Modeling Cyber-Insurance: Difference between revisions
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==Key Words== | ==Key Words== | ||
[[Cyber-Insurance]], [[Risk Assessment]] | |||
==Synopsis== | ==Synopsis== | ||
We propose a comprehensive formal framework to classify all market models | |||
of cyber-insurance we are aware of. The framework features a common terminology | |||
and deals with the specific properties of cyber-risk in a unified way: interdependent | |||
security, correlated risk, and information asymmetries. A survey of | |||
existing models, tabulated according to our framework, reveals a discrepancy between | |||
informal arguments in favor of cyber-insurance as a tool to align incentives | |||
for better network security, and analytical results questioning the viability of a market | |||
for cyber-insurance. Using our framework, we show which parameters should | |||
be considered and endogenized in future models to close this gap. | |||
==Additional Notes and Highlights== | ==Additional Notes and Highlights== | ||
Revision as of 10:16, 24 June 2010
Full Title of Reference
Modeling Cyber-Insurance: Towards A Unified Framework
Full Citation
Rainer Bohme and Galina Schwartz, Modeling Cyber-Insurance, Workshop on the Economics of Information Security, Harvard University, Cambridge, MA (June 2010). Web
Categorization
Issues: Economics of Cyber Security; Insurance
Key Words
Cyber-Insurance, Risk Assessment
Synopsis
We propose a comprehensive formal framework to classify all market models of cyber-insurance we are aware of. The framework features a common terminology and deals with the specific properties of cyber-risk in a unified way: interdependent security, correlated risk, and information asymmetries. A survey of existing models, tabulated according to our framework, reveals a discrepancy between informal arguments in favor of cyber-insurance as a tool to align incentives for better network security, and analytical results questioning the viability of a market for cyber-insurance. Using our framework, we show which parameters should be considered and endogenized in future models to close this gap.