Show HN: SOCBench – an open benchmark for AI on SoC tasks (github.com)

The newly launched SOCBench serves as an open benchmark designed specifically for evaluating large language models (LLMs) as Security Operations Center (SOC) agents using raw NetFlow data. This benchmark enables each AI model to engage in a structured multi-turn agent loop against a deterministic, indexed corpus of NetFlow, complete with various personas like SOC Analyst and Detection Engineer. Notably, SOCBench offers shared evaluation units and scoring methodologies across different AI providers, such as OpenAI and Anthropic, allowing for direct comparisons of model performance. The setup is user-friendly, requiring just a laptop, three API keys, and a sample dataset to get started at low cost. This initiative is significant for the AI/ML community as it establishes a standardized framework for assessing LLM capabilities in a multi-turn reasoning context, particularly within cybersecurity. The toolset includes detailed configurations, deterministic evaluation processes, and comprehensive scoring mechanisms, supporting reproducibility and ease of experimentation. Developers can run tests with both mock and real AI models, providing flexibility and accessibility. As SOCBench evolves, it promises to enhance the discourse on LLM applications in security tasks, contributing valuable insights into how these models handle complex decision-making scenarios.