AI-Driven Cyber Risk Assessment and Insurance

About this Research Topic

Background

An increasing dependence on digital infrastructure has exposed businesses, financial institutions, governments, and other organizations to unforeseen cyber threats and incidents. The rapid evolution, complexity, and impact of such incidents are outpacing traditional risk assessment and insurance models, creating a growing demand for more sophisticated solutions. However, transformative solutions for managing cyber risks are being developed by leveraging advanced AI methods for cyber risk assessment and insurance, in addition to AI-driven techniques for threat detection, predictive modeling, and automated cost estimation. Machine learning algorithms can sift through massive data sets to detect patterns of attack, evaluate vulnerabilities in real time, and refine the pricing of cyber insurance. Yet challenges still exist regarding reliability, bias in AI-led underwriting, systemic cyber risks, compliance with regulation, and ethical issues relating to data privacy. Resolving these obstacles is fundamental to enhancing cyber resilience, advancing risk transfer mechanisms, and designing the future of cyber insurance in a world that is becoming increasingly driven by AI.

This Research Topic aims to highlight the transformative role of AI in advancing the scientific understanding and application of cyber risk assessment, management, and insurance. It seeks to explore how AI-driven methodologies can enhance threat detection, predictive analytics, and automated underwriting while revolutionizing key aspects of cyber risk management operations. This includes AI-powered modeling, empirical analysis, and mitigation of cyber risks, as well as their operational, economic, and societal impacts on individuals and organizations, considering behavioral, ethical, and legal dimensions. The collection will welcome contributions that examine the role of AI in risk management, mitigation, quantification, and vulnerability assessment before, during, and after cyber incidents. By addressing these challenges, the ultimate goal is to inspire the development of more AI-driven, robust, efficient, and adaptive models to navigate the growing complexity of cyber risks in both operational and insurance domains.

Submissions are encouraged that offer interdisciplinary perspectives from computer science, finance, actuarial science, and cybersecurity. Specific themes of interest include, but are not limited to:
- AI-powered cyber risk assessment
- Cyber risk management operations
- Economic and societal impacts
- Insurability of cyber risks
- Data-driven cyber insurance
- AI and cyber resilience
- Systemic and operational risk modeling

Contributions can encompass high-quality original research, case studies, and comparative studies spanning:
- Theoretical and empirical research on AI-driven risk assessment models
- Comparative studies on AI methodologies for cyber insurance and risk management
- Case studies demonstrating real-world applications of AI in cyber risk operations
- Interdisciplinary research bridging AI, finance, and cybersecurity

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: risk assessment, cyber risk management, cyber risk modeling, big data analytics, machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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