Unfolding models for ordinal data in cyber risk assessment

In an increasingly digitalized world, where organizations are a ected by technological evolution, cyber attacks are multiplying rapidly. They have an impact on every class of business and no industry can consider itself immune to them. Quantitative loss data are rarely available while it is possible to obtain a qualitative evaluation, expressed on a rating scale, from experts of the sector. Hence, we focus on ordinal data models for cyber risk evaluation (rating) with particular emphasis on a mixture model taking into account the uncertainty in the process of scoring. We examine a set of data regarding cyber attacks that occurred worldwide before and during the pandemic due to Covid-19. The aim of our analysis is to investigate if Covid-19 has affected experts' uncertainty and assessment, and identify the relevant factors which influuence the severity of an attack.