Quantitative Analysis of Cybersecurity Risk: A Data-Driven Report

This report, based on a presentation by Douglas Hubbard and Richard Seiersen, delves into the critical question of how to effectively measure cybersecurity risk. It critiques traditional ordinal scoring methods and heat maps, highlighting their limitations and the potential for inaccurate assessments. The report emphasizes the importance of quantitative analysis, advocating for data-driven approaches and the use of statistical models to overcome the shortcomings of subjective expert opinions. It explores the application of techniques like Monte Carlo simulations, loss exceedance curves, and the use of beta distributions for assessing uncertainties. The report presents compelling research, including historical models, and the application of quantitative risk analysis methods to improve decision-making. It also addresses common misconceptions about data requirements and the pitfalls of overconfidence, providing a comprehensive guide to measuring and managing cybersecurity risks more effectively. The report concludes by suggesting that this approach is more effective compared to unaided expertise or soft scoring methods.