DeepTempo Releases SOCBench: Open Benchmark for Evaluating LLMs on Security Operations Tasks
Key Takeaways
- ▸SOCBench provides the first open standard for benchmarking frontier LLMs from multiple providers (OpenAI, Anthropic, Google) on real Security Operations Center tasks with identical evaluation metrics
- ▸The benchmark is designed for reproducibility and accessibility—a complete smoke test runs for under $10 on a laptop with no specialized infrastructure needed
- ▸Four distinct SOC personas enable evaluation of different threat analysis approaches, with multi-turn agent loops, tool ablations, and playbook comparisons to measure real-world performance
Summary
DeepTempo has released SOCBench, an open-source benchmark framework for evaluating frontier reasoning language models as Security Operations Center (SOC) agents. The benchmark tests models from OpenAI, Anthropic, and Google against real-world network security analysis tasks using NetFlow data, with four distinct SOC personas—SOC Analyst, Threat Analyst, Adversary Hunter, and Detection Engineer—to measure performance across different security workflows.
Designed for accessibility and reproducibility, SOCBench enables evaluation on a laptop with minimal resources: a smoke test costs under $10 and requires only API keys and a committed sample dataset. The alpha release includes a complete end-to-end pipeline featuring deterministic content-addressed indexes, persona-scoped read-only tools, fixed budget caps per investigation, standardized JSON contracts, and comprehensive scoring across multiple evaluation lenses (per-flow, per-pair, per-host F1 metrics).
The benchmark addresses a critical gap in AI evaluation by providing standardized, directly comparable metrics for how well frontier models handle real SOC tasks. By testing models from competing providers using identical evaluation criteria and scoring surfaces, SOCBench enables security teams and enterprises to make data-driven decisions about LLM-powered SOC tools based on empirical performance rather than vendor claims.
- The framework includes deterministic, content-addressed indexes and standardized scoring lenses, making results directly comparable across models and enabling rigorous measurement of which frontier models perform best on SOC workflows
Editorial Opinion
SOCBench fills a critical void in AI evaluation—moving beyond generic LLM benchmarks to measure real-world performance on high-stakes security tasks. By openly benchmarking competing frontier models with identical criteria, the tool empowers security teams to make informed decisions based on empirical data. The deliberate focus on reproducibility and low cost (under $10 per smoke test) democratizes AI evaluation and sets a strong precedent for how specialized AI benchmarks should be designed and distributed.
