Cybersecurity: Using AI for Consequence-Based Cyber Risk Management

A recent Industrial Cyber article recommends that industrial companies take a consequence-based approach to cyber risk management. This model seeks to prioritize potential threats by focusing not on their probability, but on their possible consequences to industrial control systems (ICS) and operational technology (OT) environments.  However, companies often struggle to assess the probable damage of cyber-attacks due to “inadequate historical data or broken-down information systems.”

The article says that many companies have sought to address this shortcoming by using analytics and threat intelligence technologies. It contends that AI and machine learning tools are set to transform this process:

By allowing real-time danger recognition, predictive analytics, and automatic response systems, artificial intelligence (AI) and machine learning (ML) are set to transform consequence-based cyber risk management. By enabling businesses to foresee potential outcomes and spot trends, these systems can help them address problems before they grow. Better accuracy and performance of consequence-based cyber risk management will increasingly depend on more mature AI and ML technologies.

The article quotes an industry expert as stating that the advent of AI agents offers companies a much wider range of risk management capabilities.   Specifically, “AI agents are ideal for gathering data from disparate sources, analyzing large datasets, and other tasks that can be burdensome and time-consuming for security and risk management teams.” As a result, AI agents & AI-based solutions have been rapidly adopted in consequence-based risk management because the technology excels at removing obstacles to accurate risk assessment and effective risk management.