Wednesday, September 04, 2019

The Power of Data Masking for Data Protection

Data privacy regulations and the desire to protect sensitive data requires methods to mask production data for test purposes. Data masking tools create structurally similar data that is not the same as the actual data, but can be used by application systems the same way as the actual data. The capability to mask data is important to be in compliance with regulations like GDPR and PCI-DSS, which place restrictions on how personally identifiable information (PII) can be used.

UBS Hainer’s Masking Tool for BCV5 (their test data management solution) offers robust masking of Db2 for z/OS data. I wrote about this capability previously on the blog last year (see Data Masking: An Imperative for Compliance and Governance, November 12, 2018), and if you are looking for a concise, yet thorough overview of the product’s data masking capabilities I point you to that blog post.

So why am I talking about data masking again? Well, it is a thorny problem that many organizations are still struggling with. As much as 80% of sensitive data resides in environments used for development, testing, and reporting. That is a lot of data that is ripe for exposure.

But I also wanted to share a new video produced by UBS Hainer that explains how data masking can help you to stay compliant and protect your sensitive data. It is well worth your time to watch this 2 minute video if you need to better address the protection of sensitive data at your shop.



Click to watch the video

Data masking is not a simple task, and as the video helps to explain, there is much to consider. To effectively mask your data requires a well-thought-out process and method for implementation to achieve success. As such, a tool like BCV5 Masking Tool can simplify how you address your Db2 data protection requirements. It provides dozens of easy to use masking algorithms implemented using Db2 user-defined functions. It ensures that the same actual value is translated to the same masked value every time. And the value will be a plausible value that works the same as the data it is masking. The tool understands thing like referential integrity, unique constraints, related data, and so on.


A reliable method of automating the process of data masking that understands all of the complicated issues and solves them is clearly needed. And this where UBS Hainer’s BCV5 Masking Tool excels.



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