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Beyond Heatmaps: Quantifying Cyber Risk with Bayesian Networks

This webinar is sponsored by the Forensic Accounting Section
(Learn more about our Section HERE.)

The May 2021 Colonial Pipeline Corporation ransomware attack showcased stereotypical weaknesses in cybersecurity modeling, controls, and compliance monitoring. A widespread deficiency in cyber risk assessment, and risk assessment more generally, is the failure to quantify risk. Most organizations settle for so-called “heat maps” or similar technology, which attempt to assess and rank risks but suffer from analytical deficiencies that magnify downside risk by misallocating control and mitigation resources. In his book, Logic, or the Art of Thinking (1662), Antoine Arnauld wrote, “Fear of harm ought to be proportional not merely to the gravity of the harm, but also to the probability of the event.” Three centuries later, Peter L. Bernstein declared, “Without numbers, there are no odds or probabilities; without odds and probabilities, the only way to deal with risk is to appeal to the gods and the fates. Without numbers, risk is wholly a matter of gut.” This presentation illustrates how risk – including cybersecurity risk -- can be predictively estimated and optimized using Bayesian networks, influence diagrams, and decision trees.

Learning Objective(s): Participants will be able to compare and contrast Bayesian networks to heatmaps and explain, at a basic level, how Bayesian networks can be used to predict and optimize cyber security risk.

CPE Information: Information Technology - 1.2CH

Program Level: Basic

Prerequisites: None

Advance Preparation: None

Delivery Method: Group Internet Based

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