In IoT-based systems, managing risk effectively is crucial for maintaining operational continuity, especially when faced with attacks on physical assets such as sensors or actuators. These situations demand immediate and coordinated human responses to minimize damage. This paper explores the initial challenges in managing and predicting the evolution of human-involved workflows in such contexts, especially in the absence of comprehensive historical data for model training. We propose a novel approach leveraging digital twins, modeled using BPMN 2.0, to facilitate knowledge transfer across semantically similar workflows. This methodology allows for preliminary predictions regarding unmonitored workflows by utilizing insights from previously established ones. We present initial results from both synthetic and real-world data, which suggest the potential of our approach to enhance risk management practices in IoT settings. These findings are intended to foster discussion and further exploration within the research community.