Data masking is a technique used to create a structurally similar but inauthentic version of an organization's data that can be used for purposes such as software testing and user training. It's a way to protect sensitive data while still allowing developers and QA to do their jobs without accidentally exposing customer info to the world like a certain social media company that shall remain nameless.

How to use it in a sentence

• "I can't believe we're still using production data in our dev environments. We need to get data masking set up ASAP before we end up on the front page of Hacker News."

• "Sure, the sales team says they need full customer data for their demo, but let's be real - they're just going to put it in a spreadsheet and email it around. Time to break out the data masking and give them a sanitized dataset."

If you actually want to learn more...

• Data Masking: Anonymization or Pseudonymization? Data masking techniques fall into two categories - anonymization which irreversibly destroys any way to identify the data subject, and pseudonymization which substitutes an alias for the identity but can be reversed. Read more

• The Fundamentals of Data Masking. This article covers the basics of data masking including common techniques like substitution, shuffling, and encryption, as well as when to use each approach. Read more

• Data Masking Best Practices. Practical tips for implementing data masking, such as using a dedicated masking engine, masking data as close to its source as possible, and validating that masked data retains referential integrity. Read more

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