Enhancing Cloud Security in AWS Using AI-Powered Anomaly Detection and Predictive Analytics

Cloud computing has transformed the landscape of modern IT infrastructure, and Amazon Web Services (AWS) has become one of the leading platforms for enterprises around the globe. Yet, as cloud environments have become more complex and larger in scale, they pose serious security risks, such as data breaches, insider threats, and advanced cyberattacks. Traditional security methods that depend on rule-based monitoring are often inadequate in the face of new and emerging threats, failing to ensure timely detection. This paper introduces a unique AI based anomaly detection and predictive analytic framework towards securing the cloud (AWS). This proposed model incorporates ML and DL algorithms for AWS-native security tools like AWS GuardDuty, AWS Security Hub, AWS CloudTrail. The system identifies abnormal behavior using both supervised and unsupervised learning methods to forecast potential security threats before they happen. Results from scientific experiments exhibit that the proposed intelligent automated system which utilises AI techniques for threat detection produces a 28.7% and 19.5% increase in threat detection accuracy and reduction of false positives compared to non-AI past proposed systems based on rules. More importantly, the time taken to identify real-time threats is cut down by 35% — allowing for quick incident response. It further assesses how ensemble learning models such as Random Forest, LSTM(Long Short-Term Memory) and Autoencoders improve anomaly detection performance. The advantages of deploying predictive analytics to complement AWS security controls to guard against APTs & insider attacks – a case study of a large-scale AWS deployment. Set your security nests at the top; it shows that AI-powered anomaly detection considerably boosts AWS security measures, creating a more resilient cloud habitat in finding the advance cyber threats. The proposed framework encompasses a novel combination of machine learning techniques and cloud-specific metrics to enhance the complexity and electricity of AWS-based security operations, ultimately providing a significant advancement in the field.