Large Language Model-Enabled Advanced Cybersecurity Intelligence for Hybrid Cloud API Ecosystems
DOI:
https://doi.org/10.15662/IJARCST.2026.0904005Keywords:
Large Language Models, Cybersecurity Intelligence, Hybrid Cloud, API Security, Threat Detection, Security Analytics, Artificial IntelligenceAbstract
The rapid adoption of hybrid cloud architectures and application programming interfaces (APIs) has created increasingly complex cybersecurity environments that require continuous monitoring, contextual analysis, and rapid response. Large Language Models (LLMs) provide new capabilities for cybersecurity intelligence by processing heterogeneous security information, interpreting complex events, correlating threat indicators, and generating human-readable explanations. This paper proposes an LLM-enabled advanced cybersecurity intelligence framework for hybrid cloud API ecosystems that integrates cloud telemetry, API traffic, security logs, threat intelligence, vulnerability information, and enterprise knowledge into an intelligent security architecture. The framework uses LLMs alongside conventional machine learning, semantic analysis, anomaly detection, and rule-based security mechanisms to identify suspicious activities and support threat investigation. A secure API intelligence layer enables controlled interaction between cybersecurity services, cloud platforms, applications, and security operations teams. The proposed architecture incorporates identity-aware access control, encryption, API gateways, policy enforcement, continuous monitoring, audit logging, and human oversight to reduce risks associated with autonomous AI-based security decisions. The research methodology evaluates detection accuracy, false-positive rates, response latency, threat classification, API security, scalability, and explainability. The framework is intended to improve threat visibility across hybrid environments, accelerate security investigations, enhance incident response, and provide contextual cybersecurity intelligence. The study demonstrates the potential of LLM-driven security analytics to transform fragmented security telemetry into actionable intelligence while maintaining enterprise security and governance requirements.
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