Operations | Monitoring | ITSM | DevOps | Cloud

Over the past year, organizations have embraced AI at an extraordinary pace, and Nexthink AI Activation Hub powered by AI Drive has helped customers make sense of that transformation by helping organizations discover the growing wave of AI tools entering the workplace, rapidly triage and govern them, accelerate adoption of approved AI solutions, and measure the impact of AI across the enterprise.

If you run LLM features in production, your most dangerous failures are the ones your monitoring never flags. Your LLM feature passed every test, and the demo went great. Three weeks after launch, a support ticket lands: the chatbot quoted a refund policy that does not exist. The dashboards are all green, and the same prompt answers correctly when you retry it. This is the blind spot LLM observability exists to close. Your existing tools saw the request come back fast with a clean status code.

How confident are you that you know every component inside your software, and every algorithm guarding it? In practice, the answer is usually scattered across build tools, ticketing systems, and spreadsheets that stopped being accurate months ago. A modern application draws on hundreds of open-source parts, and the encryption underneath them is harder still to trace. That visibility gap is what keeps the "SBOM vs CBOM" question alive in security and compliance reviews.

Most security breaches begin with something small and avoidable. Think of a password still set to its default, a server port left open to the internet, or configuration drift that slowly undoes last year's careful setup. Gaps like these go unnoticed because nobody is assigned to look for them, and they can be very costly. According to IBM's 2025 Cost of a Data Breach report, the average data breach incident costs companies 4.44 million dollars.

Today, we rolled out a new update to AlloyScan, our cloud-native IT asset discovery and network inventory solution. It brings a smoother notification subscription experience, richer dashboards, modern reporting, and faster, more reliable performance.

Asset tagging is the process of assigning a unique identifier (a physical or digital label) to each asset so it can be tracked, managed, and audited throughout its lifecycle. Without it, tracking assets across distributed environments becomes error-prone, loss-prone, and nearly impossible to audit. The practice applies to virtually any type of organizational asset: office furniture, vehicles, medical equipment, industrial machinery.

Every on-call engineer has lost an evening to some version of this. Something breaks; the fix is usually somewhere in the logs, and the logs are scattered everywhere: a dozen servers, a few containers, a couple of cloud services, none of them in one place. So, you SSH into one box, grep, get nothing, move to the next, and an hour later there are fifteen terminal tabs open and still no clear sequence of events. Log aggregation tools kill that scramble.