With the rapid development of cloud-network convergence, alert data processing has become a critical task in intelligent cloud-network operations and maintenance. Enterprise threat detection systems serve as a vital defense against cyberattacks. However, due to well-known challenges such as massive unlabeled data, false alerts, and lack of interpretability, threat detection remains a formidable challenge. To address these issues, we propose the Enterprise Threat Detection Explainer (ETDE), an interpretable graph neural network (GNN)-based framework designed to automatically identify attackers from intrusion prevention system (IPS) alerts. ETDE extracts relevant subgraph structures and features from security attribute graphs to model attack patterns, while providing comprehensive explanations to help security teams rapidly pinpoint threats and accelerate incident response. Evaluations on real-world operational data demonstrate that ETDE significantly reduces the manual workload for security analysts, enhancing both efficiency and reliability in threat detection.