Combining Cloud Computing with an Automated Process Engine to Build an Information-Based Internal Audit Decision-Making Support System

To address the core challenges of internal audit systems in cloud environments, including the trade-off between multisource sensitive data privacy and audit efficiency, rigid process execution, and insufficient mining of hidden associations in distributed enterprises, this study proposes an information-based internal audit decision-making support system integrating lightweight federated learning, adaptive differential privacy, and a BPMN-based automated process engine. Considering the increasing demand for secure intelligent information processing in cloud-edge infrastructures that also underpin large-scale electromagnetic information systems, the proposed framework enables privacy-preserving collaborative analysis while maintaining high computational efficiency. At the edge layer, a four-level privacy classification strategy is employed, where LSTM-based anomaly detection models are trained locally and only ϵ-differential privacy-protected encrypted gradients are uploaded for FedAvg aggregation in the cloud. Rényi differential privacy dynamically adjusts privacy budgets, while entropy weight-TOPSIS risk evaluation and a Neo4j knowledge graph drive adaptive task scheduling through the BPMN engine. NLP-based report generation, Elasticsearch logging, and blockchain anchoring further ensure traceability and reliability. Experimental results demonstrate an average end-to-end latency of 8.4±1.4 s, audit coverage of 93.9±2.2%, risk identification F1-score of 89.2±2.7%, data re-identification rate of 2.5±1.0%, and process automation exceeding 89%. The proposed framework effectively resolves the privacy–efficiency– process dilemma and provides a secure, intelligent, and scalable decision-support paradigm for cloud-enabled enterprise auditing, while offering methodological insights for trustworthy information processing in distributed electromagnetic and cyber-physical infrastructures.