Systems | Development | Analytics | API | Testing

Sovereignty is no longer a compliance debate. It is the operating condition for running data and AI in production. 89% of organizations in our 2026 survey of 320 enterprises rate data sovereignty as very or rather important. Only 38% have governance mature enough to survive a real AI production incident. That is the gap. It sits exactly where data and AI architecture meet enterprise control, and it is the central tension of the year.

Like many other countries, India has made moves to protect consumers’ data. Comparable to the European Union’s General Data Protection Regulation (GDPR), India’s Digital Personal Data Protection Act (DPDPA) establishes new, higher standards for data privacy, timely breach notification, and consent management.

Cyber insurance has become a standard line item in enterprise risk management, and for good reason. The financial consequences of a significant cyber event, whether a ransomware attack that halts operations for weeks or a data breach that triggers regulatory scrutiny and third-party liability, can far exceed what any operational budget was sized to absorb. Insurance exists to handle that tail. Most organizations recognize this benefit and carry a policy.

Software teams are under constant pressure to release faster. Yet testing, the safeguard that protects quality, has not kept pace with modern delivery speeds. More code and shorter sprints overwhelm QA capacity, while fragmented tools and late-stage performance checks create bottlenecks that slow everything down. The question is not whether testing needs to evolve. The question is how to evolve without a costly rip-and-replace of your existing stack.

Static analysis has always excelled at finding defects, vulnerabilities, and compliance violations. Before AI-assisted code remediation, however, developers still had to research the root cause, design a fix, and manually verify that the correction satisfies the relevant requirements. The new, built-in AI-assisted code remediation feature speeds up this process.

At Perforce Delphix, we have found that referential integrity is very often a deciding factor for enterprises evaluating data masking and test data management solutions. That same requirement is emerging in conversations about synthetic data, as well. Mayank Ahluwalia, Senior Product Manager at Perforce Delphix, has seen this need firsthand in his conversations with enterprise leaders.

Perforce Intelligence is in its next phase to help our customers and partners get solutions, platforms, and features that bring control at every stage of the AI software development lifecycle (AI SDLC). Read about the evolution of Perforce Intelligence and new platforms and features we’ve rolled out to make AI easier to adopt for production-ready environmental needs. Back to top.

Since the first vehicles were rolled out to customers, automakers have competed to deliver the newest features and the greatest benefits to the driving experience. Today, that competition is less about shaping a car’s physical characteristics and more about making cars smarter and more connected to the world around them. With thousands of car models and trim levels available worldwide, there is a fierce need to find new ways to stand out from the competition.