Security Pre-Screening in the Design of Cyber-Insurance Policies

Cyber insurance is a risk mitigation strategy through which entities transfer part of their cyber-risks to an insurer in return for paying a fixed premium. Recently, policy makers (such as the US Department of Homeland Security) and cyber security researchers have investigated the possibility of further using cyber insurance as an incentive mechanism. The idea is to incorporate tactics such as premium discrimination in the design of contracts so as to incentivize the adoption of better security practices by the insured. In this talk, I will discuss the design of such cyber insurance contracts, with an emphasis on users' unobservable security decisions (moral hazard) and their interdependence in security. I will begin by illustrating the role of security pre-screening in mitigating moral hazard. We show that premium discrimination based on pre-screening can both increase the insurer's payoff and improve the state of network security. I will then discuss the possibility of using repeated pre-screening for mitigating moral hazard in the long run. This is joint work with Mahdi Khalili and Mingyan Liu.
Parinaz is a postdoctoral research fellow in EECS at the University of Michigan. Her research interests include game theory, network economics, and optimization, with applications to problems in cyber security. She received her Ph.D. in electrical engineering from the University of Michigan in 2016, M.Sc. degrees in electrical engineering and mathematics, both from the University of Michigan, in 2013 and 2014, respectively, and a B.Sc. in electrical engineering from Sharif University of Technology, Iran, in 2010. She was a recipient of the Barbour scholarship in the 2014-15 academic year.