Model context protocol for cloud security: transforming AI threat detection through interpretable intelligence

The increasing sophistication of cyber threats demands AI systems that deliver accurate detection and actionable, human-centered explanations. This study introduces an Enhanced Model Context Protocol (MCP) framework for cloud security that tightly integrates threat detection with operationally useful explainable AI outputs. Using 1,939,207 real AWS CloudTrail events, a Random Forest classifier (augmented by temporal and administrative features) achieves 83.4% test accuracy; importantly. The MCP explainer transforms those machine-learning outputs into structured, analyst-ready narratives that support cybersecurity operations. The framework demonstrates how operational interpretability can be achieved by combining model signals (feature importances and temporal windows) with LLM-based contextualization to generate precise mitigation steps and investigation priorities. We compare MCP explanations to standard post-hoc methods and show that MCP delivers machine learning explainability tuned for SOC workflows (reduced cognitive load, actionable recommendations, and SIEM-compatible JSON outputs). Finally, we present a production-ready Elasticsearch integration that proves the feasibility of embedding MCP explanations directly into enterprise SIEM pipelines. This work offers a practical, reproducible path from accurate detection to interpretable, operational intelligence in cloud environments.