AI-DRIVEN CYBERSECURITY IN INDUSTRY 4.0: A GLOBAL COMPARATIVE ANALYSIS OF DETECTION METHODS, CHALLENGES, AND RESILIENT SOLUTIONS

The study provides a comprehensive review of cybersecurity detection methods within Industry 4.0, focusing on enhancing system resilience and user trust. The research uses a qualitative approach to analyze nine case studies from Africa, Asia, and the West to identify key challenges, detection strategies,and region-specific insights.The findings emphasize the critical role of AI-driven and hybrid detection systems, which have proven effective in mitigating advanced cyber threats such as ransomware, distributed denial of service (DDoS), and false data injection attacks. Comparative analysis highlights common global challenges,including resource limitations,human-related vulnerabilities, and compliance issues, while addressing unique regional factors such as infrastructure gaps and evolving threat sophistication.The study proposes a cybersecurity taxonomy integrating machine learning, real-time anomaly detection, and adaptive threat response mechanisms for Industry 4.0 environments. Recommendations for practitioners include adopting AI-enhanced intrusion detection systems (IDS), implementing energy-efficient IoT security solutions, and conducting regular system audits. Policymakers are encouraged to mandate adherence to global cybersecurity standards, foster international collaboration, and invest in workforce capacity building initiatives. The review, therefore, strengthens global cybersecurity frameworks,improves organizational resilience, and fosters public trust in the secure integration of Industry 4.0 technologies.