Artificial Intelligence and Machine Learning for Critical Infrastructure Risk Mitigation: Applications, Trade-offs, and Governance Challenges
Keywords:
Anomaly detection, artificial intelligence, critical infrastructure, cybersecurity, governance, machine learning, operational technology, SCADA systemsAbstract
Critical infrastructure systems face unprecedented cybersecurity threats that exceed the capacity of traditional risk mitigation approaches. Artificial intelligence (AI) and machine learning (ML) technologies offer transformative capabilities for real-time threat detection, predictive analysis, and automated risk assessment in operational technology (OT) and industrial control system (ICS) environments. However, the deployment of AI/ML in safety-critical infrastructure introduces complex trade-offs between speed and accuracy, availability and security, explainability and performance, and automation and human control. This article provides a comprehensive analysis of AI/ML applications in critical infrastructure protection, evaluates key trade-offs in system design, and addresses limitations, ethical considerations, and governance frameworks necessary for responsible AI deployment. Drawing on recent literature and empirical evidence, this article demonstrates that while AI/ML significantly enhances threat detection and predictive capabilities, successful implementation requires careful attention to organizational readiness, domain expertise integration, explainable AI techniques, and robust governance structures that balance innovation with safety and accountability.
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