article · Asian Journal of Research in Computer Science
Cloud computing environments increasingly rely on sophisticated security methods to counter evolving cyber threats. This study evaluates the comparative effectiveness of artificial intelligence-driven user behaviour analysis and conventional security measures. By surveying 243 cybersecurity professionals across sectors including finance, healthcare, information technology, retail, and government, the study examines threat detection accuracy, speed, and predictive capabilities. Analysis shows that while both conventional and artificial intelligence-driven approaches significantly enhance threat detection accuracy, conventional measures retain a slight edge. However, artificial intelligence-driven systems demonstrate markedly superior predictive power and overall security performance. Because each method offers distinct strengths, the study advocates for a hybrid security strategy. Such an approach combines the predictive analytics and adaptability of artificial intelligence with the fast, dependable responses of traditional tools to safeguard complex cloud environments against dynamic cyber attacks effectively.
As organisations across vital sectors migrate critical services to the cloud, protecting sensitive data against sophisticated cyber threats becomes imperative. Understanding how artificial intelligence complements traditional security measures allows organisations to design balanced defence systems. Rather than choosing between innovative predictive tools and trusted conventional defences, organisations can deploy integrated strategies that detect, predict, and mitigate security incidents more effectively.
The findings inform cybersecurity product developers and enterprise IT teams seeking to design integrated cloud protection suites. Because the study relies on survey data from 243 industry professionals rather than direct technical benchmarking or software prototyping, the research represents early-stage strategic insight. It guides vendors and organisations in developing hybrid security solutions that unite machine learning anomaly detection with existing traditional monitoring architectures across sectors such as finance, healthcare, and retail.
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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 overall enhanced security performance. These findings suggest the necessity of a hybrid security strategy in cloud computing. Such an approach would combine the advanced capabilities of AI, particularly in predictive analytics and adaptability, with the rapid and reliable responses of traditional methods. This integrated strategy is proposed to effectively address the unique challenges posed by the dynamic and complex nature of cloud-based cyber threats. This study provides valuable insights for both businesses and IT professionals on the effective integration of AI-driven security measures in cloud environments. It highlights the evolving role of AI in cloud security and the importance of maintaining a balance between innovative AI approaches and established traditional methods to create a robust, comprehensive cloud security framework.
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DOI: 10.9734/ajrcos/2024/v17i3424
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