Understanding risk in cybersecurity requires more than just identifying threats. It demands a structured approach to quantify and analyze potential losses. The FAIR (Factor Analysis of Information Risk) model offers a clear framework to do just that. This post continues the exploration of a fictitious firm’s FAIR analysis, focusing on Part 4b of the case study. I will break down key components, practical steps, and insights that help translate abstract risks into measurable t

For a potential ransomware event that could impact the availability of a fictitious medium-sized healthcare firm (see Part 1-3 of the case study used in the analysis), I meticulously conducted a Monte Carlo simulation using the FAIR technique (Taxonomy - Figure 1). The result, assuming average firm resilience and a sophisticated threat actor, was a loss of up to $1 million. This level of accuracy in the analysis, along with the inclusion of detailed cost assumptions ( as Part