Operations | Monitoring | ITSM | DevOps | Cloud

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.

Most IT operations teams cannot say how much of the MITRE ATT&CK framework they already cover. The framework gets explained in the language of threat hunting and red teams. The parts that belong to infrastructure work are easy to miss. And then, coverage questions get answered with a guess. The mismatch costs time on both sides. Security asks for a coverage answer that ops has no clean way to produce. Yet the controls that stop a large share of those techniques already sit with your team.

What do you say when an auditor asks for evidence that your security controls hold, and all you can produce is a scan report from last month? A scan lists weaknesses. It says nothing about whether an attacker could chain three of them together and reach the customer database. Vulnerability assessment and penetration testing answer two different questions about the same environment. The first asks what is exposed right now. The second asks what someone with intent and skill could do with that exposure.

Azure Virtual Desktop (AVD) is rapidly growing in popularity as modern way to deliver virtual desktops and apps to users, with Azure providing the infrastructure as alternative to on-prem VDI environments. As organizations expand their use of AVD in Azure, monitoring becomes critical.

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?