AI-powered threat detection: Strengthening data platform security with LLMs

Mathew, Thomas Aerathu (2025) AI-powered threat detection: Strengthening data platform security with LLMs. World Journal of Advanced Research and Reviews, 26 (2). pp. 387-393. ISSN 2581-9615

This article explores how Large Language Models (LLMs) revolutionize data platform security by leveraging advanced metadata analytics for threat detection and mitigation. As organizations face increasingly complex security challenges in hybrid cloud environments, LLMs offer a paradigm shift in security approaches through their ability to analyze vast amounts of metadata, identify anomalous patterns, and correlate seemingly unrelated events across system layers. The article examines how these AI systems enhance real-time threat detection capabilities by identifying unusual access behaviors, privilege escalations, and suspicious data movements with remarkable precision. It further demonstrates how LLMs automate security responses through intelligent remediation actions, streamlined compliance management, and enhanced role-based access control. The integration of these adaptive threat intelligence systems with existing security infrastructure creates a comprehensive security framework that continuously learns from attack patterns, improving detection accuracy while reducing false positives and analyst workload.