Cover: Terrorism Risk Modeling for Intelligence Analysis and Infrastructure Protection

Terrorism Risk Modeling for Intelligence Analysis and Infrastructure Protection

Published Sep 16, 2007

by Henry H. Willis, Tom LaTourrette, Terrence K. Kelly, Scot Hickey, Samuel Neill


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The Department of Homeland Security (DHS) has adopted a focused approach to risk reduction. DHS is moving increasingly to risk analysis and risk-based resource allocation, a process that is designed to manage the greatest risks instead of attempting to protect everything. This report applies a probabilistic terrorism model that is broadly applied in the insurance industry to assess risk across cities, to assess risks within specific cities, and to assist intelligence analysis. Among the authors' conclusions: Terrorism risk is concentrated in a small number of cities, with most cities having negligible relative risk, so terrorism estimates such as those described in the report should be incorporated into the grant allocation assessment process. DHS should consider funding the development of city profiles of major metropolitan areas receiving DHS preparedness grants. It should also develop descriptions of terrorist attack planning and operations that can be used to translate estimates from risk models of likely attack scenarios into detailed recommendations. Finally, DHS should develop tabletop exercises to test the scenarios and provide feedback.

The research described in this report was conducted by the RAND Center for Terrorism Risk Management Policy for the Department of Homeland Security.

This report is part of the RAND technical report series. RAND technical reports may include research findings on a specific topic that is limited in scope or intended for a narrow audience; present discussions of the methodology employed in research; provide literature reviews, survey instruments, modeling exercises, guidelines for practitioners and research professionals, and supporting documentation; or deliver preliminary findings. All RAND reports undergo rigorous peer review to ensure that they meet high standards for research quality and objectivity.

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