Optimal Cyber Insurance Policy Design for Dynamic Risk Management and Mitigation

Recently, with the growing number of cyber-attacks and the constant lack of effective and state-of-art defense methods, cyber risks become ubiquitous in enterprise networks, manufacturing plants, and government computer systems. Cyber-insurance has become one of the major ways to mitigate the risks as it can transfer the cyber-risks to insurance companies and improve the security status of the insured. The designation of effective cyber-insurance policies requires the considerations from both the insurance market and the dynamic properties of the cyber-risks. To capture the interactions between the users and the insurers, we present a dynamic moral-hazard type of principalagent model incorporated with Markov decision processes which are used to capture the dynamics and correlations of the cyber-risks as well as the user’s decisions on the local protections. We study and fully analyze a case where the user has two states, and two actions and the insurer provides linear coverage insurance. We show the Peltzman effect, linear insurance policy principle, and zero-operating profit principle of the optimal cyber-insurance policy. Numerical experiments are provided to verify our conclusions further and extend to cases of a four-state three-action user under linear coverage insurance and a threshold coverage insurance.