The rapid integration of Industrial Internet of Things (IIoT) technologies has transformed industrial environments into highly interconnected cyber-physical infrastructures in which sensors, edge devices, cloud platforms, industrial controllers, and analytics services continuously exchange operational data. This connectivity improves automation and resource utilization but simultaneously enlarges the attack surface and creates stringent requirements for real-time threat detection. Conventional intrusion detection approaches often face a tension between detection accuracy, computational overhead, response latency, and interpretability. This paper proposes a lightweight explainable artificial intelligence (XAI) framework for real-time threat detection in IIoT networks. The framework conceptually combines lightweight feature processing, resource-aware machine-learning inference, distributed edge-oriented analysis, and an explanation layer designed to translate model decisions into operationally meaningful evidence. The methodology is positioned through a critical synthesis of the provided literature concerning cloud computing, serverless architectures, machine learning services, scalable data processing, Kubernetes-based infrastructures, and cost-aware computation. The analysis indicates that architectural principles such as elastic processing, distributed computation, and resource-aware execution can support scalable IIoT security analytics, while explainability is necessary for improving analyst confidence and facilitating rapid incident interpretation. The proposed framework further incorporates a hierarchical detection mechanism in which computationally inexpensive screening is followed by deeper analysis for suspicious traffic. Its principal contribution is therefore an architectural model that attempts to balance detection responsiveness, computational efficiency, scalability, and interpretability rather than optimizing detection accuracy in isolation.