AI-Driven Behavioral Anomaly Detection for Identity Threat Monitoring in Cloud Platforms

Ebubechukwu Edokwe

AI-Driven Behavioral Anomaly Detection for Identity Threat Monitoring in Cloud Platforms

Author(s)	Ebubechukwu Edokwe
Country	United States
Abstract	As organizations increasingly move to the cloud, security risks associated with identity and access management (IAM) are increased in complexity. One of the most important problems is detecting identity-based threats in these dynamic, distributed environments. AI-driven behavioral anomaly detection has become an important tool to strengthen the identity threat monitoring capability in the cloud platforms, providing real time insights into suspicious activities and potentially malicious behaviors. This article discusses the role of artificial intelligence (AI) in detecting anomalies related to identity by analyzing user behaviors and identifying behavior departures from typical usage patterns. By harnessing machine learning algorithms, cloud platforms can proactively monitor and analyze large amounts of identity and access data, shortening the time it takes to detect and mitigate security threats. The paper also points out some of the key methodologies used for AI-based anomaly detection such as unsupervised learning, clustering, and neural networks, all of which can be used for knowledge of outliers and unusual access patterns. Additionally, the article assesses how well these AI techniques are functioning to detect new threats such as insider attacks, credential abuse and unauthorized access. The combining of behavioral anomaly detection with the current Zero Trust frameworks guarantees that security policies are dynamically enforced and continually monitored. Ultimately, this approach improves cloud security by delivering a more adaptive, efficient and scalable solution for identity threat monitoring to offer organizations greater protection from evolving and shifting cyber risks.
Keywords	AI-driven anomaly detection, Behavioral anomaly detection, Cloud security, Identity threat monitoring, Zero Trust architecture, Machine learning for security, Credential abuse detection, Privilege escalation, User behavior analytics (UBA), Unsupervised learning, Supervised learning, Deep learning for security, Auto encoder-based anomaly detection, Random Forest for anomaly detection
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-11-05

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AI-Driven Behavioral Anomaly Detection for Identity Threat Monitoring in Cloud Platforms

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