Systems | Development | Analytics | API | Testing

Drone technology — also known as unmanned aerial vehicle (UAV) and unmanned aircraft system (UAS) technology — is expanding rapidly across commercial, industrial, and defense sectors. As these systems become more complex, they increasingly rely on sophisticated software for aerial embedded systems.

Your approach to Guidewire data masking could be inhibiting innovation at your company. Insurance companies have been racing to accelerate digital transformation. But I’ve watched many neglect to properly protect sensitive information in their environments. The risk is real and immediate. Every development cycle, QA test, and sandbox refresh becomes a potential compliance violation. Or worse, it could lead to security gaps for bad actors to exploit.

Data obfuscation offers a powerful solution to today's enterprise security challenges — and the stakes couldn't be higher. IBM reports that the global average cost of a data breach crossed $4.88 million in 2024. And 60% of the organizations we surveyed for our State of Data Compliance and Security Report have experienced data breaches or theft in non-production environments — up 11% from last year.

Deterministic masking is essential for any businesses that need to secure application data across multiple non-production environments — since it ensures data is masked consistently everywhere it appears. As CTO of Perforce Delphix, I’ve worked with many companies who need to protect sensitive data while providing realistic data for testing and development. This is especially important in industries like insurance, healthcare, and financial services.

Test data compliance efforts are falling behind development speed, creating a dangerous gap exploited by bad actors and scrutinized by regulators. It's a wake-up call: Dev and test environments are under stricter regulatory scrutiny than ever. In my role as Senior Product Manager for Delphix, I regularly work with enterprise teams who are discovering this reality the hard way.

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?

To use synthetic test data or to use test data masking — that is the question. But the answer may not be what you expect. Before we dive into that, what’s happening in today’s business landscape that’s prompting the question around synthetic vs. masking? Delivering high-quality applications at lightning speed is expected in today’s CI/CD world. Fast time-to-market is at odds with security and compliance requirements.

Ephemeral test environments have surfaced as a solution to DevOps teams’ growing challenges. Dealing with spiraling cloud costs and infrastructure maintenance is only getting more complex. Development teams find themselves competing for limited or stale environments while datasets grow larger. As a result, development velocity suffers. Application teams need realistic data for effective testing.