A Generalized Linear Mixed Model for Data Breaches and its Application in Cyber Insurance

Data breach incidents result in severe financial loss and reputational damage, which raises the importance of using insurance to manage and mitigate cyber related risks. We analyze data breach chronology collected by Privacy Rights Clearinghouse (PRC) since 2001 and propose a Bayesian generalized linear mixed model for data breach incidents. Our model captures the dependency between frequency and severity of cyber losses, and the behavior of cyber attacks on entities across time. Risk characteristics such as types of breach, types of organization, entity locations in chronology, as well as time trend effects are taken into consideration when investigating breach frequencies. Estimations of model parameters are presented under Bayesian framework using a combination of Gibbs sampler and Metropolis-Hastings algorithm. Predictions and applications of the proposed model in enterprise risk management and cyber insurance rate filing are discussed