Static Data Masking vs. Dynamic Data Masking

Data masking is a critical approach to data security and privacy. It consists of a set of practices that make sensitive data unidentifiable without sacrificing the usefulness of the data for analytics and functions. In a layered security strategy, the role of data masking is to protect personally identifiable information. 

There are several ways to alter data, but two primary types of data masking are static data masking (SDM) and dynamic data masking (DDM). Both have pros and cons, and one may be better suited for specific use cases than the other. 

This guide explores static vs. dynamic data masking and explains the pros and cons and use cases of each approach. 

What is static data masking?

SDM is a method to protect large volumes of data while the data is at rest. In SDM, a copy of the data is made, all the personally identifiable info is eliminated, and then the copy is detached from the original. The copy can then be shared, stored, and processed without exposing sensitive information. The copy contains high-quality, realistic data ideal for application development and testing. 

SDM achieves realism without disclosing sensitive information, which is necessary to test relevance and accuracy. If the data vastly differed from real-life data, the test results would not apply to real-life app usage. SDM executes compliance with regulations like the General Data Protection Regulation (GDPR), Payment Card Industry Security Standards Council (PCI SSC), and Health Insurance Portability and Accountability Act (HIPAA). SDM is also helpful for masking data before it goes on the cloud. 

How Static Data Masking Differs from Dynamic Data Masking

Both SDM and DDM protect sensitive data, but there are three critical differences between the two approaches: 

  • SDM is a relatively long-standing practice. DDM is relatively new. 
  • SDM is performed on a copy of the data, while DDM is performed on the original data in real time. 
  • SDM is ideal for development, testing, and compliance. DDM is suitable for production. 

Pros and Cons of Static Data Masking

Pros 

  • Sensitive data is permanently removed from the data store.
  • SDM does not impact transactions because all the data is masked up front. Once the data is available for functions, it has already been masked.  
  • SDM protects copies of production data from many different types of queries and access.
  • SDM simplifies the process of securing data copies because all the sensitive data has been replaced. 

Cons

  • SDM does not happen instantaneously. Instead, masking is applied in a batch process to a data store which may require several hours if the data volume is substantial.
  • SDM permanently alters the data set, so it is unsuitable for production data applications. Instead, it operates against copies of production data. 
  • SDM can be challenging to rescale when new data or access are introduced. 

Pros and Cons of Dynamic Data Masking

Pros 

  • DDM provides an additional layer of privacy and control to sensitive data. 
  • Data is protected in read-only contexts. 
  • DDM helps avoid data silos because the data does not have to be copied for masking. 
  • DDM enables near real-time performance.
  • With DDM, masking is in real time and live, hence it is ideal for analytics. 
  • DDM does not require batch processing to mask data. 

Cons 

  • DDM is not designed for read/write applications because the masked data could be mistakenly put back into the database, corrupting data integrity. 
  • The overhead required to inspect all data traffic going to the database can be high and inaccessible for some enterprises. 
  • Detailed mapping of applications, ecosystems, permissions, and users is required to facilitate DDM, and this requires resources that may be out of reach for some companies. 

When to Use Static vs. Dynamic Data Masking

SDM and DDM both have specific use applications. SDM suits environments where the data is static instead of constantly changing or evolving. The ideal use applications for SDM are application and software development and training. DDM is suitable for analytics applications because the data masking is always live and up to date. In DDM, the masking is not tied to where the data is stored, so it has broad applicability, scales easily, and can handle complex scenarios and applications. 

Looking to partner with an expert in software development and testing? Contact Encora to learn more about data masking and our advanced software engineering capabilities.

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