hands.

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