Operations | Monitoring | ITSM | DevOps | Cloud

Investigation is the most time-consuming and cognitively demanding phase of incident response, and it’s the phase least served by existing tooling. Modern ITOps teams have spent years investing in better detection and alerting. The tools are faster, the dashboards are richer, and anomaly detection keeps improving.

Recently, someone talked Chipotle’s customer support agent into reversing a linked list – a task completely unrelated to burritos in any way. Screenshots circulated, people laughed, but underneath the joke sat a sharper question. If a production support agent will do that on a public channel, what else will it do that nobody is screenshotting? The bug is funny. The trust gap behind it is not.

Engineering leadership is in the middle of a real transition, and most of the leaders I talk to know it. AI has reshaped how software gets built quickly enough that the operating models many of us spent a decade refining no longer fit cleanly, and there is a great deal of serious work happening across the industry to figure out how these models should evolve. The teams I find most impressive right now are the ones treating their operating model as an open question rather than a settled one.

Canonical’s security philosophy has always been built on the premise that vulnerabilities exist and will be discovered. Our response relies on defense-in-depth architecture, rapid patch deployment, and strict adherence to Coordinated Vulnerability Disclosure (CVD). AI changes vulnerability discovery volume and speed. We have a robust vulnerability management process that is backed by rigorous compliance certifications.

AI pricing covers the cost structures and billing models providers use to charge for AI products: per-token APIs (GPT-4o at $2.50/1M input tokens), per-seat subscriptions (Copilot at $30/user/month), per-conversation billing (Agentforce at $2/conversation), and consumption-based GPU compute (H100 instances at $55.04/hour). There is no standard. The total AI cost is almost always higher than the sticker price.

Kellyn Gorman is a Database and AI Advocate and Engineer at Redgate She's the previous director of Data and AI at Silk, and the Oracle SME in Azure at Microsoft. With a robust background in cloud technology and a passion for promoting its merits and potential, I am thrilled to spearhead conversations and actions that help shape the future of this industry. Kellyn has authored numerous technical books, white papers and solution repositories in GitHub on database, AI and engineering topics.

In recent years, the conversation about digital sovereignty has focused on data. Where it is stored, who can access it, and which laws apply when something goes wrong. Those questions still matter, but AI is forcing us to think about sovereignty in a different way.

Ask three teams what assets exist in your environment, and you’ll get three different answers. Most organizations don’t lack tools. They lack agreement on what actually exists in their environment. Asset, endpoint and cloud data exist — but it’s fragmented, stale and trusted differently by teams across every department and function.