Operations | Monitoring | ITSM | DevOps | Cloud

Enterprise AI struggles not from inadequate models, but from fragmented data architecture. Critical business data remains trapped in legacy systems or lost in streaming complexity. Success requires bridging the gap between modern intelligence layers and underlying systems of record.

We have been getting the same request from teams for a while now: “We use Scout for our Rails app. Can we get the same thing for our Node services?” Today the answer is yes. Scout Monitoring now supports Node.js. If your team runs Express or NestJS in production, you get the same errors-and-traces experience that Ruby, Python, PHP, and Elixir teams have had. Let’s walk through what that means in practice.

You can use Grafana Assistant Investigations to automatically discover incidents and help find root causes—and this AI-powered Grafana Cloud feature recently got a major upgrade to give you even more confidence in its findings. You can read more about the behind-the-scenes effort in our new engineering blog Unprompted, where we get into harness engineering, context compaction, benchmarking, and keeping agents alive and working well in long-running sessions.

MSPs already have plenty of tools. The harder problem is getting a clear read on what’s happening across each customer environment, which alerts point to the same issue, and where engineers should start. Choosing the right MSP software is really about choosing the right operating layer for service delivery. MSPs are supporting more customers, more environments, and more alerts, but adding another tool doesn’t always make the work easier.

If you're sending security data to Microsoft Sentinel, you now have a faster path. A new ASIM mode lands your logs directly in Sentinel's native ASIM tables: no custom tables to predefine, no schema to design before data flows. We added ASIM mode to the Microsoft Sentinel destination, backed by a new ASIM standardization processor that converts raw logs to ASIM in the pipeline and routes each record to the table it belongs in. Here's how it works, and why we built it this way.

Organizations invest heavily in cybersecurity tools, yet many still struggle to confidently understand what is actually protected across their environment. This blog explores how disconnected systems, unknown assets, and inconsistent data create blind spots, and how Teneo’s Cyber Asset Attack Surface Management (CAASM), powered by ThreatAware, helps organizations gain a trusted view of security coverage.

Security teams today are managing growing volumes of cybersecurity data across increasingly complex environments. This blog explores the hidden operational cost of disconnected tools, manual data reconciliation, and fragmented reporting, and how Teneo’s Cyber Asset Attack Surface Management (CAASM), powered by ThreatAware, helps organizations create a more unified and trusted view across their security estate. Most organizations are not short of security tools.

Predictive maintenance is one of the most compelling use cases for time series data. Instead of waiting for equipment to fail or servicing it on a fixed calendar regardless of condition, you watch the live sensor data and act when it indicates that a failure is coming. That “watch the data and act” loop is exactly what the InfluxDB 3 Processing Engine was built for.