Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

One word defines success or failure in the race to adopt AI in security workflows: trust. While the industry moves fast toward automation and autonomy, adoption often stalls when developers and the teams supporting them can’t trust what the AI delivers. It’s not enough for a tool to explain what it did. Developers want to know: Did it actually fix the problem? Will this change break something else? Can I rely on it again next time? Nowhere is that skepticism more justified than in security.

Artificial intelligence is no longer a future consideration. It’s here — and it’s changing how software is built. Fast. Enterprise teams are moving beyond AI pilots and proof-of-concepts. They’re rolling out real-world, high-value use cases and doing it at scale. According to IDC forecasting, AI spend will more than double by 2028. At the center of that surge is AI-assisted software development.

Software development is undergoing its biggest shift since the rise of cloud and DevOps. The difference this time? The shift is being driven by artificial intelligence, and it’s moving fast. AI-powered coding tools have rapidly made their way into developer workflows. Agents and LLMs are helping teams move faster, automate more, and build in entirely new ways. But speed often comes with tradeoffs.

Software development is in the midst of a monumental shift, powered by the rapid advancements in Artificial Intelligence. AI isn't just changing how we build software; it's transforming the very nature of applications themselves. As AI-native applications become more prevalent, we're also seeing new, complex security threats emerge. Traditional security approaches aren’t designed for the dynamic and often unpredictable nature of Large Language Models (LLMs), agents, and other AI-driven systems.

Having recently reached the 100 day mark of the new administration in the U.S., Snyk thought it important to check in with some of the nation’s CISOs to gauge how they were feeling about the cybersecurity posture of both the nation and their own organizations. In partnership with Researchscape, we surveyed a total of 101 U.S. based CISOs working in Information Security and/or Information Technology who responded to the online survey from April 7 - 21, 2025.

When developing a web application, dev teams can choose from two fundamental design patterns: Single-Page Applications (SPAs) or traditional Multi-Page Applications (MPAs). Deciding which one to use can depend on multiple factors, but more and more companies are developing SPAs since they can provide a smoother user experience (UX), which, in turn, might just result in better user adoption.

At RSAC 2025, Snyk focused on the future of cybersecurity, particularly the impact of generative AI on software security. We connected with key leaders and partners to advance this discussion and anticipate further progress in 2025.

Snyk Learn, our developer security education platform, now includes lessons on risks in open source software! Check out the new learning path that covers the OWASP Top 10 risks for open source software. Open source software forms the backbone of today’s digital infrastructure. From libraries and frameworks to entire platforms, open source is everywhere. But as a famous uncle once said, with great power comes great responsibility!

The rapid adoption of AI coding assistants is transforming software development in ways both good and bad. Developers can produce more code faster than ever with AI, and 96% of developers report using AI coding assistants to streamline their work. AI code generation is becoming mainstream, and in late 2024, Google reported that AI writes more than 25% of its code. While GenAI tools increase productivity, they’re also creating more work for application security teams.