Digital Twin and BI Synergy for Predictive Cyber Risk Management in Industrial Automation

The growing convergence of industrial automation and digital technologies has transformed modern manufacturing systems into highly interconnected environments. While this connectivity enhances efficiency and adaptability, it also amplifies the surface for cyber vulnerabilities. This study explores the synergistic integration of Digital Twin (DT) technology and Business Intelligence (BI) to enable predictive cyber risk management in industrial automation. The research underscores how digital twins, functioning as real-time virtual replicas of physical assets and processes, can simulate cyber-physical interactions, detect anomalies, and assess potential threats before they escalate. When combined with BI analytics, these digital twins transform operational data into actionable intelligence, facilitating proactive decision-making and adaptive risk mitigation. The paper presents a conceptual framework that integrates sensor data, predictive analytics, and cyber-risk indicators through a unified BI-DT platform, enabling continuous monitoring, early threat prediction, and automated resilience strategies. The proposed approach not only supports situational awareness and compliance with industrial cybersecurity standards but also enhances the overall cyber resilience of automation systems. This synergy offers a pathway toward intelligent, self-learning industrial ecosystems capable of anticipating and neutralizing cyber threats in real time.