Operations | Monitoring | ITSM | DevOps | Cloud

A secure Shopify checkout doesn't mean your entire store is secure

Recent Shopify checkout hijacking attacks reveal an important security risk that many online retailers may not realise exists: the difference between Shopify itself, the apps installed on a store, and the software customers interact with before reaching checkout. In this article In October 2026, customers visiting legitimate Shopify stores were redirected to fraudulent checkout pages designed to steal their payment details. The stores were real. The products were real.

How SGP.32 unlocks next-generation IoT deployments

Every time a smart utility meter transmits consumption data, a connected vehicle updates its telemetry, or a fleet of logistics trackers crosses international borders, seamless and uninterrupted connectivity is vital. Yet for years, managing Internet of Things (IoT) devices at scale has been constrained by physical SIM logistics, operator lock-in and remote SIM provisioning standards that were simply never built for autonomous, resource-constrained hardware.

AI wrote the bug. You need production data to reproduce it

A pull request lands with a function an AI assistant generated. It reads cleanly, passes the existing tests, and gets merged. Three weeks later it breaks, but only for accounts with a specific billing history. Nobody wrote a test for that condition because nobody was thinking about it when the code went in. Now someone has to debug logic they didn't reason through, against a failure they can't picture without the exact data that triggered it. Debugging someone else's code has always worked this way.

Autonomous IT: Data, Context, and Governed Action | 2H Announcement from LogicMonitor

Autonomous IT requires three things: complete, AI-ready data; connected context; and governed action. In LogicMonitor's 2H announcement, we show enterprise IT and ITOps leaders how each layer works. LogicMonitor is the AI-first platform for Autonomous IT. This update covers the three layers behind autonomous IT operations as the category moves from systems of intelligence to systems of action. Your team decides when Edwin AI moves from recommending an action to taking it.

LM Logs Tips: Alert-to-Logs Workflow for Ops Team | Logs for Lunch

LM Logs troubleshooting starts at the alert. This Logs for Lunch session shows ITOps teams an alert-to-logs workflow and lesser-known LM Logs tips. Ryan Matuszewski demos a Tomcat application error. From the alert's Logs tab, he moves through log anomaly detection, All Logs, Log Patterns, and Log Analysis sorted by sentiment. He then opens the Logs page with the alert time range, and the same steps work for any alert type.

Cloud Log Monitoring & Root Cause Analysis | Logs for Lunch

Cloud log monitoring in LogicMonitor connects a service alert to the log event behind it. This Logs for Lunch July demo is for ITOps, CloudOps, and SRE teams. Tom Chavez hosts Ismath Mohideen (Product Marketing) and Nick Doane (Senior Sales Engineer). They cover cloud observability and multi-cloud monitoring across AWS, Azure, GCP, and OCI in LM Envision, using cloud provider APIs with no extra collectors.

SNMP Traps as Logs and Network Troubleshooting | Logs for Lunch

SNMP traps as logs, AI log anomaly detection, and network troubleshooting are the focus of Logs for Lunch: August 2026, for network and IT ops teams and LogicMonitor users. Tom Chavez introduces LM Logs, which keeps logs and metrics together. Cameron Compton troubleshoots dropped tunnel packets from a network operations dashboard, and Ron Scinta ingests SNMP traps through the Collector. Patrick Sites and Kerry DeVilbiss answer questions on load balancing, OTel and Cribl forwarding, MSP log sources, and search performance.

Comparing Coralogix with open source observability backends

We put the same OpenTelemetry data into four backends, Coralogix, ClickHouse, OpenSearch and a Loki, Tempo and Prometheus stack, then set an agent twelve questions against each one, from counting spans to working out what is wrong in a system it has never seen. Then we forced the agents to continue investigating until they got to the right answer. In this experiment, what separated them was the time, tokens, tool calls and turns it took to get there.
Featured Post

AI Is Making Infrastructure Easier to Create and Harder to Operate

AI is rapidly changing the economics of software engineering. Tasks that once required hours of engineering effort can increasingly be completed in minutes. An AI agent can write an application, generate Terraform, produce Kubernetes manifests, configure a CI/CD pipeline, and interact directly with cloud APIs. For infrastructure teams, this sounds like an enormous productivity win. And on Day 1, it is. But generating infrastructure has never been the hardest part of running it.