Abstract :

This Study Evaluates The Role Of Artificial Intelligence (AI) In Improving Threat Detection Within Cloud Computing Environments, An Area Of Growing Importance Due To Increasing Security Risks. By Applying A Range Of AI Techniques Such As Machine Learning, Deep Learning, And Anomaly Detection, The Research Focuses On Enhancing Both The Accuracy And Efficiency Of Cloud Security Systems. These Methods Were Tested Using Simulated Attack Scenarios Across Different Cloud Platforms To Measure Their Ability To Detect And Respond To Threats In Real Time. The Results Indicate A Notable Improvement In Detection Performance, Along With A Reduction In False Alarms, Demonstrating The Effectiveness Of AI-driven Approaches In Strengthening Cloud Security. The Study Highlights How AI Can Significantly Enhance The Resilience Of Cloud Infrastructures Against Advanced Cyberattacks, While Also Improving The Speed And Reliability Of Incident Response Mechanisms. Additionally, The Findings Support The Integration Of AI Technologies Into Existing Cloud Security Frameworks To Create More Adaptive And Intelligent Defense Systems. The Study Emphasizes The Importance Of Continued Research In This Domain, Suggesting Future Advancements Such As Self-learning Security Models And The Use Of Predictive Analytics To Identify Potential Threats Before They Occur. Overall, This Work Lays A Strong Foundation For Further Development And Real-world Implementation Of AI-based Solutions To Secure Cloud Environments Against Evolving Cyber Threats.

Published:

16-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

2425 -2429

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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