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Published on June 28, 2024

All You Need to Know About the Concept of Data Masking in Today’s Threat Landscape

With the significant proliferation of mobile devices, applications have become an indispensable component of modern life. From grocery shopping to internet banking, people are perfectly dependent on applications and websites for almost everything. So, there is no doubt in this particular point that applications are consistently dealing with the storage of data along with exchange which further results in the Improvement of protection of data at all stages. With the best possible regulations and compliance laid down by the authorities, it is important for companies to take data security very seriously and data masking provided by Appsealing is one such great technique for protecting the data from unauthorized users.

What do you mean by the concept of data masking? 

Data masking is basically the process of creating a duplicate of the organizational data by protecting the sensitive data so that it is rendered useless to unauthorized users and hackers. This copy of data will be a realistic version that can be used for software testing, training, and sales demo purposes. So, the data masking will alter the sensitive data but still, it will uphold the original character characteristics of the data without any problem.

Data is very well required for a significant variety of purposes but it is not a dollar practical and safe option to use the production data every time for training and testing. So, at the time of dealing with the data masking the values will be changed so that testing will be perfectly performed depending on the original data set related things. This concept will be definitely helpful in ensuring that sensitive information will be very well sorted out beyond the production environment and substitutes will be created and made available for the administrators to test the things without any compromise over the security. 

Some of the most common reasons highlighting the importance of data masking have been explained as follows:

  1. Copy of the production data in this particular case will be used in the nonproduction environment so that application development, testing, and personal training will be sorted out. 
  2. Insider threats in this particular case can never be overlooked and the data masking will serve as the effective remedy against the breaches so that unintentional employee will be sorted out 
  3. It will be perfectly working as the concept of reducing the data risk that will arise from the use of cloud technologies 
  4. Data allows, account compromise and data exfiltration will be serious threats faced by organisations and this concept will be helpful in providing them with protection from issues 
  5. This is highly effective in terms of providing people with support for data sanitisation which refers to the replacement of the old values with the new ones so that there is no scope for any kind of misuse. 

Some of the most common techniques associated with data masking have been very well explained as follows: 

  1. Encryption: This will ensure that data will never be comprehensible for unauthorized users and hackers with the use of algorithms. Only the users who will have the description keys will be able to access the data and make sense of it. It is the most common way of improving the data and managing the keys is critically important for uncompromised security. if the malicious actors will be getting the accessibility to the key, then exposure of data will be there.
  2. Data scrambling: This will refer to improving the security of data by reorganizing the characters and numbers in the database because if a particular ID number is 45879, with the application of the data masking it will result in 98754 in the test database. However, this particular technique is not at all considered to be full proof method of improving security.
  3. Nulling out: This is the basic technique that will be based upon an unauthorised person who will not be able to view the data and the sensitive data will be completely missing in this case. However, it is important to note that this technique is not at all preferable when the data has to be used in the testing environment. 
  4. Redaction: Similar to any other kind of related technique, this will be replacing the sensitive data with the generic value instead of NULL values and this is again not at all useful for the developers for QA purposes.
  5. Substitution: This is a very reliable technique of data that will be substituting the sensitive data with duplicate values supplied from the look-up table that looks very realistic. Only the authorised users will be able to read the original data in this case 
  6. Averaging: This technique is applicable in the case of numeric data and values in the individual cell will be placed with a collective average of all the values in the column in the average technique of this concept. 
  7. Shuffling: This refers to the interchanging values for example a table with an employee’s salary mentioned that would contain the real figures but not matched with comprehending names. 
  8. Date aging: This means that dates in the table will be marked by the application of certain policies to the date field and the real date in this case will be marked by setting the dates back or forth. It can be set back or forth by 100– to 200 days and in this way, the actual dates will remain hidden as per the algorithm or the chosen policy of the company. 

Apart from the above-mentioned points, the concept of data masking will be extremely beneficial for companies that are interested in seeking protection from insider threats. This will promote the application security very well and further will be successful in entertaining the data structure as well as format by making it ideal for non-production purposes. This will allow the outsourcing of data-related tasks to the third party so that there is no compromise over security and the data analytics will be simultaneously improved. In addition to this undertaking the data metrics by the experts is important to deal with the regulatory requirements where the personally identified data has to be strictly Protected.  


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