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arXiv · 2505.06394

Towards AI-Driven Human-Machine Co-Teaming for Adaptive and Agile Cyber Security Operation Centers

Massimiliano Albanese·Xinming Ou·Kevin Lybarger·Daniel Lende·Dmitry Goldgof
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Abstract

Security Operations Centers (SOCs) face growing challenges in managing cybersecurity threats due to an overwhelming volume of alerts, a shortage of skilled analysts, and poorly integrated tools. Human-AI collaboration offers a promising path to augment the capabilities of SOC analysts while reducing their cognitive overload. To this end, we introduce an AI-driven human-machine co-teaming paradigm that leverages large language models (LLMs) to enhance threat intelligence, alert triage, and incident response workflows. We present a vision in which LLM-based AI agents learn from human analysts the tacit knowledge embedded in SOC operations, enabling the AI agents to improve their performance on SOC tasks through this co-teaming. We invite SOCs to collaborate with us to further develop this process and uncover replicable patterns where human-AI co-teaming yields measurable improvements in SOC productivity.

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Massimiliano Albanese, Xinming Ou, Kevin Lybarger, Daniel Lende, Dmitry Goldgof. 2025-05-09. Towards AI-Driven Human-Machine Co-Teaming for Adaptive and Agile Cyber Security Operation Centers. https://arxiv.org/abs/2505.06394

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