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This study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in identifying deviations from standard user behaviors in cloud settings. Employing a quantitative approach, the study utilizes a detailed survey strategy, targeting cybersecurity professionals across multiple industries, including finance, healthcare, information technology, retail, and government sectors. The survey, comprising both closed-ended and Likert-scale questions, is designed to elicit nuanced responses on the perceptions and experiences of these professionals regarding AI-driven versus traditional security methods in cloud environments. The data, collected from a purposive sample of 243 cybersecurity personnel, is analyzed using multiple regression analysis. This analysis facilitates an understanding of the impact of different security systems on the efficacy of threat detection and response in cloud contexts. The results indicate that while both AI-driven and traditional methods significantly improve threat detection accuracy, traditional methods show a slight edge. Conversely, AI-driven systems demonstrate notably superior predictive capabilities and …