Ai-Driven Threat Detection and Prevention in Cloud Computing Environments

Abstract

Cloud computing has become a cornerstone of modern IT infrastructure, offering scalability and efficiency but also exposing organizations to evolving cyber threats such as data breaches, insider threats, and advanced persistent threats (APTs). Traditional security mechanisms struggle to address these dynamic challenges, necessitating the integration of AI-driven threat detection and prevention strategies. This conceptual paper explores the comparative effectiveness of supervised learning, unsupervised learning, reinforcement learning, and hybrid AI models in cloud security. Supervised learning excels in identifying known attack patterns, while unsupervised learning is crucial for detecting zero-day threats and anomalies. Reinforcement learning enables self-adaptive security measures, and hybrid models offer a comprehensive, multi-layered approach to cloud security. However, AI-driven cybersecurity faces significant challenges, including data privacy risks, bias in threat detection, adversarial AI attacks, and lack of model interpretability. Emerging AI trends such as federated learning, quantum security, and explainable AI (XAI) are shaping the future of cloud security, while regulatory frameworks like GDPR, NIST AI Risk Management, and the EU AI Act play a crucial role in standardizing ethical AI use. This study provides insights into the strengths, weaknesses, and future directions of AI-driven cloud security, offering recommendations for researchers, policymakers, and cybersecurity practitioners to enhance AI resilience against emerging threats.