Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for Cybersecurity
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918011116453888 |
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| author | Tariq, Shahroz Singh, Ronal Chhetri, Mohan Baruwal Nepal, Surya Paris, Cecile |
| author_facet | Tariq, Shahroz Singh, Ronal Chhetri, Mohan Baruwal Nepal, Surya Paris, Cecile |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for Cybersecurity Tariq, Shahroz Singh, Ronal Chhetri, Mohan Baruwal Nepal, Surya Paris, Cecile Cryptography and Security 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. |
| title | Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for Cybersecurity |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2505.03179 |