Operations | Monitoring | ITSM | DevOps | Cloud

Outrun the Threat Window: AI-accelerated Vulnerability and Patch Management

Sep 2, 2026 By N-able In N-able
The gap between vulnerability disclosure and active exploitation is shrinking—often from weeks to mere hours. Traditional patching cycles no longer cut it. In this session, discover how AI-accelerated solutions can help you: Identify exposed assets faster Prioritize vulnerabilities by real-world risk Remediate across Windows, macOS, Linux, and hundreds of apps Verify success with a connected workflow Learn how our approach, powered by AI-driven insights and automation, can help you close the gap before attackers strike. Watch now and take control of your patch management.
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sumologic

The six pillars of AI-ready telemetry

Sep 2, 2026 By Chas Clawson Bogdan Kireeve In Sumo Logic
“AI-ready” is everywhere right now, attached to nearly every product in every category. The catchy label rarely means anything specific, just as additional questions are warranted when vendors claim to be “AI-native”. After fighting through all the marketing jargon, there needs to be a standard, not a slogan. And the definition changes depending on what the data is for. AI-ready for a data warehouse and AI-ready for live operational telemetry are not the same problem.
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gitkraken

Can You Prove Your AI Agents Are Paying Off? Most Developers Can't

Sep 2, 2026 By Jade Nangah In GitKraken
We put a blunt question to developers on a recent live webinar: right now, could you actually prove AI agents are paying off for you or your team? Only 24% said yes. The other 76% were guessing, unsure, or already suspicious that agents are costing more than they’re saving. That gap between adoption and proof is the real story in agentic development right now. Teams aren’t behind on running agents. They’re behind on knowing whether it’s working.
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jfrog

The Evolution of JFrog AI Catalog: Your AI Control Plane for Agentic Development

Sep 2, 2026 By Guy Eshet In JFrog
In a single morning, a coding agent can pull an open-source model, connect to an unvetted MCP server, and execute a code-optimizing skill from the web. In the rush toward agentic automation, these AI assets quietly bypass traditional security reviews, creating new attack vectors across the software supply chain. Closing this blind spot has been the driving force behind the JFrog AI Catalog since its launch at swampUP 2025.
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cloudzero

Our Customer Success AI bill tripled. Here's why we're spending more.

Sep 2, 2026 By Scott Castle In CloudZero
Pop quiz: If you spend $40,000 per month on Anthropic, and you’ve got two customers, what’s your cost per customer? If you bypassed the easy answer of $20,000 and said, “Scott, you old trickster, that’s not enough information to answer that question,” you’ve won today’s prize: a lesson in the perils of average costs. Let’s flesh out the situation: You put an AI feature in your product, a document assistant powered by Claude.
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cloudzero

Shipped: Rightsize Kubernetes workloads without leaving your MCP client

Sep 2, 2026 By Kevin Lamb In CloudZero
Changing a Kubernetes resource request takes two numbers: what the workload requests, and what it uses. The CloudZero MCP server now returns both, by cluster, namespace, or workload. This gives you a number you can defend. Usage comes back as P95 over the date range you query, 30 days by default. When an engineering lead asks whether a service runs on a smaller request, that is the figure that settles it. Over-provisioning and under-provisioning show up on the same query.
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SaaS Sprawl Is Becoming an IT Problem: Here's How to Bring It Under Control

SaaS Sprawl Is Becoming an IT Problem: Here's How to Bring It Under Control

Sep 2, 2026 By OpsMatters In OpsMatters
For most organizations, SaaS sprawl does not begin with a bad technology decision. It starts with a useful tool. Marketing needs a new analytics platform. Sales adopts prospecting software. HR adds an applicant tracking system. Engineering signs up for another monitoring service. Someone discovers an AI tool that saves several hours a week and puts it on a company card. Each purchase makes sense on its own.
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From AI Prototype to Production: The Technical Architecture Enterprises Need

From AI Prototype to Production: The Technical Architecture Enterprises Need

Sep 2, 2026 By OpsMatters In OpsMatters
Building a generative model that spits out flawless answers in a controlled notebook feels like a massive win for any engineering team. But watching that exact same model crash the second it hits real, concurrent user traffic? That is a frustrating reality check. The gap between a slick proof of concept and a mission-critical deployment is surprisingly wide, and it almost always comes down to the underlying infrastructure. If your systems cannot handle the dynamic load, the smartest algorithm in the world will not save you.
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