Here’s How to Implement Data Masking Techniques

 

Think of it as using a specimen of the original item to educate students. Of course, the specimen does not bear sensitive parts or features such as the original. However, it gets the job done.

Data masking is exactly the same as the reference discussed above. With this technique in modern data science, you can use data for training and testing purposes without having to expose sensitive data to a large platform.

Sounds interesting (or a bit murky)? Let’s consider the rest of this post.

Data Masking: Its Definition and Why It’s Important

Data masking is the science and techniques we use to ‘mask’ or hide sensitive data from users who are not authorized to access them. Therefore, data masking plays a huge role in data protection and high-risk data management.

Why do we need data masking? The points below might aid:

● It protects your sensitive data

● It helps you use the data for training and testing without risking data manipulation

● It can aid in data management and sensitive data discovery

● You can manage data integration better by controlling multiple databases using popular masking techniques

Data scientists and providers of data masking services often use these common techniques for data masking:

● Scrambling

● Encrypting

● Randomizing

● Nulling out

More techniques exist in the present-day data universe, which this blog may not elaborate on due to its short expanse. You might get in touch with a data scientist or an official data masking provider.

Ways to Implement Data Masking Effectively

Implementing data masking is not a piece of cake unless it’s done by data scientists. Besides that, the ever-changeable nature of data might call for advanced data identification and

strategies for implementing data masking techniques for ultimate benefits. The points mentioned below might tell you more about this factor:

● Data Pseudonymization

As difficult as the name sounds, it means making a ‘pseudonym’ for your sensitive data. It almost works in the way of making a carbon copy of data altering the sensitive part of the information. This technique takes data from the original data set (name, email, phone number, ID, etc.) and makes its pseudonyms. It can again retrace the original data by reversing the process.

● Data Lookup up Substitution

In this way, you implement data masking by adding a lookup table. You can use this table to fetch alternatives to the original data. Hence, it becomes perfect for applications in procedures like tests and more.

● Data Anonymization

In this use-case scenario, you can implement a data masking strategy to help identifiers give access to sensitive data to people. Those with unauthorized access or those who haven’t yet gotten access cannot enter the database for it. Therefore, your sensitive data remains safe.

● Data Encryption

Use encryption to make lookup substitution more robust. Use a password that encrypts the data in the lookup table. When an authorised personnel accesses the data, using the password can help decrypt the data ensuring readability.

● Redaction

Data might not be necessary anymore. You can simply redact such data by replacing it with traditional values. It serves well for tests and training purposes.

To Conclude: How Solix Helps

For dynamic data masking support, you might need a little bit of assistance. Luckily, we at Solix are here to give you that.

Let us know how you want your data protected. You can leave the rest to us!