Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

You’ve deployed five AI agents into your production Kubernetes cluster: a customer support chatbot, a fraud detection agent, a data pipeline processor, a code generation assistant, and an internal summarization bot. Your security team writes one set of guardrails and applies them uniformly. Within a week, you discover the code generation agent needs interpreter access the chatbot should never have.

Last Tuesday, a platform security engineer at a mid-size fintech company ran a routine audit on their production Kubernetes clusters. The audit surfaced three LangChain-based agents, two vLLM inference servers, and a Model Context Protocol (MCP) tool runtime. None had been reported by the development teams. None appeared in any security inventory. All had been running for weeks. One of the agents had been making outbound API calls to a third-party data enrichment service every four minutes.

You’re evaluating AI workload security tools and every demo looks the same. Vendor A shows you an AI-SPM dashboard. Vendor B shows you a nearly identical AI-SPM dashboard with slightly different branding. Vendor C shows you posture findings with an “AI workload” tag that wasn’t there last quarter.

Automation platforms sit at the center of modern infrastructure. They connect APIs, databases, CI/CD pipelines, SaaS tools, and internal systems. But when automation engines become compromised, the blast radius can be enormous. In February 2026, n8n, a widely used open-source workflow automation platform, disclosed four critical vulnerabilities that can lead to remote code execution (RCE) by authenticated users with workflow creation or editing permissions.

We’ve got a boatload of features to show off this week. We knocked out some of the most common requests, user management, single-sign on, and my personal favorite: the weekly email summary.

Every cloud security vendor now has an AI-SPM dashboard. Strip away the branding, though, and most of these dashboards are doing the same thing: checking IAM configurations, scanning for misconfigured network access, inventorying AI models across cloud accounts, and flagging compliance gaps. It’s cloud security posture management with an AI label applied. That’s a problem, because AI workloads don’t behave like other cloud workloads.

DevOps engineers running OpenShift know the platform well. They know how to build on it, scale on it, and operate it under pressure. What they often hit unexpectedly is the question of backup and recovery, especially once OpenShift Virtualization enters the picture. Most of the tooling that exists today wasn’t built with Kubernetes in mind. It was built for something else and extended toward it.