Unifying Data Masking and Synthetic Data for Test Data Management

Provisioning data for software testing requires balancing realism against security. Teams need production-like data to validate applications effectively. But they also have to adhere to strict privacy regulations. Two of the leading methods for creating and securing test data are data masking and synthetic data generation.

Data masking de-identifies sensitive production data, preserving its scale, realism and referential integrity. Synthetic data generation creates entirely new, customized, artificial data where no production data exists, mimicking the format of real data but containing no sensitive information.

The most effective test data management strategy is not about choosing one method over the other; It is about unifying them into a single, cohesive data delivery pipeline. 

Integrating both allows organizations to address a wider range of use cases, accelerate development, and ensure compliance without compromise.

Data Masking and Synthetic Data: Complementary, Not Competing

Our State of Data Compliance and Security Report found that the world’s leading enterprises use both masking and synthetic data: 95% of organizations use static data masking, while 63% use synthetic data which are not mutually exclusive. The widespread adoption of both underscores that they solve different, yet related, problems.

What Data Masking is For

Data masking is the solution for testing scenarios that require the full scale and complexity of a production environment with enterprise-grade security. 

Its primary use cases include:

• Regression and performance testing — where large, realistic data volumes are essential to validate application stability and response times.
• Full-scale development and testing — especially for later-stage integration testing and user acceptance testing (UAT) that demand a comprehensive, production-like dataset with referential integrity across multiple applications in the end-to-end system.
• AI/ML model development and analytics — where production-fidelity data is required to preserve statistical accuracy, behavioral patterns, and data relationships while ensuring sensitive information is protected from exposure to data scientists, ML engineers, and analysts.

By replacing sensitive fields like PII or PHI with fictitious yet plausible values, masking de-risks data while maintaining its operational integrity and analytical meaning. This is critical for "brownfield" projects involving mature applications built on large, existing databases.

What Synthetic Data Generation is For

Synthetic data generation is ideal for early-stage, "shift-left" activities where speed and agility are the main priorities. It offers the ability to provide custom datasets where no production data exists. It also supports unique and new testing scenarios with test data custom-built to support those scenarios. 

That makes it valuable for:

• Unit testing — where developers need small, specific datasets to validate individual components quickly.
• Net-new development — for "greenfield" projects where no production data exists yet, or for testing new features that require data structures not present in the current production schema.

📘Further reading: Synthetic Test Data vs. Test Data Masking: How to Use Both.

The Power of Unification: A Complete Test Data Management Strategy

Synthetic and masked data’s full potential is unlocked when you combine the two. This unified strategy creates a comprehensive test data management framework, enabling organizations to handle any testing scenario with optimal data while ensuring their data is secure and compliant in non-production environments.

The need for a robust, unified strategy is evident when you consider that 60% of organizations have experienced a data breach or theft in non-production (according to our report). 

A unified approach mitigates this risk by providing flexible, compliant data for every development stage. 

Some use cases include:

Augmenting Masked Data

For new features requiring data fields not present in production, a unified approach allows you to take a fully masked production dataset and synthetically generate realistic values for only the new columns. This enables immediate testing without compromising existing masked data integrity.

Maintaining Referential Integrity Across Masked and Synthetic Data

When masked production data serves as a foundation, a unified approach allows you to synthetically generate child records that correctly link to masked parent data, ensuring complex tests remain valid. An example: synthesizing new fictitious loan applications linked to masked customer accounts.

End-to-End Testing

An integrated platform ensures teams use the right data at the right time. Lightweight synthetic datasets are ideal for rapid unit tests. For full-scale integration testing and UAT, a comprehensive, masked copy of the production database can be utilized.

Future-proofing Applications Securely

A unified approach starts with fully masked production data to preserve real-world complexity while protecting sensitive information. Synthetic data can then simulate growth, new customer segments, or peak-load conditions, enabling proactive performance validation and capacity planning. This ensures applications remain stable, scalable, and secure during critical business events.

For Example: Large Retail Company

For instance, a large retail company testing a new recommendation engine can initially use synthetic data for algorithm validation. However, for real-world performance validation, generating massive amounts of synthetic data is impractical. 

Instead, they can create masked copies of their production database to de-risk sensitive information. If additional data fields are needed for the new engine, they can then generate synthetic data on top of the masked dataset, providing a complete and secure environment for end-to-end validation.

The State of Synthetic Data Today

63% of organizations now use synthetic data to protect sensitive information in non-production environments.

Our State of Synthetic Data report explores the role of synthetic data in the enterprise today. Learn where enterprise teams are using it and how leading companies optimize compliance with a portfolio approach.

Uncover expert insights and recommendations in our mini report.

Explore the State of Synthetic Data [report]

How Delphix Delivers a Unified Data Solution

Creating synthetic or masked data is only part 1 in your test data delivery process. Part 2 is quickly getting that data to downstream environments and users.

In our experience working with enterprise teams, a fragmented approach to test data is a significant bottleneck to DevOps. The Delphix DevOps Data Platform was built to natively integrate data masking, synthetic data generation, and data virtualization into a single, unified solution.  

It spins up masked or synthetic data copies in minutes, delivers as many copies as you need for little to no extra storage, and it lets you maintain a comprehensive test data library with masked or synthetic virtual data copies for developers to pick from.

Here is how Delphix provides a superior, unified solution, in a nutshell:

Many organizations see protecting sensitive data as a roadblock. In fact, 61% of organizations say protecting all sensitive data in non-production “slows innovation.” But this doesn’t have to be the case. Automation and self-service capabilities are key to securely speeding up development at enterprise scale.