PREVENTING INSIDER THREATS IN CLOUD ENVIRONMENTS: ANOMALY DETECTION AND BEHAVIORAL ANALYSIS APPROACHES

Durga Prasada Rao Sanagana

Author:

Durga Prasada Rao Sanagana

Published in

Journal of Science Technology and Research

( Volume , Issue )

Page No: 225-232

Volume , Issue

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Abstract

Consider threats pose a significant risk to cloud environments, where traditional security measures may fall short. This manuscript delves into the use of anomaly detection and behavioral analysis to mitigate these risks. We explore the unique challenges of cloud security, examine current methodologies, and provide practical insights into implementing effective insider threat detection mechanisms. By integrating these advanced techniques, organizations can enhance their security posture and protect sensitive data in the cloud. In today's digital age, the fusion of cybersecurity and network architecture is paramount to building a resilient and secure IT infrastructure. This manuscript explores the critical interdependence between these two domains, emphasizing the need for an integrated approach to safeguard against ever-evolving cyber threats. By examining current trends, challenges, and best practices, we aim to provide a comprehensive guide for organizations to enhance their cybersecurity posture through robust network architecture design.

Keywords

Insider Threats, Behavioral Analysis, Anomaly Detection, Data Security and RealTime Monitoring

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ABSTRACT:

Preventing insider threats in cloud environments is crucial as traditional security systems often fail to detect internal risks. This study investigates how anomaly detection and behavioral analysis can strengthen cloud security frameworks. We examine the core challenges posed by cloud infrastructure, especially when authorized users unintentionally or maliciously compromise sensitive systems. By evaluating current threat detection techniques and integrating behavior-based monitoring, we demonstrate effective methods for enhancing data protection. Anomaly detection highlights irregular access patterns, while behavioral analysis tracks deviations in user behavior. When combined, these strategies offer a proactive defense mechanism against insider threats. Our findings emphasize that a layered security approach—tailored to the decentralized and scalable nature of the cloud—can mitigate internal vulnerabilities. As organizations increasingly rely on cloud technologies, implementing intelligent detection systems becomes essential. This research offers practical insights into preventing insider threats in cloud environments, promoting data integrity, compliance, and operational resilience.

INTRODUCTION:

As cloud adoption accelerates, preventing insider threats in cloud environments has become a top security priority. While cloud computing enables scalability, cost-efficiency, and flexibility, it also presents complex security challenges. Insider threats—stemming from authorized users misusing access, whether accidentally or intentionally—are particularly difficult to detect in cloud settings. Traditional on-premises security solutions often lack the visibility and adaptability needed for dynamic, multi-tenant cloud platforms. Additionally, the shared responsibility model complicates accountability between service providers and users. This paper explores how combining anomaly detection with behavioral analysis can mitigate insider threats effectively. Anomaly detection identifies suspicious deviations in activity, while behavioral analysis tracks user intent and usage trends. These techniques enable real-time monitoring and rapid response, essential for securing cloud infrastructure. By adopting these advanced methods, organizations can safeguard critical data, reduce risk exposure, and maintain trust. This introduction frames our comprehensive examination of proactive strategies for preventing insider threats in cloud environments.