AI-Driven Threat Detection: Enhancing Cloud Security with Cutting-Edge Technologies

Nina, Patel and Ethan, Kim (2019) AI-Driven Threat Detection: Enhancing Cloud Security with Cutting-Edge Technologies. International Journal of Trend in Scientific Research and Development, 4 (1). pp. 1362-1374. ISSN 2456-6470

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Abstract

In an era where cyber threats are becoming increasingly sophisticated and pervasive, traditional security measures often fall short in protecting cloud environments. This article delves into the transformative role of artificial intelligence (AI) in enhancing threat detection and response mechanisms within cloud security frameworks. We explore the limitations of conventional security approaches and demonstrate how AI-driven technologies can significantly improve threat identification through advanced analytics, machine learning algorithms, and behavioral analysis. The article discusses key applications of AI in threat detection, including anomaly detection, predictive modeling, and automated incident response, showcasing real-world case studies that illustrate successful implementations. Additionally, we examine the integration of AI with existing security tools, emphasizing best practices for leveraging these technologies to create a robust, proactive security posture. As organizations continue to migrate to the cloud, adopting AI-driven threat detection strategies will be essential for mitigating risks and safeguarding sensitive data. This article serves as a comprehensive guide for security professionals seeking to understand and implement cutting-edge AI technologies in their cloud security initiatives, ultimately empowering them to stay ahead of emerging threats in a rapidly evolving digital landscape.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	24 Oct 2024 05:20
Last Modified:	24 Oct 2024 05:20
URI:	http://eprints.umsida.ac.id/id/eprint/14264

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