5 Best Practices of Data Masking

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5 Best Practices of Data Masking

Data breaches are on the increase; it’s no secret. Almost every day brings news of a large corporation disclosing the loss of personal information, along with officials asking for a full investigation and a renewed commitment to securing consumer data.

What’s particularly perplexing about these circumstances is that current technologies and data protection best practices may enable firms to neutralize attempted breaches thoroughly. Data masking tactics that use next-generation techniques, in particular, have been shown to halt hackers and attackers in their tracks. 

What is data masking?

Data obfuscation, also known as data masking, substitutes sensitive information with fake but plausible values. Confidential information is made inactive, such as names, addresses, credit card numbers, or patient health information, but the masked data is still useful for application development, testing, and analytics. The version with the masked information may then be used for user training or software testing. The primary goal here is to generate a functioning replacement that hides the original data.

Why Is Data Masking Necessary?

  • Data masking eliminates several significant dangers, including data loss, data exfiltration, insider threats or account breach, and insecure connections with third-party systems.
  • Reduces the risks connected with cloud adoption in terms of data.
  • Data is rendered unusable by an attacker while retaining many of its basic functioning qualities.
  • Allows authorized users, such as testers and developers, to share data without exposing production data.
  • Can be used for data sanitization — whereas standard file deletion leaves data traces on storage media, sanitization replaces the original values with disguised ones.
  • Many types of sensitive information may be protected with data masking, including:
    • Personally identifiable information (PII)
    • Protected health information (PHI)
    • Payment card information (subject to PCI-DSS regulation)
    • Intellectual property (subject to ITAR and EAR regulations)
    • Data on Health and Finance 
    • IP addresses and passwords, particularly when combined with personally-identifying information

It’s crucial to examine your data thoroughly to establish what is sensitive (this is a significant component of many compliance programs. Consider how much difficulty your organization would have if you had to reveal that you had leaked this information. Would your business go bankrupt as a result of penalties or a loss of client confidence? Document which data is deemed sensitive, what systems handle that data, and how access is maintained with the help of your security expert or privacy team.

5 Best practices for Data Masking

Determine which data is sensitive

Identify and categorize the following items before masking any data:

  • Location of sensitive data
  • Groups of people that have been given permission to view the data
  • Application of the data

Masking is not required for every element of the company. Instead, in both production and non-production situations, properly identify any existing sensitive data. This might take a long time, depending on the intricacy of the data and the organizational structure.

Define your data masking technique stack

Because data differ so much, large enterprises can’t employ a single masking method across the board. Furthermore, the method you use may need you to adhere to certain internal security regulations or fulfill budgetary constraints. You may need to refine your masking approach in some circumstances. So, take into account all of these important criteria while selecting the proper collection of tactics. Keep them in sync to guarantee that the same type of data utilizes the same referential integrity approach.

Make sure your data masking procedures are secure

Masking techniques are just as important as sensitive data. A lookup file, for example, can be used in the replacement strategy. If this lookup file gets into the wrong hands, the original data set may be revealed. Only authorized people should access the masking algorithms; thus, organizations should develop the necessary standards.

Make the masking process reproducible

Changes to an organization, a specific project, or a product might cause data to alter over time. Whenever possible, avoid starting from the beginning. Instead, make masking a repeatable, simple, and automated procedure so that you may use it whenever sensitive data changes.

  • Define a data masking procedure that works from beginning to finish.
  • An end-to-end procedure must be in place for organizations, which includes:
  • Detecting confidential information
  • Using an approach that is appropriate
  • Auditing regularly to ensure your choosen technique is operating properly

Maintain Referential Integrity

Referential integrity requires that every data from a business application be disguised using the same methodology. In big enterprises, a single technique isn’t practicable. Data masking may be necessary by each business line owing to budget/business considerations, IT administration practices, or security/regulatory requirements. When working with the same kind of data, ensure that various data masking technologies and processes are synced. This will help later when data is needed across business divisions.

Conclusion

An efficient data masking plan is an apparent gain for the organization, mainly because the cost of a data breach can be measured in millions of dollars. Using a solution like Logiq.AI for implementing data masking will help developers, testers, analysts, and other data consumers spend less time figuring out the right ways to secure data and more time working.

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