Paper Number

2040

Paper Type

Complete

Description

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.

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Dec 12th, 12:00 AM

Artificial Intelligence for Cybersecurity: Towards Taxonomy-based Archetypes and Decision Support

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.

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