Abstract

The present research compares the efficiency of AI-based user behavior analysis to conventional security mechanisms in cloud environments. It specifically tests their precision, velocity, and predictive capacity for identifying and acting upon cyber attacks. As the adoption of the cloud continues to increase, incorporating Artificial Intelligence (AI) and machine learning into security infrastructures has become increasingly important. The study investigates the performance of AI-based security systems, using sophisticated pattern recognition and anomaly detection, compared to conventional methods in detecting deviations from normal user behavior in cloud environments. Adopting a quantitative research approach, the study utilizes a systematic survey of cybersecurity professionals in various industries like finance, healthcare, IT, retail, and government. The survey, comprising closed- ended and Likert-scale questions, collects information on professionals' attitudes and experiences with AI-based versus conventional security methods. Data from a purposive sample of 243 cybersecurity professionals are examined through multiple regression methods to evaluate the effect of such security systems on threat detection and response effectiveness in cloud environments. Results show that although both AI-based and conventional methods improve threat detection accuracy significantly, conventional methods have a marginal advantage. Yet, AI-based solutions show better predictive ability and overall security performance. These results suggest that a hybrid security strategy is essential for cloud security, combining AI’s advanced predictive analytics and adaptability with the reliability and speed of traditional security measures. Such an integrated approach is recommended to address the complex and evolving nature of cloud-based cyber threats effectively. This research provides useful information for IT practitioners and companies on how to best optimize AI-powered security integration in cloud environments. It highlights the changing role of AI in cloud security and the need to strike a balance between new AI-based solutions and proven security practices in order to construct an integrated and robust cloud security framework.