Cyber-risk decision models: To insure IT or not?
Decision Support Systems
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e-business organizations are heavily dependent on distributed 24X7 robust information computing systems, for their daily operations. To secure distributed online transactions, they spend millions of dollars on firewalls, anti-virus, intrusion detection systems, digital signature and encryption. Nonetheless, a new virus or a clever hacker can easily compromise these deterrents, resulting in losses to the tune of millions of dollars annually. To cope up with the problem, in this work we propose to further enhance their security management by investing in e-risk insurance products as a viable alternative to reduce these individual financial losses. We develop a framework, based on copula aided Bayesian Belief Network (BBN) model, to quantify the risk associated with online business transactions, arising out of a security breach, and thereby help in designing e-insurance products. We have simulated marginal data for each BBN nodes. The Copula model helps in arriving at the joint probability distributions from these marginal data. From the joint distribution data, we arrive at the conditional distribution tables for each node. This is input to the Bayesian Belief Network model. The output is frequency of occurrence of an e-risk event. Frequency of loss multiplied with the expected loss amount, provides the risk premium to be charged by insurance companies.