An AI-Powered Framework for Intrusion Detection and Threat Analysis in Cloud Computing Environments

Cloud computing has become a foundational element of digital transformation, enabling organizations to access scalable, on-demand computing resources.However, its distributed architecture, dynamic provisioning, and multi-tenant design make it highly vulnerable to cyber threats.Traditional signature-based intrusion detection systems (IDS) often fail to identify novel or sophisticated attacks.This paper presents an AI-powered intrusion detection and threat analysis framework that leverages advanced machine learning (ML) and deep learning (DL) techniques to enhance security in cloud computing environments.We implemented and evaluated multiple models, including XGBoost, Random Forest, Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) networks, achieving an accuracy of 98.7% and a false positive rate of 2.1% on benchmark datasets.The proposed framework demonstrates the potential of AI and ML for scalable, real-time threat detection, reducing security incident response time and improving overall cloud resilience.