The Benefifits of AI in Cyber Crisis Management for Corporate Communications

Metin, Bilgin, Tümer, Efsun, Wynn, Martin G ORCID: https://orcid.org/0000-0001-7619-6079 and Taskin, Nazim (2025) The Benefifits of AI in Cyber Crisis Management for Corporate Communications. In: Mediterranean Conference on Information Systems (MCIS) 2025 Proceedings. Elsevier, pp. 1-12. ISBN 9781958200179

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Abstract

This paper investigates how organizations employ Artificial Intelligence (AI) to strengthen corporate communication during cyber crises. Cyber incidents are distinct from traditional crises due to their rapid occurrence, cross-border effects, and socio-technical complexity. This complexity necessitates new methods for communication and coordination. We conducted a systematic literature review (SLR) following the PRISMA 2020 guidelines, synthesizing 23 peer-reviewed studies published between 2017 and 2025. Our findings identify four key areas where AI enhances crisis communication: external communication with stakeholders, internal communication and incident response, application throughout the crisis lifecycle, and limitations that require human oversight. In these areas, AI contributes through real-time monitoring, automated message drafting, sentiment analysis, and regulatory reporting, thereby improving communication both internally and externally, as well as at the executive level. However, the accuracy, empathy, and ethical standards of communication still rely on human communicators. The study provides practical guidance for organizations on integrating AI responsibly while balancing automation with human expertise.

Item Type:	Book Section
Uncontrolled Keywords:	Corporate Communications, cyber crisis, AI, Artificial Intelligence, cybersecurity, Crisis Communication
Subjects:	Q Science > Q Science (General) > Q336 Artificial intelligence Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Martin Wynn
Date Deposited:	15 Oct 2026 12:53
Last Modified:	16 Oct 2026 08:00
URI:	https://eprints.glos.ac.uk/id/eprint/16565

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