Operations | Monitoring | ITSM | DevOps | Cloud

Knowing something is broken is easy. Figuring out why is hard. Introducing three new, native AI diagnostic capabilities in the Kentik Network Intelligence Platform to accelerate root cause analysis and keep your network running better.

What if the next breach in your cloud is already in motion, and your team has no idea how to see it? Cloud workloads are growing fast. APIs, identities, and data are spread across AWS, Azure, GCP, and on-prem systems all at once. Every layer creates its own logs, its own alerts, and its own blind spots. Most security teams are short on visibility, context, and time. That is the gap cloud threat detection is built to close.

Whether you’re a small start-up or a large-scale enterprise, data storage and backups are a necessity for a business to run smoothly. While many businesses may need on-premises storage for maximum control, this can be expensive, so if you’re wondering about the cloud migration cost savings when using a company like Internxt S3, this article will cover everything you need to help migrate or store data in the cloud.

In October 2023, security researchers published CVE-2023-46604, a CVSS 10.0 remote code execution vulnerability in Apache ActiveMQ. Within days, it was being actively exploited in ransomware campaigns. The attack required nothing more than network access to port 61616. No authentication, no credentials, no social engineering. The attacker connected to the standard ActiveMQ port and executed arbitrary code on the server.

AWS re:Invent 2025 made one thing very clear: enterprise interest in AI is no longer theoretical. The conversation has moved beyond curiosity. Teams are actively experimenting, leaders are looking for production-ready use cases, and engineering organizations are trying to figure out where AI can create real leverage across software delivery, security, platform engineering, and operations.

When an incident occurs, an L2/3 engineer or SRE can spend 20–30 minutes investigating across alert consoles, combing through change records, and pinging teams on Slack or Microsoft Teams. When you multiply that time spent across thousands of incidents per year by the cost of an IT outage at $14,056 per minute, the cost is staggering. Enterprises can’t afford to waste time searching across disparate tools.

When I read about the Vercel breach tied to a Context AI compromise, I wasn’t surprised. I’ve been talking with customers for a while now about how AI was going to introduce a new kind of supply chain risk. This is exactly what that looks like. What stands out to me is how familiar the pattern is. We saw it with open source, then again with SaaS, and again with cloud.

Monitoring coverage, anomaly detection, and SLO-based alerting have significantly narrowed detection windows for most failure types, but MTTD remains stubbornly high for a specific silent failure. This blog covers why type mismatches, swallowed exceptions, and values that pass validation without occurring without triggering errors, and what changes when your monitoring stack can generate those signals without waiting for a failure to surface them.