Why You Should Avoid Testing With Production Data At All Costs

Using high-quality data in software testing ensures test results are relevant and accurate. But the challenge often lies in retrieving data for testing. It might be tempting to use production data in your tests, since this data comes from actual user scenarios and is already available. However, this can be a costly mistake.

In this article, we will explain the risks of testing with production data. You will learn about the pitfalls of using live data in non-production environments and discover two safer and more scalable alternatives: synthetic data generation and data masking. In the end, we will recommend practical tools you can use for these purposes.

The Importance of Data in Testing

Data is a fundamental component of the testing process, since it allows simulating real-world scenarios and user behavior. For example, accurately testing an eCommerce checkout flow requires realistic product listings, user profiles, and payment details. Testing data also allows testing edge cases, which are the rare conditions that might occur. In the checkout example, a user enters the wrong credit card number or their home address into the payment section.

Without relevant, complete and accurate data, tests may pass in theory but fail in production. This is because test outcomes in the controlled environments would not be representative of how users interact actually with the application. In other words, production would not mirror the scenarios used in the test conditions.

Moreover, reliable data also supports testing automation. By ensuring there is a consistent and reliable data source in your testing workflow, you can automate testing in CI/CD pipelines and speed up release cycles.

Data Requirements for Testing

For data to be reliable in testing, it must be:

• Relevant  - Reflecting the scenarios being tested.
• Complete - All necessary fields and variations should be present.
• Accurate - Correct and realistic, mimicking production-like behavior.
• Consistent - Maintaining integrity across related systems and tests.
• Fresh - Reflecting the latest schema, rules, and logic.
• Compliant - No real user information.
• Scalable - High volumes of data, on-demand.
• Accessible – Easy to retrieve, manage, and reset across testing environments.
• Repeatable– Ensuring  consistent test results across multiple runs.
• Traceable – Allowing mapping test failures back to specific data inputs or conditions

Why You Should Not Test with Production Data

Testing with production data might seem like the obvious choice. It is readily available, reflects exactly how users operate in production and answers all the requirements listed above. Unfortunately, you should avoid it at all costs. Here is why:

Security and Privacy Risks

Production data often contains personally identifiable information (PII), financial records, credentials, or proprietary business insights. Using it in a testing environment creates a security vulnerability, since test environments often are not held to the same security standards as production systems. (And even if they are, why enhance the attack surface with two environments for attackers to target?). As a result, a misconfigured test system or a leak during development could lead to costly data breaches or compliance violations (GDPR, HIPAA, etc.).

Legal and Regulatory Issues

Testing with regulated data without proper consent or anonymization could be in breach of legal and compliance obligations. This means that using the data is a violation in itself, even if the data never leaves your network and no security breach occurs.

Data Integrity and Contamination

Testing can unintentionally alter data, especially if the test environment connects to live systems or APIs. If production data is mistakenly written back or modified, it could corrupt live business operations, skew analytics, or mislead end users. For example, a test that simulates user deletion might accidentally remove real customer accounts or transactions.

Scalability

Testing, especially performance testing, might require large volumes of data, which are not always available in production. For example, if you want to simulate a thousand concurrent users checking out on an e-commerce site, you need a large, diverse set of user accounts, orders, payment methods, and product inventories. Production data might not have enough variety or scale—or might be skewed toward typical, not extreme, usage patterns.

Edge Cases

Production data covers real-world use cases, which are its main strength. However, data is also needed to cover edge cas before they become real-world scenarios. This data simply does not exist in production. In addition, there is the issue of data drift. Over time, user behavior evolves. Tests based on old assumptions might break or miss new edge behaviors.

The Solution: Synthetic Data and Data Masking

With testing data being a fundamental requirement and the use of production data off the table, what can testers do to simulate data in tests? It is recommended to either use synthetic data or data masking, depending on the need. Take a look at both options.

Option #1: Use Synthetic Data

Synthetic test data is artificially generated data that mimics the structure and behavior of real-world data without containing any actual sensitive or personal information. This allows teams to simulate various scenarios without exposing personal data, de-risking data privacy or compliance violations.

Synthetic data can also be created at any required scale, on-demand. It also enables covering test cases not yet existing in real-world scenarios (edge cases). When AI is used to streamline the creation, this can occur automatically and semi-autonomously.

Benefits of Synthetic Test Data:

• Enhanced Security and Compliance: By using synthetic data, organizations can avoid the risks associated with handling real user data, ensuring compliance with regulations like GDPR and HIPAA.
• Customizable and Flexible: Synthetic data can be tailored to specific testing needs, allowing for the creation of diverse datasets that cover a wide range of scenarios, including edge cases.
• Improved Test Coverage: With the ability to generate data on-demand, teams can ensure comprehensive test coverage without being limited by the availability of real data.
• Consistency: Unlike real-world data, which may contain errors, missing values, or inconsistencies due to human input or complex system behaviors, synthetic data is algorithmically produced using predefined logic, models, or constraints.
• Cost and Time Efficiency: Generating synthetic data can be faster and more cost-effective than collecting and sanitizing real data, accelerating the testing process.

Option #2: Data Masking

Sometimes, generating synthetic data is not a good fit. This can happen due to resource requirements, when you are testing unique and complex cases, or when the test case is small-scale.

In this case, we recommend using data masking. 

Data masking protects sensitive information by replacing real data with fictitious, yet realistic values. For example, replacing PII or PHI with anonymized versions. This approach ensures that data remains useful for development, testing, and analytics while safeguarding against unauthorized access

Benefits of Data Masking

• Enhanced Security and Compliance: By anonymizing sensitive data, organizations can mitigate the risk of data breaches and comply with regulations such as GDPR, HIPAA, and PCI DSS.
• Preservation of Data Utility in Sophisticated Scenarios: When the use case is complex and required data is hard to replicate, masked data allows teams to perform realistic testing and analysis.

Data Masking vs. Synthetic Data Creation: Comparison Table

About Perforce’s Test Data Capabilities

Perforce provides testers with both synthetic data creation and data masking capabilities.

Synthetic data creation is offered through BlazeMeter Test Data Pro, an AI-driven solution designed to streamline the creation and management of artificial test data. BlazeMeter automatically identifies and creates synthetic data at scale with AI, simplifying the data generation process and accelerating testing.

Data masking is offered through Delphix. When you need to use real data in your tests, Deplhix protects you by automatically identifying sensitive information across various data sources and replacing sensitive data with realistic, fictitious values while preserving referential integrity. 

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