Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Security teams accept that standing up a real SOC requires days of configuration, credential wrangling, and infrastructure work before any actual security engineering begins. With LimaCharlie, actual setup time is closer to ten minutes. It gives valuable time back to SecOps teams by managing infrastructure and simplifying onboarding and operations with Claude Code. Using agentic AI to deploy SOC capabilities means your team spends less time on infrastructure and more on security work.

The human layer is not impacted by Anthropic's Mythos Preview announcement. If anything, it is reinforced, and for reasons that deserve to be spelled out clearly.

The AI security market is crowded. Vendors are racing to protect prompts, harden models, detect jailbreaks, and scan for data leakage at the LLM layer. The investment is real. The intent is good. And most of it is missing the point. Here is the problem: agents do not just think. They act. They call APIs. They trigger workflows. They write to databases, send emails, move money, and modify production systems.

AI cybersecurity companies in 2026 fall into two categories: platforms using AI to automate detection, investigation, and response, and platforms built to secure the AI systems organizations are now deploying. With this grouping into ‘AI for Security’ and ‘Security for AI’, this article covers the breadth and depth of AI cyber security companies.

A platform security engineer gets an alert at 2:14 a.m. One of the LangChain agents running in their production Kubernetes cluster has produced an execution graph with eleven nodes, seven tool calls, and an egress edge to a domain that is not in the agent’s approved integration list. The chain is fully rendered in their console. Every signal is there.