AI-enhanced threat detection and response framework for advanced cyber-physical smart ecosystems
Deepika Malve, C. Kishor Kumar Reddy, H. Meenal, Pagadala Indira, Kari J. Lippert · 2025
While the smart environment has achieved incredible growth that has maximized the efficiency and connectivity of its operation, it has also been found to raise complex security issues. From complex cyberattacks to physical invasions, such environments, ranging from smart cities and industrial IoT to vital infrastructure, are seriously threatened. The administration of these risks is undergoing a profound shift with AI, which can automate, predict, and adapt to solutions that will be implemented in order to better threat detection, response, and overall management of security. This research aims to elucidate the importance of various AI technologies involved, including machine learning, anomaly detection, predictive analytics, and edge computing in AI-driven threat detection and the developing of robust threat management frameworks. In these regards, the real case studies are effective in demonstrating useful applications and effects of AI in industries such as healthcare and urban infrastructural development. But even with AI-driven threat management, there are disadvantages in play: data complexity, false positives and negatives, and adversarial attacks are some of them. To ensure proper implementation, this chapter discusses the ethical and privacy issues surrounding AI technologies. Future directions of AI in threat management include designing autonomous cybersecurity systems, explainable AI (XAI), and quantum-resilient AI. The safe development of smart environments into highly connected environments will rely on integration with blockchain and other edge-of-the-art technologies and developments in edge AI for scalability. This quite comprehensive review is sort of a handbook to understand the existing scope, challenges, and potential of artificial intelligence for smart environment threat management.