Data Masking is the act of replacing meaningful data with non-meaningful, or "masked" data to be used during development, and testing, etc. This is generally used to protect personally identifiable information from being seen outside of a production system.

The main reason for applying masking to a data field is to protect data that is classified as personally identifiable data, personally sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles. It must also look real and appear consistent.

The primary concern from a corporate governance perspective is that personnel conducting work in non-production environments are not always security cleared to operate with the information contained in the production data. This practice represents a security hole where data can be copied by unauthorized personnel and security measures associated with standard production level controls can be easily bypassed. This represents an access point for a data security breach.

A well-conceived data-masking program will have, among others, the following attributes:

  1. The data must remain meaningful for the application logic. For example, if elements of addresses are to be obfuscated and city and suburbs are replaced with substitute cities or suburbs, then, if within the application there is a feature that validates postcode or post code lookup, that function must still be allowed to operate without error and operate as expected. The same is also true for credit-card algorithm validation checks and Social Security Number validations.

  2. The data must undergo enough changes so that it is not obvious that the masked data is from a source of production data. For example, it may be common knowledge in an organisation that there are 10 senior managers all earning in excess of $300K. If a test environment of the organisation's HR System also includes 10 identities in the same earning-bracket, then other information could be pieced together to reverse-engineer a real-life identity. Theoretically, if the data is obviously masked or obfuscated, then it would be reasonable for someone intending a data breach to assume that they could reverse engineer identity-data if they had some degree of knowledge of the identities in the production data-set. Accordingly, data obfuscation or masking of a data-set applies in such a manner as to ensure that identity and sensitive data records are protected - not just the individual data elements in discrete fields and tables.