As reported in Industrial Cyber, consequence-based cyber risk management has become essential for protecting industrial control systems (ICS) and operational technology (OT) environments, particularly in sectors including energy, manufacturing, and utilities where cyber incidents can lead to severe operational, safety, and environmental consequences. Unlike traditional risk management approaches focused on threat probabilities, this strategy prioritizes the impact of cyber events, ensuring cybersecurity investments align with critical business objectives.
Emerging technologies like artificial intelligence and machine learning are transforming this approach by enabling real-time threat detection, predictive analytics, and automated responses, though challenges such as limited data and fragmented systems persist. Key performance indicators—mean time to detect (MTD) and mean time to respond (MTTR)—help organizations refine their strategies, while entities can scale efforts using simplified models and external expertise.
Sarah Freeman, chief engineer for intelligence, modeling, and simulation at MITRE’s Cyber Infrastructure Protection Innovation Center, told Industrial Cyber that consequence-based cyber risk management strategies should dovetail with existing risk management approaches. “Put another way, a cyber risk mitigation program should exist as a dimension within other risk programs, not supersede it.”
She added that risk management programs should be framed against the critical functions or activities an organization must perform to succeed and, ideally, flourish. “There are multiple methods and approaches to identify potential risks to an organization. Regardless of which is used, organizations should validate that cyber-induced risks, whether by malicious actors or normal failures, are properly accounted for.”