Test data management (TDM) requires a way to create production-like environments that are safe for software testing. However, creating realistic test data is difficult when the environment contains sensitive information.

Data masking is an alternative that transforms real data into formatted fake data. This allows organizations to use existing data in more relaxed nonproduction environments or third-party developers, with reduced levels of risk.

Test Data Management

Data masking is a common practice to protect test data management in a non-production environment. It obfuscates sensitive information, ensuring test teams can access it with a reduced risk of leaks and breaches. This helps to ensure compliance with CCPA, GDPR, HIPAA, and other regulatory requirements for privacy protection.

The process of masking replaces identifiable values with fictitious, realistic equivalents. This makes it impossible to discern a meaningful value from masked data and renders the masked data useless for thieves or hackers. It also enables you to use the same lookup file for multiple masking algorithms so that your test data is consistent across different environments.

A good solution for data masking will offer a scalable and easy-to-use platform that can handle large amounts of data. It will allow you to reduce the number of unmonitored copies, provide data scrubbing functionality, and automate workflows to save on processing costs and storage. This will also help to eliminate manual processes and improve efficiency.

Test Data Masking

Data masking is the process of modifying real-world data to protect sensitive information in non-production environments. It’s often used for software development and testing, user training, or sales teams.

Masking can be done through a variety of techniques, including reordering characters, scrambling, replacing, or encrypting data. The end goal is to create pseudo data that looks authentic and is still useful for testing and analytics.

However, if a hacker knows which masking algorithm was used or the alternative data sets and dictionaries provided to the scrambler, they can reverse engineer the original information. This means that it’s important for organizations to have on-the-fly data masking and a consistent approach across all of their data sources.

Hevo’s on-the-fly data masking is fast and automatic, allowing it to be integrated into SDLC or DevOps workflows. It also ensures that the right amount of masked data is sent to developers and users without overflowing into production systems or creating compliance risks.

Test Data Encryption

Unlike encryption, masking makes it impossible to decrypt data back to its original form. It is a standard procedure for protecting copies of sensitive data in non-production environments such as development and testing. It replaces sensitive values with fictitious, yet realistic equivalents and meets data compliance requirements.

It’s a vital part of shift left testing because it allows developers to work with representative test data without the need to obtain the real thing from production systems. It is accomplished by obfuscating PII data and transforming it into characteristically irreducible pseudo data, which can be used for software development purposes, user training, Business Intelligence, or sales teams.

It’s also an important part of agile software development because it allows developers to run tests with real data as early as possible in the lifecycle, enabling them to spot defects sooner and improve the quality of their applications. It also saves time and money by eliminating the need for a staging environment, which can delay project timelines.

Implementation

The vast majority of sensitive data in an enterprise exists in non-production environments used for development and testing functions. This creates a large surface area of risk and requires protection to ensure compliance with privacy regulations.

Test data management tools offer a secure and reliable solution to protect this critical information. Data masking techniques obfuscate data by shuffling or replacing it with formatted fake data, allowing developers to continue working on applications without risking the integrity of live production systems.

One of the more common methods of data masking involves character scrambling, which reorders the order of characters and replaces them with null values. This technique is not always effective and can leave unauthorized personnel able to decipher masked data.

Another common method of masking is using repeatable algorithms to scramble the data. This can also be a dangerous approach if the algorithms are revealed, as hackers may know which patterns to look for. To reduce this risk, it’s recommended that the entire data masking process be run by teams consisting of database administrators and security personnel to ensure proper oversight.