Article

Quantifying the Financial Impact of Cyber Incidents: A Machine Learning Approach to Inform Legal Standards and Risk Management 

---

Abstract

The escalating frequency and sophistication of cyber incidents present a significant challenge for organizations, insurers, and legal systems, which often struggle to quantify financial risk and establish clear standards of liability. Traditional risk assessments are frequently subjective and lack the empirical rigor needed to connect specific incident characteristics to financial outcomes. This research addresses this gap by developing a machine learning model to predict the financial impact of security breaches and identify the key drivers of cost. Using a dataset of 5,000 incidents enriched with threat, asset, and organizational data, this study employed two ensemble models, a Random Forest Regressor and XGBoost, to perform a regression analysis. The results demonstrate that a predictive model can successfully account for a significant portion of the variance in breach costs. The Random Forest model emerged as the superior performer, explaining approximately 49.3% of the variance (R² = 0.4932) in financial impact on unseen test data, with a Mean Absolute Error of $174.89k. The feature importance analysis yielded a clear and powerful insight: the volume of data breached is the single most dominant predictor of financial loss, with an importance score (~0.83) that far exceeds all other variables, including threat type, asset vulnerability, and incident resolution time. This finding has profound implications, suggesting that legal and regulatory standards of 'due care' should prioritize controls aimed at data minimization and the prevention of large-scale data exfiltration. The study provides a quantitative framework to help courts assess damages more empirically, allows insurers to refine underwriting criteria based on data exposure risk, and guides organizations to focus cybersecurity investments on protecting their most valuable data assets at scale.

Keywords: Cyber Risk, Cybersecurity, Data Breach, Financial Impact, Machine Learning

How to Cite:

Alamsyah, R. & Wahyuni, S., (2025) “Quantifying the Financial Impact of Cyber Incidents: A Machine Learning Approach to Inform Legal Standards and Risk Management ”, Journal of Cyber Law 1(3), 264-281. doi: https://doi.org/10.63913/jcl.v1i3.48