Test Data Management Best Practices

Modern software development methods have revolutionized how quickly organizations deliver new applications and features, but it has also expanded the attack surface dramatically. 

Organizations often provision sensitive data into testing and development environments without adequate protection, inadvertently increasing exposure to threats, e.g., insider risks, data leaks, and regulatory violations. 

Relying solely on perimeter-based defenses like firewalls or access controls to protect sensitive test data is no longer sufficient. 

In today's complex landscape of global data residency laws (GDPR, CCPA, HIPAA, PCI DSS), rampant shadow IT, and sophisticated cyber threats, a data-centric Zero Trust approach to test data management isn't just a best practice – it's critical. 

By shifting protection directly onto the test data itself, you significantly reduce risk, streamline compliance, and ensure that even in the event of a breach, your sensitive data remains secure and valueless to bad actors, within or outside your organization.

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6 Test Data Management Best Practices

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1. Apply an “Assume Breach” Mentality for All Test Data

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In today’s environment, you must protect your data by default. In other words, there must be an organizational policy where all data going to non-production environments – e.g., development, testing, quality assurance, etc – must be protected before provisioning or access.

To be clear, we’re talking about the data specifically. Where traditional cybersecurity methods had focused on building taller walls around the data (e.g., access controls, firewalls, DLP, etc), today, you need to ensure the data in and of itself is safe. 

For example, each time a development team requests access to test data, they should only get high-fidelity, de-identified, synthetic data. This ensures that even when that data gets lost or exfiltrated (under an ‘assume breach’ mentality), it’s not your real, sensitive data – just a collection of substitutes.

So, in this example, you’re not just focused on who can access the production data, but instead, you’re focused on ensuring the data itself – no matter who accesses or uses it – is not exposed.

This fundamental rethink not only shields you better from the potential fallout of a breach, but it also streamlines reporting and compliance to security standards, privacy and data residency rules (read the test data management best practices below to see how). 

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2. Avoid Copying Production Data

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The standard practice should be to provision high-fidelity synthetic data or production data that maintains referential integrity, preserves the structure and usability of the original data and has undergone de-identification for all testing activities.

This means replacing sensitive information with meaningful substitute values that both preserve structure and usability without exposing the real information. 

Moreover, high-fidelity synthetic data equips your development and QA teams to test real-world use cases without compromising on security.

With this approach, you avoid the practice of copying your live production data into less secure environments, thereby reducing the risks caused by data sprawl, such as broad attack surfaces and insecure repositories. 

That said, even if the de-identified test data ends up in a shadow or insecure repository, it’s made of substitutes that offer no value to bad actors who could find and exfiltrate it. So, there’s an inherent safety net built directly into the test data to protect you from the worst-case scenario. 

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3. Use Tools to Provision Protected Test Data in Real-Time

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The right test data management tools will allow you to provision high-fidelity synthetic data in real time or on the fly. With the right tools, there’s no compromise between data security and development agility. They simply make the whole process more efficient.

DataStealth’s Test Data Management solution, for example, can transform sensitive data from production environments into meaningful substituted values that maintain both the structure and characteristics of the original using de-identification. 

This anonymization process is done in real-time with practically no latency and without the need to duplicate the live production database at any point. Besides minimizing the risk of data leaks, this also helps save resources.

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4. Ensure Referential Integrity in the Test Data

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When provisioning synthetic data, the test data must maintain the same consistency and relationships of the original (e.g., business logic references, multi-table joins, etc) after de-identification – i.e., referential integrity.

Referential integrity ensures that the scripts, processes, and applications will still function as expected, even though the data is de-identified. This prevents test failures due to broken data relationships, saving significant time and effort for development and QA teams.

The right tools – e.g., DataStealth’s TDM Solution – will maintain referential integrity in your test data so that your teams can carry out large-scale realistic and reliable testing scenarios.

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5. Employ De-Identification and Localization to Comply with Data Residency Requirements

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Many organizations face challenges with data residency laws like GDPR, CCPA, and others, which dictate where personal data can be stored and processed.

These regulations can complicate global development and testing if teams need to use data across different countries, creating compliance risks when handling raw production data.

Synthetic data allows development teams to work with high-fidelity data that emulates the main statistical properties and structure of the real production data, but does not contain any sensitive information. This approach is inherently secure; even if the synthetic data gets breached and/or exfiltrated, it doesn’t offer any value to the bad actor because it’s artificial information. 

Besides equipping your home teams with inherently secure, high-fidelity test data, this approach also supports organizations with remote distributed development and/or third-party development teams located overseas. 

Many key markets have data residency and data sovereignty laws that require their residents’ data not to leave their borders, which can make supporting distributed teams difficult. 

However, synthetic data allows you to readily comply with these laws while also supporting your teams, no matter where they’re located, as you’re only provisioning artificial information that emulates the production data, but not the actual production data itself. 

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7. Use a Data Security Platform (DSP)

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Using separate tools for secure test data management (e.g., discovery, classification, protection or de-identification, and provision) often leads to inconsistent protection and makes it difficult to ensure test data is secure by default.

That’s why you should deploy a DSP that integrates data discovery, classification, protection (through vaulted data tokenization), synthetic data generation, and policy enforcement into one cohesive platform. 

The right DSP allows you to seamlessly discover and classify sensitive production data and apply protection in real time as test datasets are created. This ensures that all your sensitive data is protected and that your teams get high-fidelity de-identified test data that’s fully usable and reflective of the real world. DataStealth, in particular, protects the data by default, ensuring that every process or task happening after that rests on a breach-resilient foundation. 

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Next Steps

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Overall, adopting test data management best practices requires more than just a shift in your processes – you need the right technology.

A DSP like DataStealth, for example, provides tools for provisioning secure and high-fidelity test data in real-time or on the fly in one integrated platform.

With DataStealth, you will:

• Provision secure, high-fidelity test data instantly without slowing down development or compromising agility
• Support global dev and QA teams while staying compliant with GDPR, PCI DSS, and other data privacy or residency laws
• Minimize breach impact by design, ensuring test data is protected and valueless even if exposed
• Streamline compliance audits with built-in reporting and no-code deployment across any environment

Secure your test data. Accelerate your development. Schedule a call with our team! 

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