Ethical Risk Modeling for Trustworthy ML-Based Cyber Defense

Machine learning (ML) has significantly advanced cybersecurity by enabling intelligent detection of threats such as unauthorized access and malicious behavior. However, these systems introduce ethical risks, particularly regarding algorithmic bias and data privacy. This paper presents Ethic-Guard, a principled framework designed to embed fairness and privacy safeguards into ML-based cyber defense systems. Ethic-Guard follows a two-phase protocol: Risk Assessment, which quantifies fairness using disparate impact and privacy via feature exposure; and Mitigation, which applies corrective actions such as model retraining or feature elimination based on a composite risk score R = 0.4F + 0.4P + 0.2A, where F, P, and A represent fairness, privacy, and accuracy, respectively. These weights prioritize ethical considerations while preserving model performance. The framework is implemented using Random Forest and Logistic Regression on the LANL cybersecurity dataset (100,000 samples; 60% benign, 40% malicious). Ethic-Guard improves fairness from 0.72 to 0.92 with Random Forest and to 0.95 with Logistic Regression, while maintaining a 90% risk detection rate. These findings demonstrate Ethic-Guard’s potential to enhance ethical accountability in ML-driven cybersecurity systems, with broad relevance to cybersecurity practitioners, AI ethicists, and policymakers in sectors such as finance, healthcare, and government.