City Research Online

Abstract

The cyber threat landscape is evolving with increasing sophistication, making robust risk management strategies essential. Cyber insurance has emerged as a key mechanism for organizations to transfer risk. While not yet mandatory, recent regulatory trends are encouraging the adoption of multiple technologies, potentially paving the way for broader implementation. However, traditional cyber insurance underwriting still relies heavily on questionnaire-based risk assessments, lacking a truly data-driven approach. This gap has received limited attention in existing research. In this survey we examine data-driven methodologies for cyber insurance as a critical component of modern risk management. We systematically review the literature on data sources, data collection practices, risk calculation methods, and pricing strategies. Traditional actuarial approaches are analyzed and contrasted with emerging AI-enabled methods, including supervised learning models, predictive analytics, and simulation-based techniques that enhance the estimation of incident likelihood and severity. We identify key challenges, such as data quality, lack of standardization, constrained data sharing, and model interpretability, and outline opportunities for future work aimed at integrating empirical, automated analyses into underwriting and pricing workflows. Overall, this survey offers a structured overview of existing approaches and highlights directions for developing more data-driven and AI-supported approaches to cyber insurance risk management.

Publication Type:	Article
Additional Information:	© The Authors. Published by Springer. This is an open-access article distributed under the terms of Creative Commons: Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords:	Cyber insurance, Cybersecurity, Artificial intelligence, Risk management, Risk assessment
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HF Commerce H Social Sciences > HG Finance K Law > K Law (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science School of Science & Technology > Department of Computer Science > Software Reliability
SWORD Depositor:	Symplectic Administrator

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