Details
| Original language | English |
|---|---|
| Number of pages | 17 |
| Publication status | Published - 2022 |
| Event | International Conference on Information Systems 2022 - Copenhagen, Denmark Duration: 9 Dec 2022 → 14 Dec 2022 |
Conference
| Conference | International Conference on Information Systems 2022 |
|---|---|
| Abbreviated title | ICIS 2022 |
| Country/Territory | Denmark |
| City | Copenhagen |
| Period | 9 Dec 2022 → 14 Dec 2022 |
Abstract
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2022. Paper presented at International Conference on Information Systems 2022, Copenhagen, Denmark.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Artificial Intelligence for Cybersecurity
T2 - International Conference on Information Systems 2022
AU - Gerlach, Jana
AU - Werth, Oliver
AU - Breitner, Michael H.
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2022
Y1 - 2022
N2 - Cybersecurity is a critical success factor for more resilient companies, organizations, and societies against cyberattacks. Artificial intelligence (AI)-driven cybersecurity solutions have the ability to detect and respond to cyber threats and attacks and other malicious activities. For this purpose, the most important resource is security-relevant data from networks, cloud systems, clients, e-mails, and previous cyberattacks. AI, the key technology, can automatically detect, for example, anomalies and malicious behavior. Consequently, the market for AI-driven cybersecurity solutions is growing significantly. We develop a taxonomy of AI-driven cybersecurity business models by classifying 229 real-world services. Building on that, we derive four specific archetypes using a cluster analysis toward a comprehensive academic knowledge base of business model elements. To reduce complexity and simplify the results of the taxonomy and archetypes, we propose DETRAICS, a decision tree for AI-driven cybersecurity services. Practitioners, decision-makers, and researchers benefit from DETRAICS to select the most suitable AI- driven service.
AB - Cybersecurity is a critical success factor for more resilient companies, organizations, and societies against cyberattacks. Artificial intelligence (AI)-driven cybersecurity solutions have the ability to detect and respond to cyber threats and attacks and other malicious activities. For this purpose, the most important resource is security-relevant data from networks, cloud systems, clients, e-mails, and previous cyberattacks. AI, the key technology, can automatically detect, for example, anomalies and malicious behavior. Consequently, the market for AI-driven cybersecurity solutions is growing significantly. We develop a taxonomy of AI-driven cybersecurity business models by classifying 229 real-world services. Building on that, we derive four specific archetypes using a cluster analysis toward a comprehensive academic knowledge base of business model elements. To reduce complexity and simplify the results of the taxonomy and archetypes, we propose DETRAICS, a decision tree for AI-driven cybersecurity services. Practitioners, decision-makers, and researchers benefit from DETRAICS to select the most suitable AI- driven service.
UR - http://www.scopus.com/inward/record.url?scp=85184800013&partnerID=8YFLogxK
M3 - Paper
Y2 - 9 December 2022 through 14 December 2022
ER -