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An agent-based simulation framework designed to train and benchmark AI/LLM agents in Security Operations Center (SOC) environments using structured attack scenarios and deterministic grading.
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The project addresses a high-value niche (AI for cybersecurity) but is currently a Day 0 repository with no stars, forks, or community traction. It follows the established pattern of 'Gym-like' environments for AI agents, similar to Microsoft's CyberBattleSim, but tailored for LLM-based SOC tasks. Its current state is that of a personal experiment or early prototype.
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