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A Review of Cyber Threat (Artificial) Intelligence in Security Management
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.ORCID iD: 0000-0003-1692-5721
School of Informatics, University of Skövde, Skövde, Sweden.
2022 (English)In: Artificial Intelligence and Cybersecurity: Theory and Applications / [ed] Tuomo Sipola, Tero Kokkonen, Mika Karjalainen, Springer, 2022, 1, p. 29-45Chapter in book (Other academic)
Abstract [en]

Managing cybersecurity within organizations typically relies on careful consideration and management of its risks. By following an iterative—often sequential—risk management process, an organization’s exposure to risks can be assessed by weighing organizational digital asset values against the probability of being harmed by a threat [29]. However, this approach has been criticized for reflecting only a snapshot of the organization’s assets and threats. Furthermore, identifying threats and the ability to remain updated on current threats and vulnerabilities are often dependent on skilled and experienced experts, causing risks to be primarily determined based on subjective judgment [46]. Nevertheless, this also poses a challenge to organizations that cannot stay up-to-date with what assets are vulnerable or attain personnel with the necessary experience and know-how to obtain relevant information on cybersecurity threats towards those assets [8, 30, 37].

Place, publisher, year, edition, pages
Springer, 2022, 1. p. 29-45
National Category
Information Systems
Research subject
Information Systems
Identifiers
URN: urn:nbn:se:ltu:diva-98281DOI: 10.1007/978-3-031-15030-2_2Scopus ID: 2-s2.0-85160500378ISBN: 978-3-031-15029-6 (print)ISBN: 978-3-031-15030-2 (electronic)OAI: oai:DiVA.org:ltu-98281DiVA, id: diva2:1766456
Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2023-09-05Bibliographically approved

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Lundgren, Martin

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