In previous articles, we have covered the importance of using a data masking tool within a company. We have gone through the following points:

• How to gain speed and reliability when it comes to generating data for testing.
• How to avoid data leaks and, in turn, reputational crises.
• How to make data more secure and different techniques to do so.
• Several concepts on how to implement a cybersecurity strategy.

Have you ever thought about how you can protect sensitive data in your application? One way to do this is by using data masking, which is the process of obscuring sensitive data to prevent unauthorized access or disclosure. In this blog post, we'll talk about how to integrate data masking into a CI/CD pipeline using Azure DevOps as an example.

There are several reasons why it is important to integrate data masking into a CI/CD pipeline:

Data masking is an effective business tool used by large companies around the world to save time and money while improving their strategies.

The following three principles are: save time, save money and save resources. Saving time means spending only what is necessary. Saving money means not spending more than necessary. And saving resources means not wasting things that can be used over and over again. To do this, data masking allows us to see precisely what we have and don't have, so we can be intentional about how we spend it.

Continuous testing is one of the various techniques related to the DevOps movement that deals with the execution of automated tests for new software developments in each of the stages of the software lifecycle to ensure software quality.

Some tools that allow us to implement a testing system are Jest or Cypress, frameworks for unit testing in JavaScript; PHPunit or Pest, for PHP; Pytest or TestProject, for Python and JUnit or TestNG, for Java.

These test suites can be automated using tools such as Selenium, Tricetis, or Testim, among others.

Data masking is a strategy used to protect sensitive data in a dataset by transforming them into different data that maintain the coherence and consistency of the original set. A good data masking not only has to maintain data consistency and relations between tables, but also needs to replicate the same statistical distribution as the original source.

Also known as data "anonymization", "obfuscation" or "tokenization", seeks to generate realistic and anonymized datasets based on real production data, which can be used for alternative purposes such as analytics, test generation or AI training, all without compromising the security of the real data. Therefore, in order to keep the real data secure, the data masking process is irreversible; the user will not be able to get the real data by using the masked version.

How many times have we found that the data we are working with, either in testing or development, belong to real customers or users? Although it may not seem like it is a common practice in many companies. Working with real data is one of the factors that increase the risk of suffering a security breach in a company.

According to IBM's 2021 Data Security Breach Cost Report, these data breaches have increased by 10% in just one year and 135% in the last 6 years. This data corroborates that there is a trend in the search for this type of vulnerabilities in companies.