Introduction to Data Masking in Software Testing

Recall the times when you've checked out a new software product or gone through any training. Have you ever come across gibberish or codes instead of actual data? That's data masking.

Data masking allows you to hide personal identification information or sensitive data by scrambling or masking it. It helps tools and products showcase features while adhering to privacy and security measures.

The data masking process involves four stages. First, you identify the sensitive information that needs to be protected. Second, you choose the right masking technique for that scenario. Third, you deploy the chosen data masking method and hide the information. Fourth, you generate an audit report for analysis and compliance. 

Software Testing

One of the primary use cases of data masking is software testing. Software companies must conduct thorough user acceptance testing of the new features before releasing them. Some of these features must use data for testing, like adding employee details in an onboarding process or lead details in a sales pipeline.

Data Masking in Software Testing

Let's take a deeper look at software testing. Once a feature is tested, its results are published to internal teams and stakeholders or on public forums. If you use real data for these tests, you will expose people, places, or files to huge risks like identity thefts or cyberattacks. These threats and security risks can also become a liability for your company. To mitigate these risks, the real data must be masked before conducting any tests.

Once you identify the data that needs to be protected, the next step is to choose the right type of data masking for your use case. The most common type of software testing is Test Data Masking.

As previously discussed, software companies conduct rigorous testing before releasing new features. These tests are conducted by engineers and internal team members who need data sets for seamless testing without compromising data privacy. Most teams also indulge in continuous testing during the development cycle to ensure the end product is bug-free and ready to provide value right from the launch. This means there is a constant need for data masking to maintain data integrity and protection.

When choosing the right technique for software testing, you need to consider the features you're testing, the data required for testing, the security policies you need to follow, and the core functionality of your software. The most common masking techniques for testing software functionality are substitution, tokenization, and nulling.

Here's a complete list of all the data masking techniques you can choose from depending on the tests you're conducting and the data you need to hide.

Data Masking Techniques

Now that we understand data masking, let's examine the different ways to hide or protect sensitive data in various scenarios.

Randomization and Anonymization

These are the easiest and most common techniques to mask confidential data like names or places. In this technique, you substitute the original data with randomly generated or fictitious values that do not relate to the actual data. You can also use algorithms or AI to generate dummy data for you.

Encryption

Encryption masking is a secure way of storing original data for susceptible data. In this method, data is encrypted using algorithms and needs a key to be decrypted. While you may need to decrypt it for analysis, only authorized users can access it, protecting your sensitive information.

Shuffling

As the name suggests, this method involves shuffling or reordering the data to make it incoherent. It is particularly useful in data sets or tables where you must record individual data items. By shuffling, you preserve the data but make it unidentifiable.

Hashing

In this method, the data is converted into a string of characters. This is a common technique for masking sensitive information, such as passwords or captcha responses, that need to be displayed on the screen without revealing the actual data.

Tokenization

In this method, you replace the data with a series of letters and digits as a randomly generated token. However, you need to store the original data in a separate, secure location to prevent it from getting corrupted. This technique is used to showcase data when required while maintaining data integrity.

Nulling

This is a technique where sensitive data is replaced by blank spaces or greyed out. It is suitable for retaining the structure or format but hiding the data. This is widely used in screenshots, for example.

Now, for the main question: how do you mask data? While you can manually switch names or replace sensitive information with tokens and blank spaces, conducting tests on a larger scale would require a more sophisticated approach. Several tools offer data masking solutions, and K2view masks any kind of data, structured or unstructured, using the techniques listed above. With their solution, you can also produce compliance reports for audits and compliance.

Conclusion and the Way Forward

So far, we've discussed the benefits of data masking, but it comes with its challenges. One of the biggest challenges in data masking is preserving the original data while protecting it. Whichever technique and tool you use for data masking, ensure the original data is kept secure and under authorized access. The tool you work with should also ensure airtight data protection. As most things happen on the cloud in this age, your data can be susceptible to breaches or cloud jacking.

Another thing to keep in mind is to maintain consistency in masking data. If you've chosen to hide certain information, it must be hidden throughout your testing and in all the instances of your tests. Lastly, when handling sensitive data, you must comply with local data protection laws, such as HIPAA and GDPR.