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 sensitive data.
The findings, summarized in our State of Data Compliance and Security Report, surprised us. Did you know that 54% of organizations have already experienced a data breach or loss involving data in their non-production environment? And 52% of organizations have already experienced a data compliance audit issue or failure involving that same data?
Many businesses have large sensitive data footprints. This 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 you may be wondering what importantly, how should your organization go about protecting sensitive data? Read on to learn how to ensure your business’s sensitive data protection for these vulnerable non-production environments.
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 Sensitive Data Protection 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, 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 this is. So The State of Data Compliance and Security Report drilled into this particular topic in detail. We found out that for 75% of organizations, the volume of sensitive data stored in their non-production environments grew over the past year. But more importantly, 91% of respondents were concerned about this expanding exposure footprint in their non-production environment.
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 data volume in non-production:
- AI and ML advances (60%)
- Digital transformation (53%).
- Increased digital customer interactions (53%).
- Increased use of data to drive decision-making (49%).
- Cloud adoption (44%).
- Application development (37%).
- Great use of non-production environments (27%).
The State of Data Compliance and Security Report
Sensitive data is growing, and protecting it is becoming more challenging. Find out what enterprise leaders are doing to protect sensitive data in non-production environments. Get your copy of the report now.
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You may be wondering how prevalent exactly is sensitive data in these non-production environments? Our findings were quite staggering. In the report, respondents reported that the following environments at their organizations contained sensitive data:
- Data analytics environments (99%).
- Software testing environments (97%).
- Software development environments (94%).
- Artificial intelligence (AI) / machine learning (ML) (93%).
With such high rates of data in lower environments, one thing is clear. The sensitive 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 cited several challenges related to protecting sensitive data in non-production environments:
- Ability to track and comply with ever-changing regulations (38%).
- Impacts on software quality (36%).
- Difficulty in implementing and managing protective measures (33%).
- Impacts on speed of software development (32%).
- Time consumption (27%).
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 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 86% 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 data. But static data masking is the only irreversible data anonymization technique, and it is the best way you can protect your sensitive data. In the report, 66% of respondents said they currently use it. 97% of organizations said static data masking was valuable to them in protecting sensitive data in non-production environments, while 100% said static data masking is effective at doing so.
However, the question in my mind is, why do only 66% currently use static data masking? I say this only slightly tongue in cheek. More seriously, this finding suggests a widespread awareness gap as to this avoidable risk. Are the remaining 34% just not protecting data in lower environment? Or are they perhaps using reversible methods like encryption or dynamic data masking to protect sensitive data in lower environment, taking on unnecessary risks?
At 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.
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How Delphix Makes Protecting Sensitive Data Easy
Delphix is a powerful way to protect sensitive data in non-production environments.
With Delphix, you can discover and irrevocably mask sensitive data, governed by an ever-expanding regulatory landscape, eliminating regulatory and security risks in development, testing, analytics, and AI environments. We mask data using a rich library of pre-built and customizable algorithms to adhere to all external regulations and internal policies.
We ensure that masking does not slow down innovation by automating and accelerating the discovery and delivery of masked data at enterprise scale, from mainframe to cloud. We typically hear from customers that our solution accelerates their software development because we not only speed up the masking process itself, we automate the delivery of ephemeral, masked test data to development and test teams, when and where they need it, allowing them to test often and early, shift left and accelerate.
Finally, we help organizations overcome the compliance and quality trade-off by delivering production-like, compliant test data. We consistently discover and mask sensitive data by permanently replacing sensitive data with fictious but realistic data, preserving data utility and referential integrity across data sources, thus ensuring high-quality test results and better-quality software.
With Delphix, you can have the peace of mind that your sensitive data is protected in lower environments, without blocking innovation or hurting quality. No trade-offs necessary!
Request your compliance demo today to see how Delphix protects sensitive data across non-production environments.