Integration of AI and ML for Cloud Security and Threat Detection

Author(s)	Chandrasena Cheerla
Country	United States
Abstract	This comprehensive article explores the integration of artificial intelligence and machine learning technologies in cloud security, focusing on implementation strategies, challenges, and future directions. The research examines how AI-powered security solutions transform threat detection, predictive analytics, and incident response in cloud environments. The study investigates key challenges including data privacy, model interpretability, and infrastructure integration while presenting best practices for successful implementation through phased approaches and continuous learning frameworks. The article encompasses both current capabilities and emerging trends in neural network architectures, automated response mechanisms, and zero-trust integration, providing insights into the future landscape of AI-enhanced cloud security.
Keywords	AI-Powered Cloud Security, Predictive Analytics, Threat Detection Automation, Security Implementation Frameworks, Model Interpretability
Field	Computer
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-29
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.34083
Short DOI	https://doi.org/g8xgpb

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International Journal For Multidisciplinary Research