Operations | Monitoring | ITSM | DevOps | Cloud

Shadow AI on company computers often starts with an AI agent that an employee installed on their own, signed in with a personal subscription and connected to internal systems through a Model Context Protocol (MCP) server. Shadow AI is one branch of shadow IT, with a shorter path from install to impact: these tools set up quickly, act on files and systems, and work under whatever account the employee chose. This guide skips the long definitions and goes to the practical part.

ClickHouse monitoring guide: the Prometheus endpoint, the system tables that explain slow queries, the Too many parts limits, replication lag and alerts. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

GKE monitoring guide: what Google Kubernetes Engine collects by default, which metric packages to turn on, Autopilot limits, and the alerts to set. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

✓ operational truth IT finds out late because traditional monitoring is built to detect what goes wrong, not what has quietly stopped happening. Closing that gap requires observability that validates business journeys end to end, detects missing activity, checks its own coverage, and predicts degradation before a threshold is ever crossed.

Most teams that run on Azure do not run only on Azure. There is a database on a VM nobody wants to move, a Kubernetes cluster somewhere else, a few servers in a rack, and a monitoring setup that grew around all of it. Azure Monitor sees the Azure part very well and nothing else, so the picture of an incident ends up split across two consoles, two alerting configurations and two sets of dashboards. Bleemeo now connects to Azure the same way it already connects to AWS.