Journal of Ai ML DL

Journal of Ai ML DL | Online ISSN 3070-2143
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RESEARCH ARTICLE   (Open Access)

Business Intelligence and Explainable Machine Learning for Capacity-Constrained Cyber Disruption Prediction: A Simulation-Based Decision Framework for U.S. Enterprises

Masrul Hasan1*, Md. Rezaul Hasan2, Kazi Rakib Hasan Saurav3, Md. Arifur Rahman4, Md. Fazle Alahi Bhuiyan5

+ Author Affiliations

Journal of Ai ML DL 2 (1) 1-8 https://doi.org/10.25163/ai.2110974

Submitted: 11 August 2026 Revised: 20 October 2026  Published: 28 October 2026 


Abstract

Background: Enterprise cybersecurity teams must decide which organizations, services, or business units to investigate before disruption occurs, yet predictive models are usually judged by ranking accuracy alone, ignoring probability quality and limited analyst capacity. Objective: To propose and demonstrate a business intelligence (BI) and explainable machine learning framework that evaluates cyber disruption forecasts jointly on discrimination, calibration, alert capacity, and decision value.Methods: A simulated panel of 600 enterprise profiles across ten sectors was observed weekly for 52 weeks (31,200 enterprise-weeks; twenty predictors). Logistic regression, random forest, and gradient boosting predicted material disruption within fourteen days, trained on weeks 1–39 and tested on weeks 40–52 (7,800 observations; prevalence 20.8%). Evaluation included ROC-AUC, PR-AUC, Brier skill score, and a 15% alert-capacity operating point.Results: Discrimination was modest and statistically indistinguishable across models (ROC-AUC 0.701, 95% CI 0.686–0.716; 0.694; 0.693). Logistic regression and random forest probabilities were worse than a prevalence-only forecast (Brier skill score −0.251 and −0.148), whereas gradient boosting improved on it slightly (0.089). At the capacity threshold, logistic regression flagged 14.8% of enterprise-weeks with 47.0% precision (2.3-fold above prevalence) but captured only 33.4% of positive windows. Conclusions: The best-ranking model was not the best source of absolute risk, and a capacity-feasible queue missed two-thirds of events. Forecasts should be recalibrated before use in expected-loss reporting and validated on real enterprise data before operational claims are made.

Keywords: business intelligence; cybersecurity analytics; machine learning; probability calibration; alert capacity; explainable artificial intelligence; third-party risk; simulation study

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