The aim of this paper is to shed light on this issue and come up with frequencies of different types of cyber events. This means combining, testing, validating and analyzing frameworks, modeling approaches and using different types of analysis techniques, to create the most accurate and realistic frequency distribution.Īccording to Kovrr’s simulations, even a slight change of 0.02 in frequency of cyber events can cause up to 30-40% increase in the annual overall loss. The challenges of predicting frequency of cyber events includes: lack of data or very sparse data stemming from enterprises not willing to expose themselves, the natural chaotic dynamic of cyber events, understanding the natural target population of certain events, etc. The fast pace of change makes it difficult for corporations to rely solely on past data of the industry. Reliable data has been sparse, and the technology landscape is fluid and constantly evolving. How can you analyze the frequency of cyber events for cyber risk modeling?Ĭybercrime and the variables involving data loss cannot be quantified in the same manner as in other industries. It’s not enough to have great cyber data - you need to group and analyze the data correctly to properly predict your probability of suffering a cyber attack. If you really want to have accurate numbers for cyber risk quantification of cyber events, then you need to have great frequency data.
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