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Understanding cyber events: A model for reducing frequency and increasing resilience

This study analyses data and explores how it can inform models of potential cyber risk frequency.

How often is an organisation likely to experience an event compared to similar companies?

As cyber threats evolve and increase, it can be challenging for organisations to determine where to focus their limited cybersecurity resources, including insurance, to best support their resilience strategies. This new report shows how Marsh McLennan is using the ever-increasing amount of available cybersecurity data to help companies better understand, measure, and manage their risks.

In this study, Marsh McLennan analyzed three types of data and how they can inform models of potential cyber risk frequency. Specifically, Marsh McLennan developed “frequency modifiers” to help organisations better gauge their risk over time of experiencing a cyber event relative to the risk faced by their peers.

This report reveals that company size influences frequency modifiers.

Download the report today to learn how understanding the frequency of events can impact your organisation’s cyber risk management strategies.

This publication is not intended to be taken as advice regarding any individual situation and should not be relied upon as such. The information contained herein is based on sources we believe reliable, but we make no representation or warranty as to its accuracy. Marsh shall have no obligation to update this publication and shall have no liability to you or any other party arising out of this publication or any matter contained herein. Any statements concerning actuarial, tax, accounting, or legal matters are based solely on our experience as insurance brokers and risk consultants and are not to be relied upon as actuarial, accounting, tax, or legal advice, for which you should consult your own professional advisors. Any modelling, analytics, or projections are subject to inherent uncertainty, and any analysis could be materially affected if any underlying assumptions, conditions, information, or factors are inaccurate or incomplete or should change.

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