Intelligent Threat Detection in Cloud Environments Using Data Science-Driven Security Analytics
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V2I2P105Keywords:
Cloud Threat Detection, Data Science–Driven Security Analytics, AI/ML in Cloud Security, Behavioral Analytics in Cloud Environments, User and Entity Behavior Analytics (UEBA), Indicators of Attack (IOAs)Abstract
The convergence of cloud computing, cybersecurity, and data science has reshaped how organizations approach threat detection. Traditional rule-based systems fail to scale in dynamic, distributed environments where threats evolve rapidly and telemetry volume is immense. This paper explores how machine learning and data science techniques are redefining intelligent threat detection across cloud platforms. It presents a comprehensive review of cloud-native attack vectors, data analytics pipelines, and real-time monitoring strategies, while integrating foundational research and emerging innovations. By evaluating use cases, model deployment techniques, and privacy-enhancing architectures, the study aims to guide the development of scalable, proactive, and intelligent security systems in multi-cloud environments
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