X-Threat Copilot Visual Explainable AI for Predictive Cyber Threat Analysis

Abstract

Modern cyber-physical infrastructure has led to a surge in sophisticated and unpredictable cyberattacks, requiring security analysts to transition from reactive mechanisms to proactive defense strategies. This research presents X-Threat Copilot, a Visual Explainable AI (XAI)–based platform for predictive cyber threat analysis. The system integrates automated threat forecasting, real-time telemetry monitoring, model explainability, and analyst-centric visualization. Using Cyber Threat Intelligence (CTI) data, machine-learning–based anomaly detection, and explanation frameworks such as SHAP, LIME, and Attention mechanisms, X-Threat Copilot provides clear insights into why threats are predicted and how attacks evolve. The platform assists Security Operations Centers (SOCs) in reducing false-positive alerts, speeding up incident response, and improving situational awareness. The results demonstrate that combining predictive analytics with explainability builds operational trust and significantly enhances proactive cyber defense capabilities.