Cyber Insurance: recent advances, good practices & challenges

The aim of this ENISA report is to raise awareness for the most impact to market advances, by shortly identifying the most significant cyber insurance developments for the past four years – during 2012 to 2016 – and to capture the good practices and challenges during the early stages of the cyber insurance lifecycle, i.e. before an actual policy is signed, laying the ground for future work in the area

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

This paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. T he goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIP-based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times

Robustness of Optimal Investment Decisions in Mixed Insurance/Investment Cyber Risk Management

An integrated risk management strategy, combining insurance and security investments, where the latter contribute to reduce the insurance premium, is investigated to assess whether it can lead to reduced overall security expenses. The optimal investment for this mixed strategy is derived under three insurance policies, covering, respectively, all the losses (total coverage), just those below the limit of maximum liability (partial coverage), and those above a threshold but below the maximum liability (partial coverage with deductibles). Under certain conditions (e.g., low potential loss, or either very low or very high vulnerability), the mixed strategy reverts however to insurance alone, because investments do not provide an additional benefit. When the mixed strategy is the best choice, the dominant component in the overall security expenses is the insurance premium in most cases. Optimal investment decisions require an accurate estimate of the vulnerability, whereas larger estimation errors may be tolerated for the investment-effectiveness coefficient

Siting new fire stations in Istanbul: a risk-based optimisation approach

The locations of emergency medical service and fire stations are of paramount importance in order to achieve an effective and reliable emergency response system. As communities grow and demographics change, it may become necessary to replace existing stations or add more stations to satisfy the increasing public demands for emergency responses. With its fast growing population, new urban developments, and heavy traffic conditions the location planning problem for new fire stations has recently gained greater importance for the City of Istanbul. In this paper, we describe an integer programming-based approach to address this problem. We formulate the problem as a dynamic risk-based multiple coverage model. In this model, the demand regions are required to be serviced from two or three stations within the desired time limits according to the risk categories they are associated with. We first identify the risk category of each region and then solve the model to optimally determine the locations for the new fire stations. The results show that new fire stations can be sited more effectively utilizing the described optimization approach