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arXiv · 2505.03179

Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for Cybersecurity

Shahroz Tariq·Ronal Singh·Mohan Baruwal Chhetri·Surya Nepal·Cecile Paris
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Abstract

This study investigates whether large language models (LLMs) can function as intelligent collaborators to bridge expertise gaps in cybersecurity decision-making. We examine two representative tasks-phishing email detection and intrusion detection-that differ in data modality, cognitive complexity, and user familiarity. Through a controlled mixed-methods user study, n = 58 (phishing, n = 34; intrusion, n = 24), we find that human-AI collaboration improves task performance,reducing false positives in phishing detection and false negatives in intrusion detection. A learning effect is also observed when participants transition from collaboration to independent work, suggesting that LLMs can support long-term skill development. Our qualitative analysis shows that interaction dynamics-such as LLM definitiveness, explanation style, and tone-influence user trust, prompting strategies, and decision revision. Users engaged in more analytic questioning and showed greater reliance on LLM feedback in high-complexity settings. These results provide design guidance for building interpretable, adaptive, and trustworthy human-AI teaming systems, and demonstrate that LLMs can meaningfully support non-experts in reasoning through complex cybersecurity problems.

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Shahroz Tariq, Ronal Singh, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris. 2025-05-06. Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for Cybersecurity. https://arxiv.org/abs/2505.03179

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