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Digital Twinning technology in SCADA systems has the potential to increase efficiency, reduce overall cost, and improve safety for human operators. As a significant development of Industry 4.0, Digital Twinning has opened new avenues with cybersecurity research and development, particularly in real-time anomaly and intrusion detection systems using machine learning. Digital twin models allow for the safe testing of these algorithms without exposing real systems to critical levels of risk. This paper proposes and tests a digital twinning framework specifically for SCADA cybersecurity research, focusing on software interoperability and universal interfacing. In this, we analyze industry-leading digital twinning software, such as Microsoft’s Azure Digital Twins, Siemens Digital Twin, and PTC ThingWorx to identify crucial features and limitations, particularly regarding hybrid environments and data collection capabilities. This framework utilizes technologies like containerization to address our identified limitations and offer support for virtual and hybrid SCADA networks. Our approach aims to enhance the development of anomaly/intrusion detection models, increasing the overall security posture of global critical infrastructure systems. By offering a comprehensive platform for simulation and analysis, the framework seeks to align industry practices with academic research, fostering progress in cybersecurity for SCADA systems.