Synthetic Test Data vs. Test Data Masking: How to Use Both

To use synthetic test data or to use test data masking — that is the question. But the answer may not be what you expect.

Before we dive into that, what’s happening in today’s business landscape that’s prompting the question around synthetic vs. masking?

Delivering high-quality applications at lightning speed is expected in today’s CI/CD world. Fast time-to-market is at odds with security and compliance requirements. The traditional paradigm of “speed, cost, or quality; pick two” has held companies back for years. It usually requires application teams to accelerate untested products to market, or worse, allow sensitive data to be used by non-privileged users for testing.

To solve this problem, the key lies in striking the right balance between realism and security in your testing environments. This is where two essential techniques come into play, test data masking and synthetic test data generation.

What Is Synthetic Test Data?

Synthetic test data is a class of artificially generated data unrelated to real-world events. It can be referred to as “fake test data” or “simulated test data.”

Synthetically generated values are useful when no real data exists that matches schemas or when compliance regulations restrict production data access. Unlike masked or anonymized data, synthetic test data is not a transformation of production data. It is entirely artificial.

Beyond mitigating security risks, synthetic data generation can protect sensitive information while greatly expanding test scenario coverage. For example, allowing for testing against broader sets of data that may not be available in your environment, such as edge cases, new markets, and changes to business procedures. 

You can generate large volumes of synthetic test data quickly and easily, which can help accelerate development speed.

Types of Synthetic Test Data

The type of synthetic data you need depends on your test scenario. Here are the main types:

Sample Data

Sample data is the simplest form of synthetic data, created quickly by developers during testing. It ensures all fields are filled and is useful for specific tests (like credit card numbers). However, it’s unsuitable for large-scale testing due to a lack of accuracy which can increase bug risks.

Rule-Based Data

Rule-based data is generated intentionally to meet specific test parameters, such as names, addresses, or special characters based on a geographic rule. It’s more structured than sample data and tailored to data field requirements.

Anonymized Data

Anonymized data is synthetic replacement data rather than generated data. It replaces real data with synthetic or randomized values to preserve security while optionally keeping the essence of the real data (like fake names). It’s ideal for protecting sensitive information while leaving remaining data intact.

What Is Test Data Masking?

Test data masking is the process of replacing sensitive data with deterministic, synthetic values that are realistic. Other ways you can refer to test data masking include static data masking, deidentification, scrambling, and PII (personal identifiable information) data masking.

Masking is useful because it provides realistic data with referential integrity intact, both in the tables being masked and in the values that have been masked.

With masking, you can protect sensitive data, which is critical for compliance with data privacy regulations and industry standards, while keeping it available for use.

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Get the complete guides to:

• Data masking methods and techniques
• Test data automation

Test Data Masking vs. Synthetic Test Data

The main difference between test data masking and synthetic test data is that masked data is a demographically accurate, yet synthetic replacement for a real value, while synthetic data is entirely artificial and may not always capture the nuances and patterns of real data.

Here’s an at-a-glance comparison of the two:

When to Use Test Data Masking and Synthetic Test Data

Test data masking and synthetic test data both have a time and place.

Use Test Data Masking to Shield Sensitive Data and Preserve Integrity

Imagine testing environments that can be kept up-to-date with production and mirror the richness and complexity of your production data — without ever exposing actual sensitive information. That's the power of test data masking.

Why is this important? Using real production data poses significant security risks. Using obfuscated encrypted data results in nonsensical values that aren't indicative of real-world functionality or performance.

By masking production data for testing, you’ll create a safe and compliant copy where sensitive values are replaced with synthetic, yet realistic, alternatives.

The best use cases for masking are for:

• Data privacy: Customer data, financial information, and other confidential details are protected from unauthorized access.
• Compliance: Meet stringent industry regulations (like GDPR, HIPAA, PCI DSS) without sacrificing testing accuracy.
• Realistic testing: Evaluate application performance, functionality, and error handling against datasets that closely resemble real-world usage patterns.
• Non-production network safety: Ensure that sensitive data is never present in non-production environments without sacrificing speed or realism.

The 2025 State of Data Compliance and Security Report

63% of organizations we surveyed are using synthetic data — and between 62 and 74% of them currently use it for software testing, development, and integration testing.

Explore more insights on synthetic data, AI, and the state of data compliance today in our 2025 report, compiled from a survey of 280 enterprise leaders. 

Get the Data Compliance Report

Use Synthetic Test Data to Tailor Data for Precise Testing

While masked production data excels in replicating real-world scenarios, synthetic data generation provides a powerful alternative.

The best use cases for synthetic data are when:

• Real data is scarce or sensitive: Early in development, when testing new features, or exploring edge cases, real data might be limited or too risky to use.
• Specific data characteristics are required: You need to generate data that precisely aligns with specific test cases, including unusual values, boundary conditions, or error scenarios.
• Chaos testing: You want both realistic and unrealistic data to ensure errors are caught before moving to production.
• API testing: Create a wide range of inputs and expected outputs to thoroughly validate API functionality and error handling.

By generating synthetic test data, QA teams can achieve targeted testing, enabling focus on specific application aspects or functionalities with data tailored to the task at hand.

Testing no longer must wait for a major release to be completed. It can be done at the feature level earlier in the development lifecycle, even before real data is available. This flexibility also extends to testing unusual or niche scenarios where QA teams can find potential issues that might be difficult to replicate with real data alone.

The Winning Formula: Test Data Masking and Synthetic Test Data Generation

What’s the secret to success for the world’s largest enterprises? A combined approach. Masked data and synthetic data generation, used together, will help you achieve the best results.

Masking production data for testing safeguards sensitive information in non-production environments. Meanwhile, synthetic test data generation fills in gaps and allows targeted testing of specific scenarios.

Synthetic test data for software testing can complement masking, and vice versa. Here at Perforce, we’re proud to offer solutions that help you do both.

AI-Powered Data Compliance with Perforce Delphix

Perforce Delphix unifies masking, AI-generated synthetic data, and data delivery in a single platform. The Perforce Delphix platform provides powerful masking for production data in testing — and in 2025 introduced AI-powered synthetic data generation. These capabilities create secure, compliant copies of production databases, ensuring realistic, yet risk-free testing environments. With Delphix, sensitive data will never reach non-production environments.

You’ll gain:

• AI-powered compliant data: Protect sensitive data through blending static masking and AI-generated synthetic data, using a rich library of pre-built and customizable algorithms to adhere to a unified enterprise policy.
• Compliant data in hours, not weeks: Automate compliant test data at enterprise scale, from mainframe to cloud — removing bottlenecks to innovation, increasing developer productivity, and safely speeding up digital transformation.
• Software quality: Leverage AI models to deliver realistic, production-like compliant test data by consistently discovering and protecting sensitive data with a single policy across sources. 

AI-Powered Synthetic Data [Demo]

Synthetic Test Data Generation with BlazeMeter

BlazeMeter excels at automated, continuous testing and includes algorithm or AI-driven synthetic test data generation tools. This allows QA teams to build and execute comprehensive test suites for even the most complex applications.

With BlazeMeter, you’ll gain:

• Data you can use now: Use synthetic data early in development, when testing new features, or when exploring edge cases.
• Data for every testing requirement: Generate data that aligns with specific test data, regardless of what real production data you have available.
• Chaos testing: Leverage AI and built-in rules to create negative counterparts of the expected “happy path” data. Use mock services to support unexpected scenarios.
• API testing: Create API tests in minutes and monitor APIs from development to production.

Start Testing With BlazeMeter for Free

Delphix + BlazeMeter: Accelerate the Development Lifecycle

Many enterprises use both Delphix and BlazeMeter to accelerate the software development lifecycle. Together, these powerful tools enable teams to deliver high-quality software faster, with greater efficiency and confidence.

Have questions, or want to learn more about how Perforce solutions can help you optimize testing, improve data management, and deliver software at scale?

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