Data Masking

Data masking is a method of creating a structurally similar but inauthentic version of an organization's data that can be used for purposes such as software testing. The purpose is to protect the actual data while having a functional substitute for occasions when the real data is not required.
In Application development "speed to market" is the overall goal but without data masking there is a risk of breach. 

We provide dynamic and static data masking to ensure that sensitive information is unavailable beyond the permitted production environment.

Data masking allows you to work with accurate data without re-engineering or identifying the original values. This allows developers to have the opportunity to work with data that is similar to what they would be working on in a live production environment by using synthetic data.

We provide Dynamic: "on the fly"  real-time data masking of production data and Static: protecting specific data elements at rest, typically database column or flat-file field values.

Sensitive Data
Masked Data
      Name            SSN                       Age
      Name            SSN                        Age
      Jean           180-80-0724            41
      Luisa              347-78-2178         45
      Clyde         252-38-1786            67
      Daniel            359-87-2143         64
    Data Masking Example

Static Data Masking

SDM is Primarily Used to Protect Data in the Following Scenarios:

Non Production User
Non Production Data with Masked Data
Masked Values​​​​
Application Development and Testing

  • Protect data and reduce the scope of compliance in dev and test environments where users need quality data without seeing production data.

Data Publishing and Sharing

  • Deidentify data for exchange among various parties or for publication.
Business Intelligence (BI) and Analytics

  • Protect data consumed by business application users where some or all users need to be prevented from seeing production data.
Static Data Masking ​​
Creates Non-Production Data
Production User
Values in Database​​
Production Database with Sensitive Data

Dynamic Data Masking

DDM is Primarily Used to Protect Data in the Following Scenarios:

Authorized User
Authorized User A​​
Authorized User B
Original Values
Masked Values
Scrambled Values
Business Applications 

  • Stored production data consumed by business application users, where some users are unauthorized to see original data. 

BI and Analytics

  • Protect data consumed by business application users where some or all users need to be prevented from seeing production data.

Application Development and Testing

  • Only a few authorized users need to see actual data or if regulations prevent the effective use of SDM.

Dynamic Data Masking Applies Rules Based On User Role​​
Values in Database​
Database Containing Sensitive Data

Why Should You Include Data Masking in Your Security Strategy? 

Test Data Management

Production Database Protection
Organizations use data masking to meet compliance requirements such as PCI-DSS, HIPAA, GPDR, PII, PHI, ITAR and EAR to protect data from abuse, preventing fraud and maintaining privacy. Data masking can support consistent policies across your data masking scenarios, applications and platforms. 
Sensitive data within production is susceptible to data breach which has been occurring more frequently. This is due to internal and external threats including employees and contractors who have access to production applications and who may intentionally or intentionally misuse data. Data masking works by reducing sensitive data exposure by providing users fictitious yet realistic data instead of real and sensitive data while maintaining their ability to carry out business processes.  
As businesses grow so does their data. Companies are continuing to transition to new operational processes such as DevOps, Agile and Waterfall to ensure the continuous delivery of quality data. Data masking protects data in development and test environments where users need representative data but do not need to see the real data. Having fictitious yet realistic data will allow teams to work with production like data to ensure quality while reducing risk.
Contact Us Today To Learn More About Data Masking
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