Protecting Sensitive Data in Non-Production Environments: No Trade-Offs Necessary!

Yes, you’ve heard it all before: the frequency of cyberattacks and their devastating aftermath, organizations’ gaps in protecting sensitive data, and the financial consequences of not complying with GDPR and the likes. I am not here to share any old news. 

But there is a risk that is not discussed frequently enough in the news. And it should be. How often do you suppose data in non-production environments is compromised or fails compliance audits? We wondered that ourselves, so we did our own research on protecting production data in lower environments. 

The findings, summarized in our 2026 State of Data Compliance and Security Report, surprised us. Did you know that 98% of leaders are confident in their ability to protect sensitive data in non-production, yet 43% have already experienced an audit failure and 34% have experienced a breach or theft involving that same data? Yet, 84% of organizations continue to allow data compliance exceptions in non-production environments.

As confirmed by CSO Online reporting, high-profile incidents continue to put a spotlight on non-production environments too. Securing non-production environments is a challenge. In our annual research, we seek to answer why it's such a challenge and what top enterprises could do differently.

For example, many businesses have large sensitive data footprints. That data is created in production environments, but sensitive data sprawls from production environments to non-production environments — like software development, analytics, and AI — where it multiplies, increasing the organization’s threat surface. At the same time, these non-production environments are typically less controlled or governed and are therefore much more exposed. 

But more importantly, how should your organization go about protecting sensitive production data in lower environments? Read on to learn how to ensure your business’s sensitive data protection.

What is Sensitive Data?

Sensitive data refers to information that must be protected and kept confidential. Sensitive data may be governed by data privacy regulations, internal corporate policies, or both. Examples of sensitive data include: 

• Personally identifiable information (PII): Any data that can identify a person.
• Protected health information (PHI): Any data that can identify a patient.
• Biometric data: Unique biological data such as face, voice, and fingerprints.

In recent years, regulations surrounding sensitive consumer data have been launched globally and come to the foreground. These include the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and many more global government and industry regulations. These regulations followed on the heels of the United States’ Health Insurance Portability and Accountability Act (HIPAA), which governs the protection of patient information.

Why is Protecting Sensitive Data Increasingly Important?

Sensitive data protection is gaining more importance for a few reasons: 

• Compliance enforcement is getting stricter as data privacy risks grow.
• Data breaches are becoming more sophisticated.
• The consequences of regulatory compliance failure and of data breaches are grave for any business, both financially and reputationally. 

But you may be wondering, how does all this translate to protecting sensitive data in non-production environments? 

Why is Sensitive Data Volume Rising in Non-Production?

Sensitive data footprints in production are growing due to many reasons, with digital transformation being a major contributor. That’s no secret, and this makes data protection more challenging. 

But this problem is greatly exacerbated in non-production. Based on what we hear from our customers, in a typical enterprise every production database has 7–10 copies in non-production. So, this sensitive data exposure is one of exponential proportions. 

We wanted to find out exactly how much of a problem non-production data is, so the State of Data Compliance and Security Report drilled into this particular topic in detail. We found that 57% of organizations reported an increase in the volume of sensitive data stored in non-production environments over the past year. In addition, 84% said they are concerned about the expanded exposure footprint created by sensitive data in non-production environments.

You may already be guessing the reasons for this increasing footprint. But in case you are wondering, there are several reasons for this growth of sensitive production data in lower environments, the top ones being:

• Increased use of non-production environments (31%)
• Increased use of data to support decision-making (30%)

With such high rates of production data in lower environments, one thing is clear: The sensitive non-production data must be protected.

Why is Protecting Sensitive Data in Non-Production Environments Difficult? 

Sensitive data is multiplying, but so are the non-production environments in which it’s often found. This makes protecting sensitive data in those environments difficult.

Sensitive data is often created and stored in production environments. Access to data in these environments is secure, with tight controls. But during standard IT operations, one production dataset often gets copied many times over into non-production environments.

Once in non-production environments, more workers have access to sensitive data. This data is also no longer subject to the same strict security controls, so it’s more vulnerable to both external and internal bad actors.

That’s concerning by itself. But multiple non-production environments exist for every production environment. Securing sensitive data copies in each non-production environment consumes time and effort. It takes even longer with manual processes and coordination across teams. Collectively, this weakens product quality and slows down innovation.

According to our study, the exposures and risks are well understood and are cause for concern for our audience. So, you might be wondering, why aren’t all organizations protecting sensitive data in non-production? 

There are a number of reasons for this hold-up, as we uncovered in our study. Respondents identified several barriers to protecting all sensitive data in non-production environments, primarily:

• Concerns that data quality will be degraded (24%)
• The effort required to implement protection at scale (23%)

Looking at these challenges brings back a familiar trade-off dilemma: Can I improve the security and compliance of my systems without slowing down innovation or hurting non-production data quality? 

Common perceptions may suggest that you cannot. Even though DevOps has certainly made progress on improving the trade-off dilemma in general, unfortunately DevOps has not really touched data practices as much. 

Consequently, I suspect that many teams forgo sensitive data protection in non-production to avoid slowing down their releases or introducing more software defects. Or maybe they request an exception from the CISO. In fact, our data confirms exactly that.

We were quite shocked to learn that 84% of organizations allow data compliance exceptions in non-production. This staggering statistic is likely a contributing factor to our findings shared at the top of this blog regarding the extent of breach, theft, and audit failures associated with non-production data.

In my view, organizations that grant these exceptions are taking avoidable risks, and I believe that there is a better way. Let’s talk about that in the next section. 

How You Can Protect Sensitive Data 

Many enterprises use data masking and anonymization to protect their sensitive production data in lower environments. Static data masking is the only irreversible data masking technique, and it is the best way you can protect your sensitive data. In the 2026 State of Data Compliance and Security Report, 86% of organizations reported using static data masking to protect sensitive data in non-production environments.

However, the question in my mind is, why do 84% allow data compliance exceptions in their non-production environments? I say this only slightly tongue in cheek. More seriously, this finding suggests a widespread awareness gap as to this avoidable risk. Considering that 34% of organizations have experienced breaches or theft in non-production environments and 43% have experienced audit failures, it is alarming that so many organizations continue taking on unnecessary risks? 

At Perforce Delphix, we believe that organizations should be statically masking sensitive data in lower environments, as a best practice. And we believe that there are ways to do that without making the dreaded trade-offs with speed and quality. Let’s discuss that in the next section.

Discover more >> What is Delphix?

Examples of Protecting Sensitive Data in Enterprises

IDC Study*

Organizations that automate compliant data delivery don’t just reduce risk — they move faster. A recent IDC study of Delphix customers found that teams using Delphix:

• Achieved 58% faster application development.
• Protected 77.2% more environments.
• Realized a 408% ROI with payback in under six months.

*Source: "IDC Business Value White Paper, sponsored by Delphix, by Perforce, The Business Value of Delphix, #US52560824, December 2024"
 

UniSuper Secures Sensitive Data While Improving Testing Efficiency

UniSuper, one of Australia’s largest superannuation funds, needed to protect highly sensitive member data without slowing development and testing. By implementing automated data masking in non‑production environments, UniSuper significantly reduced manual risk, accelerated environment refreshes, and improved testing efficiency — proving that security and speed don’t have to be trade‑offs.

Molina Healthcare Accelerates Development While Maintaining HIPAA Compliance

As a large healthcare provider operating under strict HIPAA requirements, Molina Healthcare faced growing risk from sensitive PHI in non‑production environments. With automated data masking and faster provisioning, Molina cut environment setup times from days to minutes while ensuring patient data remained protected throughout the application lifecycle.

Proximus Protects Sensitive Data at Enterprise Scale

Proximus, a leading telecommunications provider, needed to support large‑scale testing while reducing the risk of exposing sensitive customer data. By automating data masking across non‑production environments, Proximus dramatically reduced masking time and storage footprint — enabling faster, safer testing without compromising compliance.
 

How Perforce Delphix Makes Protecting Sensitive Data Easy

Perforce Delphix gives you a powerful, automated way to protect sensitive data across non-production environments, without slowing down innovation.

With Delphix, you can automatically discover and irrevocably mask sensitive data across an ever‑expanding regulatory landscape, reducing compliance and security risk in development, testing, analytics, and AI environments. You apply masking using a rich library of pre-built and customizable algorithms, helping you consistently enforce external regulations and internal policies at enterprise scale.

Want to see how non-production data masking works? Watch this quick demo from my colleague Felipe Casali and see for yourself how easy it is to discover sensitive data in Delphix. 

Delphix ensures compliance never comes at the expense of speed. You automate and accelerate the discovery, masking, and delivery of compliant data—from mainframe to cloud—so teams can move faster with confidence. Customers consistently tell us that Delphix accelerates software delivery because you’re not just masking data faster; you’re automating the delivery of ephemeral, masked test data to development and test teams exactly when and where they need it. This enables you to test earlier and more often, shift left, and accelerate application releases.

You also eliminate the traditional tradeoff between compliance and data quality. Delphix delivers production-like, compliant test data by permanently replacing sensitive values with fictitious but realistic data — while preserving data utility and referential integrity across systems. The result is high-quality test data that supports accurate testing, better outcomes, and higher-quality software, all while reducing sensitive data exposure in non-production environments.