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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.
AI Workload Infrastructure Requirements: What You Actually Need
Artificial intelligence (AI) infrastructure requires four pillars working in tandem as a system (compute, storage, networking, and orchestration) tailored to your actual workload needs, not hype. Artificial intelligence (AI) infrastructure isn’t just more hardware. It’s a new class of system—highly distributed, resource-intensive, and tightly coupled across compute, storage, and network layers.
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.
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 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.
Use OpenTelemetry with Observability Pipelines for vendor-neutral log collection and cost control
Today, many DevOps and security teams operate in a world of complex, hybrid, or multi-vendor environments. As more teams look to avoid lock-in by adopting open standards, OpenTelemetry (OTel) is quickly gaining adoption as the primary open source method for DevOps and security teams to instrument and aggregate their telemetry data. However, OTel alone may lack the advanced processing functions, native volume control rules, and hybrid environment support that large organizations need.
Redgate Test Data Manager Live at PASS Data Community Summit
A look at Redgate Test Data Manager and the latest AI custom dataset functionality.
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.
Kafka To ClickHouse in 6min
Learn how to stream data from Kafka into ClickHouse using Aiven’s integration wizard. In this demo, we show you how to generate sample logistics data in Kafka, configure the integration to map Avro-formatted fields, and connect to ClickHouse to view and query the ingested data. We also demonstrate how to create a materialised view in ClickHouse to store and query streamed data efficiently, making real-time analytics fast and easy.