AI-DRIVEN THREAT DETECTION IN CLOUD-BASED APPLICATIONS

Abstract

This comprehensive article examines the evolution and implementation of artificial intelligence and machine learning technologies in cloud security environments. The article analyzes the transformation from traditional security approaches to AI-driven solutions, focusing on deep learning architectures, reinforcement learning applications, and emerging technologies. It addresses the critical challenges in training AI models for cloud security, including data-related issues and their mitigation strategies. The article demonstrates significant improvements in threat detection, response capabilities, and cost-effectiveness through a comparative analysis of traditional versus AI-driven detection methods. The article further explores future directions in automated response systems, emerging technologies such as quantum computing integration, zero-trust architectures, and blockchain-based audit trails while identifying key research opportunities in explainable AI, real-time model updating, and cross-platform threat intelligence sharing.