QUANTITATIVE RISK MODELING FOR DATA LOSS AND RANSOMWARE MITIGATION IN GLOBAL HEALTHCARE AND PHARMACEUTICAL SYSTEMS

Abstract

This study addresses the escalating problem of data loss and ransomware in globally networked healthcare and pharmaceutical ecosystems, where cross-border cloud and enterprise interdependencies amplify tail risk. The purpose is to develop and test a quantitative risk model that links measurable control maturity to three outcomes: perceived 12-month ransomware likelihood, expected data-loss severity, and expected financial loss. Using a quantitative, cross-sectional, case-based design, we surveyed security, IT, and governance leaders from cloud-enabled and on-premise enterprise cases across providers, payers, pharmaceutical manufacturers, and CROs, and triangulated results with purposively selected organizational cases. Key variables captured six capability domains on five-point Likert scales: Security Control Maturity, Backup and Recovery Readiness, Network Segmentation and Zero-Trust, Security Awareness and Training, Third-Party Risk Management, and Regulatory Compliance Posture. The analysis plan comprised descriptives, correlations, ordered logit or probit for ordinal outcomes, and log-linear OLS for transformed financial-loss bands, with interactions for architectural and governance complementarities, and cluster-robust or heteroskedasticity-consistent errors plus fixed effects for segment and region. Headline findings show that Backup and Recovery Readiness is the strongest predictor of lower severity, Network Segmentation and Zero-Trust most reduces ransomware likelihood, and Third-Party Risk Management, especially when paired with auditable compliance posture, yields the largest percentage reduction in expected financial loss, while size, cloud intensity, and IT or OT coupling raise baseline risk but are partially offset by these controls. Implications prioritize immutable, routinely tested backups, micro-segmentation with least privilege, enforceable vendor governance, and board-visible resilience metrics that translate coefficients into Expected Annual Loss and Expected Shortfall for capital allocation.