HE0933 : Artificial Intelligence in Industrial Security

 This practical and highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops. 

 

This course is designed to provide participants with a detailed and up-to-date overview of Artificial Intelligence in Industrial Security. It covers the role of AI in industrial security and the differences between traditional and AI-driven security approaches; the machine learning (ML) and deep learning (DL) and supervised and unsupervised learning; the role of neural networks in security applications and AI algorithms commonly used in industrial security; the common threats in industrial cybersecurity and AI as a tool for cyber threat detection; and the AI-based anomaly detection in industrial networks and incident response and mitigation. 

 

Further, the course will also discuss the computer vision and facial recognition in security, AI-based behaviour analysis and anomaly detection; the integration of AI with CCTV, access control and ethical concerns and regulatory compliance; the security vulnerabilities in industrial IoT (IIoT), Al-driven IIoT threat monitoring and role of AI in predictive maintenance for security; the AI-based risk assessment in IIoT environments and data collection and preprocessing for AI Models; and the AI for intrusion detection and prevention and AI-driven access control systems. 

 

During this interactive course, participants will learn the AI in predictive risk assessment, AI for network security monitoring and machine learning for anomaly detection; the user behavior anomalies, AI-based identity and access monitoring; preventing data leaks with AI-driven analysis and integrating AI with HR security policies; the AI-driven object and person detection, real-time crowd behavior analysis, perimeter security monitoring and AI-assisted facial recognition and tracking; the AI for Industrial facility security, AI-based weapon and threat detection and AI for perimeter and border security; the AI-powered emergency response systems, predicting and mitigating industrial accidents and AI-assisted disaster management; the recovery real-time AI-based communication during crises; the AI-driven robotics in security, malware detection and prevention, phishing and social engineering prevention and security information and event management (SIEM); the AI for automated incident response, data encryption, privacy protection and ethical and legal aspects of AI in security; the role of blockchain in AI security applications, enhancing data integrity using blockchain and AI and AI-driven smart contracts for security enforcement; the predictive threat intelligence using AI, AI in cyber threat landscape mapping and AI-driven forensic analysis; and the AI-based decision support systems in security and AI in industrial security infrastructure.