Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

You’re evaluating AI workload security tools and every demo looks the same. Vendor A shows you an AI-SPM dashboard. Vendor B shows you a nearly identical AI-SPM dashboard with slightly different branding. Vendor C shows you posture findings with an “AI workload” tag that wasn’t there last quarter.

If you’re like me, you’ve been enthralled with the recent story, expertly written by Sean Hollister at The Verge, about how Sammy Azdoufal built a remote control for his DJI Romo vacuum with a PlayStation controller, and ended up in control of 7,000+ robovacs all over the world. On the surface, it sounds like vibe coding gone slightly sideways. I mean, really, what could a vacuum possibly do? Turns out… a lot.

AI coding assistants are quietly resurrecting millions of abandoned open source packages. For the last decade, developers relied on a simple heuristic for open source security: Prevalence \= Trust. If a package was downloaded millions of times a week (lodash, react, requests), we assumed it was "safe enough" because thousands of eyes were on it. If it was obscure, we approached with caution.

In 2026, the digital landscape has shifted from a world of "AI assistants" to one of autonomous operators. For managed service providers (MSPs), this evolution marks the end of the traditional "land and expand" human services playbook and the beginning of a high-speed era of machine-on-machine warfare.

AI tools are quickly becoming part of everyday business workflows. From chatbots to automation tools, large language models now handle sensitive tasks and data. But with this growth comes new security risks. One of the biggest emerging threats is the prompt injection attack, in which attackers manipulate inputs to cause AI systems to ignore their original instructions. Unlike traditional cyberattacks, this method exploits weaknesses through language rather than code.