Sensitive data sprawl into non-production environments isn’t new — it’s been a quiet reality of enterprise IT for two decades. What’s changed is the scale and the surface area. DevOps velocity, analytics workloads and now AI training pipelines have multiplied the number of places sensitive data lands and the speed at which it gets there.
According to the Perforce Delphix 2025 State of Data Compliance and Security Report, 60% of organizations experienced a breach or data theft in non-production environments last year and 95% reported growth in sensitive data outside production. At the same time, 84% still allow compliance exceptions in these environments, allowing the hidden risks to seep in.
Why the risks in non-production environments are growing
The numbers tell us this is happening at scale. The mechanics tell us why.
As Ilker Taskaya, Field CTO at Perforce Delphix explains: speed drives these decisions to spin up these environments, but scale is what amplifies the risk. What begins as a single masked dataset quickly becomes dozens of copies, accessed by distributed teams, tools and partners — often without consistent policy enforcement.
And the bigger problem? 84% of organizations continue to allow data compliance exceptions in their non-production environments, often in the name of speed. What begins as a compliance exception quickly becomes the default, creating unmanaged risk at scale.
“Compliance exceptions are often framed as the price of developer productivity — but they don’t have to be,” explains Ilker Taskaya, Field CTO at Perforce Delphix. “Intelligent data automation platforms — combining masking, synthetic data generation and virtualization — can deliver full-size, de-risked environments or synthetic datasets that mimic production-like output without being derived from production data.”
How organizations with a mature security posture approach the hidden risk of non-production data
Taskaya argues that the enterprises pulling this off don’t think about non-production environments one at a time. They think in loops — which is what lets them future-proof their security posture as regulations and operating regions evolve.
“The organizations that succeed at speed and scale while still protecting data run a closed-loop process,” he says. “Instead of treating each environment as a one-off, they define what matters at the enterprise level — say, 18 attributes that need to be protected across every application — and tie the tooling directly to their governance model. The policy is the control plane. Whether it’s derived from country, local, or industry regulation, or from internal standards, the same policy is applied consistently across every enterprise application asset — even when local variations are layered on top for specific jurisdictions.”
Taskaya continues, “When the policy changes, the assets get re-profiled and re-protected automatically — the next execution of the tooling already reflects the change. And the loop closes the other way too: the tooling reports back what’s protected and when, so risk posture is monitored continuously rather than audited after the fact.”
This closed-loop model is what Perforce Delphix is built around. In the Delphix DevOps Data Platform, Data Control Tower (DCT) serves as the unified control plane — bringing data masking, AI-powered synthetic data generation and data virtualization under a single governance layer, so the same policy that defines what’s sensitive also drives how data is protected, provisioned and reported on across every non-production environment.
Success story: how a Fortune 500 company eliminated the hidden risks of non-production data
Molina Healthcare, a Fortune 500 managed care company, the challenge wasn’t just protecting patient data. It was doing so across dozens of non-production systems supporting development and testing.
To address these challenges, Molina Healthcare chose Delphix to automate masking and data delivery, ensuring protected health information (PHI) data in non-production environments. Today, Molina has centralized policy enforcement while giving teams self-service access to compliant, production-like data. In addition to compliance, they gain speed, cutting project timelines in half without expanding risk.
The data privacy compliance challenge is a common one that Taskaya sees frequently with his customers. As sensitive data quietly proliferates into development, testing and analytics, many organizations lack repeatable processes to manage exposure at scale.
To address this, Taskaya emphasizes embedding data protection into everyday operations, starting with three fundamentals: fostering a data-conscious culture, consistently enforcing policies beyond production and closing the loop with centralized data management processes that ensure non-production data doesn’t fall through the cracks.
Secure what happens beyond production
Enterprises can no longer afford to treat non-production environments as a necessary risk of innovation. As data fuels DevOps and AI at unprecedented scale, the weakest controls increasingly live where the most work happens. Securing non-production data isn’t about slowing teams down. It’s about enabling speed with confidence, protecting sensitive information without undermining quality or delivery timelines.
Delphix is the industry leader in automated, compliant data delivery. Learn more about how Delphix’s data masking tools can make your non-production data fast, trusted and AI-ready.
