Transferable AI-Causal Models for Proactive Threat Detection Across Heterogeneous Cloud Infrastructures

Abstract

Cloud infrastructures have rapidly evolved into highly distributed, heterogeneous environments that integrate diverse computing resources, network configurations, and service delivery models. This complexity creates both opportunities and vulnerabilities, especially in the context of cybersecurity. Traditional machine learning methods for threat detection often struggle with domain transfer, adaptability, and explainability, particularly when applied across heterogeneous cloud infrastructures. This paper explores the development and application of transferable AI-causal models that unify causal inference with advanced transfer learning techniques to enable proactive threat detection. By leveraging causal reasoning, these models go beyond correlation-based anomaly detection to uncover causal structures that explain the origins and propagation of cyber threats. Transferability ensures that models trained in one environment can be adapted efficiently to different cloud ecosystems, reducing retraining costs and improving robustness against zero-day attacks. We discuss the architectural design, learning paradigms, and operational deployment strategies for these models, along with their advantages in scalability, transparency, and resilience. Finally, we highlight open challenges and research directions, including causal discovery in dynamic environments, cross-domain explainability, and integration with real-time decision systems.