Whether you are an individual or you own a company, data security is likely one of your top concerns, and rightly so. Data breaches are becoming an increasingly common phenomenon. Major companies such as eBay, Yahoo, Marriott International have all suffered major data breaches.
The upshots of such breaches are grave: millions of dollars are lost, reputations are destroyed and companies often face an uphill task trying to keep going. A study by Ponemon Institute indicates that data breaches cost $3.86 million in 2018.
Industry experts agree that one fool-proof way to prevent data breaches is to create a protective layer of data that looks just like the real data but is ultimately useless. This is known as data masking.
What is Data Masking?
Data masking is simply altering real data in order to protect it from breaches. In data masking, the original data from a data store is changed such that while the format is maintained, the content of the data is not the same.
Take credit card numbers for instance; such numbers have a 16-digit format like this: 5143-4321-2378-4567. With data masking, the numbers are changed but the 16-digit format is maintained. Using the example above, the masked credit card number could become 1234-7482-1986-6543. Clearly, while the structure of the original data is maintained, the content is ultimately useless such that even if hackers gain access, their efforts will be futile.
Often, data such as personally identifiable information, protected health information, payment card information and intellectual property are supposed to be masked.
Why is Data Masking Important?
Data masking is important for three principal reasons:
1. Protect Consumer Data: Often for application development, testing and training as well as analytics, an enterprise may have to provide data. However, because of data masking, similar but changed data sets can be handed over to third parties thus protecting consumer data.
2. Prevent Insider Threats: The Ponemon Institute indicates “that 88% of all security breaches involve insider negligence.” By masking delicate production data, companies make available data workers require to perform their roles while reducing the risk of a data breach from a malicious, careless or compromised insider.
3. Compliance: There is increasing regulation and legal action in the sector of data security. For instance, Uber shelled out $148 million to settle legal action in connection to a 2016 data breach that affected 57 million customers and drivers. Masking data ensures that an enterprise does not contravene laid down rules.
Data Masking Software
Data masking is no easy task. It is impossible to perform data masking manually. That is why efficient data masking software is important. Here are a few things you should look out for when searching for a good data masking software.
• Performance and “Scalability.” The data masking tool you use should be easy to install, deploy and manage. The flexibility and potential of scaling the product should also be considered. Additionally, the data masking software should integrate well with other supporting systems.
• Data Integrity: You should find out if the data masking solution being presented to you preserves the usability of your data. If the data is to be used for application testing and business analytics, it is important that the data looks real and does not break any application logic.
• Track Record and Services: Before deciding on the software to use, it is important to consider the track record of the solution provider as well as the auxiliary services that are provided. For instance, does the company help with deployment and setup? How about maintenance?
In Conclusion
As data breaches become more prevalent and common, it is important for companies to adopt foolproof strategies to avoid any kind of tampering with their data. That is why data masking is a key practice every enterprise should develop. However, to properly implement data masking, you’ll have to understand it, know its benefits as well as the tools and softwares that can help make it possible.
