Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

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.

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.

It’s no surprise that AI is being integrated into identity governance and administration (IGA) platforms. Automation promises productivity boosts, risk detection can be in real-time and cloud environments allow greater scalability. What’s more, the pace of AI means IGA is quickly moving beyond slower, more rigid, rule-based approaches.

Static application security testing (SAST) tools help developers quickly catch potential vulnerabilities as they code. However, these tools rely on inflexible rules that often generate a high number of false positives, reducing trust in their accuracy and slowing adoption. To help developers access context-aware vulnerability detection, we’ve released an open source AI-native SAST solution. This tool scans code changes incrementally and surfaces security issues in real time.