A deployment goes out late at night. Everything seems fine at first. The dashboards are green, there are no alerts, and the release looks clean. A few hours later, the latency starts to increase. Nothing is critical. No alerts go off. By the time users notice, the system is already stressed. In a typical case, someone gets paged, checks the logs, reviews recent changes, and the team starts to connect the dots manually. It works, but it is slow and reactive. Now think of a different setup. The same pattern starts. Instead of waiting for things to break, an AI agent notices something is off. It connects it with a deployment, finds a likely cause, and takes action before users feel the impact. This is where modern DevOps is headed. With the rise of tools like Claude agents, the conversation is shifting from automation to autonomy. The question is no longer if AI can help DevOps. The question is whether it can take over a lot of it. From Defined Pipelines to Adaptive Systems ...
The XZ Utils backdoor was a wake-up call, but the underlying problem it exposed has not gone away. Sophisticated adversaries are playing the long game, spending months or years earning trust within open source projects before introducing malicious code into libraries that sit at the foundation of modern software infrastructure. Mike Vizard and Josh Bressers, VP of security at Anchore, dig into why the software supply chain remains dangerously vulnerable and what the industry is getting wrong in its response. Bressers points out that the vast majority of open source projects are maintained by a single person or a very small group of volunteers. These maintainers are often overworked and under-resourced, managing critical dependencies that thousands of organizations rely on in production. When an attacker targets one of these projects, the maintainer is the entire security perimeter. No amount of scanning or compliance tooling downstream can fully compensate for a compromise that h...