AI-Driven Cybersecurity for IoT–Cloud Ecosystems

Abstract

The convergence of the Internet of Things (IoT) and cloud computing has created a highly distributed, data-intensive ecosystem that drives innovation across industries. However, the same integration introduces complex cybersecurity risks due to device heterogeneity, scalability requirements, and dynamic threat landscapes [1], [4], [7]. Traditional security measures are insufficient in such environments, creating demand for adaptive, intelligent, and proactive defense mechanisms [16], [17]. Artificial intelligence (AI) offers powerful capabilities for intrusion detection, anomaly detection, malware analysis, and predictive threat modeling [3], [6], [9]. This paper explores how AI techniques ranging from machine learning and deep learning to federated and reinforcement learning are being applied to strengthen IoT–cloud ecosystems against evolving cyberattacks [2], [10], [11]. The discussion covers architectural models, real-world deployments, challenges such as adversarial AI, privacy, and compliance, and emerging directions like explainable AI and quantum-safe security [13], [24], [30]. The study concludes that AI-driven cybersecurity has transformative potential but requires careful balancing of efficiency, interpretability, and resilience to ensure trust in IoT–cloud ecosystems [19], [23], [31].