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Human-AI Integration in Cybersecurity: An Industry-Aligned Perspective on Incident Management.

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Title: Human-AI Integration in Cybersecurity: An Industry-Aligned Perspective on Incident Management.
Authors: Kincl, Jan1,2 (AUTHOR) jan.kincl@uon.edu.au, Adam, Marc T. P.2 (AUTHOR) marc.adam@newcastle.edu.au, Pavleska, Tanja1 (AUTHOR) tanja.pavleska@ijs.si
Source: International Journal of Information Security. Jun2026, Vol. 25 Issue 3, p1-29. 29p.
Abstract: The integration of Artificial Intelligence (AI) into cybersecurity incident management has gained momentum amid rising threats and increasing operational complexity. While academic research has been widely analysed and found to predominantly address detection tasks and algorithmic performance, the industry perspective remains under-explored, with limited understanding of its practices and priorities. This paper presents a document analysis of a rich corpus of publicly available, industry-issued reports, to examine how AI is understood, deployed, and evaluated in real-world cybersecurity operations. By contextualising the well-established Technology-Human-Task-Context (THTC) framework to the cybersecurity domain, we identify key dimensions shaping AI integration, including task alignment, human oversight, operational constraints, and expected benefits. Our findings highlight critical gaps in current implementations, such as limited attention to recovery, governance, and task assignment, and emphasise the dual role of human experts as both controllers and beneficiaries. To address these gaps and support actionable system design, we propose a set of industry-aligned recommendations derived from a synthesis of the THTC framework and document analysis. These recommendations encompass AI capabilities, integration requirements, task alignment, outcomes, risk management, control mechanisms, and human factors. Our work offers a comprehensive foundation for aligning academic research with industry needs, guiding the development of AI-powered cybersecurity expert systems that are technically effective, ethically sound, and operationally viable. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Information Security is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Human-AI Integration in Cybersecurity: An Industry-Aligned Perspective on Incident Management.
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  Data: <searchLink fieldCode="AR" term="%22Kincl%2C+Jan%22">Kincl, Jan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jan.kincl@uon.edu.au</i><br /><searchLink fieldCode="AR" term="%22Adam%2C+Marc+T%2E+P%2E%22">Adam, Marc T. P.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> marc.adam@newcastle.edu.au</i><br /><searchLink fieldCode="AR" term="%22Pavleska%2C+Tanja%22">Pavleska, Tanja</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tanja.pavleska@ijs.si</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Information+Security%22">International Journal of Information Security</searchLink>. Jun2026, Vol. 25 Issue 3, p1-29. 29p.
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  Data: The integration of Artificial Intelligence (AI) into cybersecurity incident management has gained momentum amid rising threats and increasing operational complexity. While academic research has been widely analysed and found to predominantly address detection tasks and algorithmic performance, the industry perspective remains under-explored, with limited understanding of its practices and priorities. This paper presents a document analysis of a rich corpus of publicly available, industry-issued reports, to examine how AI is understood, deployed, and evaluated in real-world cybersecurity operations. By contextualising the well-established Technology-Human-Task-Context (THTC) framework to the cybersecurity domain, we identify key dimensions shaping AI integration, including task alignment, human oversight, operational constraints, and expected benefits. Our findings highlight critical gaps in current implementations, such as limited attention to recovery, governance, and task assignment, and emphasise the dual role of human experts as both controllers and beneficiaries. To address these gaps and support actionable system design, we propose a set of industry-aligned recommendations derived from a synthesis of the THTC framework and document analysis. These recommendations encompass AI capabilities, integration requirements, task alignment, outcomes, risk management, control mechanisms, and human factors. Our work offers a comprehensive foundation for aligning academic research with industry needs, guiding the development of AI-powered cybersecurity expert systems that are technically effective, ethically sound, and operationally viable. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Information Security is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2026
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