Cross-Comparative Study of Cloud-Native Security Platforms to Detect and Neutralize Insider Attacks in Online Retail

Abstract

Cloud-native security platforms in online retail environments govern a wide range of monitoring and detection technologies designed to thwart insider threats. Internal actors who possess legitimate credentials and an understanding of system operations can subvert traditional defenses by blending illicit activities into routine workflows. The confluence of high transaction volumes, fast-moving development cycles, and dispersed infrastructure introduces added complexity to insider threat detection. Cloud-native architectures offer scalability and visibility, yet methods for identifying and containing insiders vary widely across available platforms. This paper presents a cross-comparative study of cloud-native security platforms that specialize in insider threat detection and neutralization. The discussion focuses on how different approaches leverage identity management, behavior analytics, microservice instrumentation, and data correlation to uncover subtle warning signs in user activities. Attention is given to real-time anomaly detection, automated policy enforcement, and the ability to track ephemeral assets across multi-cloud or hybrid infrastructures. Architectural considerations are explored to highlight how platform-specific integrations, log ingestion, and analysis pipelines influence detection fidelity. Strategies for refining alerts and aligning them with key business assets are examined to enhance risk mitigation. Conclusions underline the importance of a well-structured framework that addresses technical requirements, regulatory obligations, and evolving attacker tactics. Cloud-native solutions that unify continuous monitoring, contextual intelligence, and proactive mitigation can support robust protections against insider misuse, ultimately preserving customer trust and operational continuity in the competitive online retail marketplace.