Operations | Monitoring | ITSM | DevOps | Cloud

Impossible travel and new-country sign-in alerts are famously noisy. The reasons why, the difference between the Entra ID and SharePoint signals, and what separates a real account takeover from proxy egress.

Imagine if your workday started as soon as you woke up. Before you can even start your coffee maker, email alerts are flooding in, coworkers are pinging you on Slack, and your phone is buzzing nonstop with reminders. You haven’t even pulled the covers back, and your boss is asking you about deliverables. This is what Kubernetes pods deal with every day. Unless, that is, you use readiness probes.

In 1846, Britain poured roughly 7% of its national income into railways, proportionally about three times what the U.S. spends on AI infrastructure today. The technology delivered everything it promised, and a generation of investors still lost their shirts. What sorted the winners from the wreckage wasn't conviction about the technology; it was whether ROI was measured or asserted. The man who fixed that problem gave his name to the S&P 500.

When engineering teams evaluate AI agents, the first questions are usually about capability. Which model performs best? How much faster can it write code? What's the return on investment? Security, if it enters the conversation at all, tends to come later. Patrick Dawkins, Principal Software Engineer at Upsun, thinks that's backward. Over the past year, he's been building the infrastructure that enables AI agents to operate safely within engineering teams.

AI is changing artifact management in two ways at once. Every AI-generated pull request, dependency update, and automated build creates more container images, packages, and Helm charts than ever before. Registries are growing faster than engineering teams can manage them, driving up storage costs and leaving thousands of stale artifacts behind. At the same time, the cost of deleting the wrong artifact has never been higher.

Cloud cost visibility at scale usually works great… until it suddenly doesn’t. At first, everything feels manageable. You can track spend by service. You know which team owns which resources. Reports are clean, and the numbers make sense. Then one day, there’s a $47,000 spike spread across three AWS accounts that no one noticed for eleven days. Leadership wants answers. Engineering wants context. And your carefully designed tagging strategy?