Achieving Data Privacy in Testing

test data management

Data privacy can seem like a huge hurdle for a company to overcome. However, with a little forethought and application of best practices, it is possible to make privacy an integral part of your testing process.

Robust test data management processes incorporate several attributes that ensure both a robust and secure testing environment. These include masking, subsetting, and access control.

Test Data Management

Test Data Management (TDM) is the process of creating nonproduction data sets that reliably mimic an organization’s actual data for systems and software testing. It enables application and development teams to create high-quality applications that reduce time-to-delivery while meeting compliance and data privacy regulations.

TDM tools offer a variety of functionality to protect data privacy, including obfuscation, masking, subsetting, and access control. These features enable CIOs and CISOs to manage PII, financial, proprietary, or other sensitive data without risking business-critical applications. For example, obfuscation techniques like masking or replacing personal information such as names and dates with fictitious data ensure that privacy-sensitive values remain unaffected by test cases. Another way to protect data privacy is through data subsetting, which allows users to distribute data with minimal storage and processing costs and lower idle times for testing environments.


Data encryption provides a level of protection for sensitive data. It is an effective strategy for reducing the risk of cyberattacks and helps organizations meet industry regulations and government policy. Encrypting data may be required or strongly encouraged in some industries such as healthcare and financial services.

Test teams rely on real-time production data for development and testing purposes. This can lead to security breaches, especially when the data is transferred to a third party or accessed by non-production environments. Masking or obfuscating data can reduce this risk by replacing real data with unrealistic values, such as fake names or numbers. This technique also protects data at rest and in transit, which is particularly important for regulated industries such as healthcare or finance. This is also a way to comply with data privacy laws, such as GDPR.


Masking is a technique that removes personally identifiable information from data sets while preserving the functionality of the data. This allows development and QA teams to work with realistic, accurate data for testing purposes without exposing sensitive personal information to cyber attackers.

This approach also helps companies comply with regulations such as GDPR and CCPA and maintain data security. Additionally, masked data isn’t vulnerable to breaches and other cyber attacks that can occur inside an organization (internal threats account for more than 60% of all data attacks). Data masking techniques should be customized per dataset and used in conjunction with other tools such as encryption. It is critical to consider the algorithms that will be used to scramble data to avoid reverse engineering.


Data subsetting is the process of identifying and provisioning a subset of production data for testing. It can help reduce the costs and time required to provision test data, allowing for a more realistic and efficient testing environment.

Testing with production-like data is the best way to improve application performance. However, creating reliable and usable test data is a complex task.

Using real data in test environments often involves exposing sensitive information. This can raise security and privacy concerns, including the need to adhere to data protection regulations protecting test-takers. It also poses the risk of data breaches. To address these challenges, organizations can use tools like GenRocket’s Intelligent Data Subsetting. This tool enables users to create subsets of SQL production data and provides them for automated testing within a CI/CD pipeline in minutes.

Access Control

In addition to physical barriers (like fences and security guards), access control consists of authentication and authorization, which determine whether an unauthorized user should have unrestricted access. These techniques can help ensure that test data management tools and environments are accessible only to authorized users.

New technologies are advancing healthcare in extraordinary ways – enabling providers to map patients’ bodies for surgery visualization; use robotic hands for less-invasive, more precise surgeries; and remotely monitor diabetes patients’ blood sugar levels. While these innovations can improve the health and quality of life for many patients, they also present new security risks.

The new GDPR privacy regulations have refocused attention on the need for more robust test data management (TDM) practices. The Informatica TDM platform includes a powerful suite of features to identify personal and sensitive content including data discovery, masking, and subsetting.

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