Cybersecurity Insurance: Modeling and Pricing

Abstract

Cybersecurity risk has attracted considerable attention in recent decades. However, the modeling of cybersecurity risk is still in its infancy, mainly because of its unique characteristics. In this study, we develop a framework for modeling and pricing cybersecurity risk. The proposed model consists of three components: the epidemic model, loss function, and premium strategy. We study the dynamic upper bounds for the infection probabilities based on both Markov and non-Markov models. A simulation approach is proposed to compute the premium for cybersecurity risk for practical use. The effects of different infection distributions and dependence among infection processes on the losses are also studied.

Beyond Herd Immunity Against Strategic Attackers

“…Cybersecurity insurance (or cyber liability insurance) is a product that an entity can purchase to help reduce the financial risks associated with online business. It encompasses a contract wherein, in exchange for a fee, the insurance policy transfers some of the risk to the insurer [64], [65]. Our results imply that the modeling and pricing of cybersecurity insurance should take into account both positive and negative externalities derived from immunization.…”

“…Our results imply that the modeling and pricing of cybersecurity insurance should take into account both positive and negative externalities derived from immunization. In particular, the model proposed in this paper may serve as an additional ingredient when assessing insurance prices [65].…”

A Comprehensive Model for Cyber Risk Based on Marked Point Processes and Its Application to Insurance

“…Expected aggregate network losses are estimated using a polynomial approximation of claims and a mean-field approach. [80] use Markovian and Non-Markovian processes for epidemic spreading and propose to use a copula approach to capture the dependence among time-to-infection distributions. Via Monte-Carlo simulation, a pricing framework for cyber insurance is evaluated and the effects of different infection distributions and dependence among infection processes on aggregate losses are studied.…”

Enterprise security economics: A self‐defense versus cyber‐insurance dilemma

“…Using the latter approach, the security vulnerabilities, the expected loss due to security breaches, and the amount of premium to be charged by the insurer are assessed, while taking into consideration the computed expected loss, the risk profile, and wealth of the insured firm. In the work of Xu and Hua, a novel cybersecurity insurance model is designed, which is based mainly on the use of (a) Markov and non‐Markov models to depict the dynamic evolution of the network attack and recovery processes over time and (b) Monte Carlo simulations to study the insurance pricing strategies in practice. In the same context, Bandyopadhyay and Mookerjee proposed a model for assessing the insurer's optimal pricing decision that is based on the insured firm's claim indemnity strategy when different types of breaches and the subsequent losses (primary and secondary losses) are considered.…”