Explainable Artificial Intelligence Framework for Real-Time Cyber Threat Detection in Cloud Computing

Manish Yadav

Cloud computing environments are increasingly targeted by sophisticated cyber threats that require rapid and accurate detection mechanisms. This study proposes an Explainable Artificial Intelligence (XAI) framework for real-time cyber threat detection in cloud computing environments. The proposed approach combines machine learning-based anomaly detection with explainable AI techniques to identify potentially malicious activities while providing interpretable explanations for each detection decision. The framework analyzes network traffic and system behavior to detect abnormal patterns and improve transparency in automated security decisions. The proposed model aims to reduce false positives, support security analysts in understanding detected threats, and enable faster incident response. The study demonstrates how explainable AI can improve the reliability, transparency, and practical adoption of intelligent cybersecurity systems in modern cloud infrastructures.