Operations | Monitoring | ITSM | DevOps | Cloud

Five key takeaways from EDUCAUSE 2025: Adopting AI while navigating change

Having just returned from the 2025 EDUCAUSE Annual Conference in Nashville, I want to share some insights on the future of campus IT from the higher education technology leaders in attendance. Every year, this conference provides an opportunity for technology providers and higher ed professionals to connect and explore the latest innovations in higher education technology. Two themes emerged as critical priorities.

Search Telemetry Without Limits in a Multi Cloud and AI World

Cribl Search gives you one lens across all your telemetry data no matter where it lives. Instead of forcing teams to move data into one system or jump between tools, you get a familiar pipe based query experience with dashboarding and alerting built in. Storage and query processing stay separate so you decide where your data lives while your users get fast, simple access in one place.

Episode 1 - Preparing the workforce for AI | The Intelligent Enterprise

In our first podcast episode of The Intelligent Enterprise, Ricardo Costa, Senior Vice President and Chief Technology Officer at Purolator, gives us his views on how to prepare the workforce for AI. In his role as a technology "translator" connecting business strategies with tech implementations, Ricardo highlighted the importance of translating complex tech concepts into simple, understandable stories and addressing leadership challenges in preparing the workforce for AI, including upskilling and ethical considerations.

AI Observability: How to Keep LLMs, RAG, and Agents Reliable in Production

AI observability closes the gap between “something’s wrong” and “here’s what to fix.” If you run AI in production, you might have felt the whiplash. Yesterday, your LLM answered in 300 milliseconds (ms). Today p99 crawls, costs spike, and nobody’s sure if the culprit is model behavior, data freshness, or GPUs stuck at the ceiling. Dashboards light up, but they don’t tell you which issue puts customers at risk. That’s the gap AI observability closes.

What Are AI Workloads? Everything Ops Teams Need to Know

AI workloads break every assumption you have about infrastructure management. AI is everywhere. Machine learning-based tools are answering customer service questions, accelerating incident resolution, catching fraudulent transactions, spotting defects on production lines, and powering late-night searches that delve into the random topic that pops into your head right before bedtime. Behind every prediction, response, or generated sentence is massive computing power doing serious, continuous work.

AI Monitoring, Explained: Challenges, Core Components, and Why Observability Is the Next Step

Monitoring AI systems isn’t business as usual. Monitoring AI isn’t like monitoring traditional systems. You can’t just track uptime or response times and call it a day. AI models evolve, data shifts, and behavior drifts over time, which means your monitoring has to evolve, too. If you’re running AI workloads in production, you already know this. Your models might look healthy according to your infrastructure metrics, but they’re still making bad predictions.

AI for Good: Securing Networks in the Age of Autonomous Attacks

The rise of autonomous AI attacks operating at machine speed demands that network security evolve beyond human capacity and manual processes. Kentik AI Advisor counters this threat by using AI for good, reasoning across full network context to proactively eliminate vulnerabilities and guide immediate, confident defense.

Architecture for the agentic era: How AI will reshape data, security, and observability

As AI agents move from copilots to autonomous systems, they’re generating and consuming data at unprecedented scale. The result is a new kind of infrastructure pressure — one that’s quietly reshaping how organizations think about data, cost, and control. Across IT, Security, and Observability, leaders are realizing a hard truth: too much data is too costly.