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Articles

Large Language Model-Based Intelligent Cyber Threat Analysis for Sovereign Cloud Infrastructure

Abstract

Sovereign cloud infrastructure is becoming increasingly important for governments, regulated organizations, critical industries, and enterprises seeking greater control over sensitive data, infrastructure, digital services, and cybersecurity operations. However, sovereign cloud environments face increasingly complex cyber threats involving advanced persistent attacks, identity compromise, malicious insiders, supply-chain vulnerabilities, API exploitation, ransomware, and sophisticated lateral movement. Large Language Models (LLMs) provide new opportunities for intelligent cyber threat analysis because they can process heterogeneous security information, understand contextual relationships among events, summarize incidents, generate threat hypotheses, and assist analysts in security decision-making. This paper proposes an LLM-based intelligent cyber threat analysis framework specifically designed for sovereign cloud infrastructure. The framework integrates security logs, network telemetry, identity events, vulnerability information, cloud configuration data, threat intelligence, and incident reports into an AI-driven analytical pipeline. Retrieval-Augmented Generation (RAG), domain-specific knowledge bases, structured threat representations, and explainable reasoning mechanisms are used to improve analytical accuracy and reduce unsupported outputs. A sovereignty-aware governance layer controls data residency, model access, auditability, privacy, and policy enforcement. The proposed methodology evaluates threat detection accuracy, contextual relevance, false-positive reduction, response latency, explanation quality, and operational overhead. The framework aims to strengthen sovereign cloud security by providing intelligent, auditable, privacy-aware, and context-sensitive cyber threat analysis while maintaining organizational control over sensitive security information and AI operations.

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