Systems | Development | Analytics | API | Testing

The challenge of any aerospace company is to deliver new capabilities without compromising safety, reliability, or precision. At our current juncture, legacy technology runs into conflict with modern tool stacks. Artificial intelligence (AI) creates fissures in compliance and auditability, and innovation and productivity gains come at a cost of greater complexity. Despite these seismic shifts, the central question remains the same.

Agile ALM manages the entire application lifecycle, including requirements, development, testing, and release, using Agile principles while maintaining end‑to‑end visibility and traceability. It supports iterative delivery, continuous feedback, and changing requirements to ensure that every decision and change is connected, auditable, and aligned with business and regulatory needs. The benefits of Agile ALM include.

Data masking comes in different forms: dynamic vs. static masking. Each has its own characteristics, use cases, and methods for data protection. But when it comes to comprehensive, consistent protection, static data masking rises above. In this blog, we’ll break down where dynamic data masking works, how it fails, and which use cases you need to use static masking for.

In my experience working with enterprise leaders, the journey to the cloud rarely follows a straight line. Many organizations set ambitious goals to move all operations to the cloud. They quickly find that certain legacy systems must remain on-premises. This reality results in a complex, hybrid multicloud environment. That means they need to adopt a new strategy for managing test data.

According to the Perforce 2026 State of DevOps report, 77% of organizations express high confidence in the outputs generated by their artificial intelligence systems. Yet, this widespread optimism masks a critical vulnerability. While executive confidence in AI results remains high, only 38% of organizations have embedded AI deeply across their delivery stages. Plus, only 39% maintain the fully automated audit trails required to verify these results.

Security expectations for version control infrastructure have evolved dramatically over the years. While Perforce P4 has always empowered administrators with deep configurability, the default configurations shipped with previous versions of P4 are no longer sufficient. With the upcoming P4 2026.1 (scheduled for availability in May), we are implementing a Secure by Default posture designed to enforce best practices when protecting the source code and binary assets stored in P4.

For decades, software engineers have relied heavily on C and C++ to build embedded systems. These legacy languages offer the deep control and speed required for constrained environments, but they reveal gaps in memory management and concurrency. The Rust programming language has emerged as a solution.

Unaccounted data copies within non-production environments can make enterprises vulnerable to cyber theft. Non-production environments — which are often less secure than production environments — are treasure troves for hackers seeking to steal customer data. How many copies of test data are currently floating around your organization’s non-production environments?

FTI audits are designed to ensure sensitive tax data is properly protected. But in modern enterprises, they’re about much more than passing inspections. Today, you need to manage FTI securely while still enabling fast, reliable access to data across DevOps, analytics, and increasingly, AI workflows. Treating FTI audits as part of a broader data strategy helps teams reduce risk without slowing innovation or creating bottlenecks.