Complex interconnected societies and their infrastructures are inherently vulnerable to threats. Hence, robust approaches, processes, and technologies that strengthen the resilience of such systems must undergo a seamless process of development and improvement. In the field of critical infrastructure protection (CIP), simulation software needs to model, assess, and anticipate threatening external factors like extreme weather events, natural disasters, malicious human activities as well as degradation due to natural aging. In this context, the use of open source (OS) software and open data can support transparency and reproducibility when properly implemented and help practitioners that aim to address the problem within a digital twin framework, enhancing interoperability, data sharing, and model reuse. This contribution highlights the use of OS tools in five CIP cases: (i) the simulation of pedestrian movements, which plays a crucial role in safety analyses of public spaces. Here, OS software enables integration of pedestrian dynamics models into coupled multi-physical scenarios; (ii) the analysis of airborne contaminant dispersion processes, for which a model has been derived based on the EZyRB and Queens OS packages to perform model order reduction and uncertainty quantification analyses that lead to rapid, comprehensive, and reproducible simulations; (iii) in the same scenario, the hIPPYlib OS package has been used to develop methods for the optimal placement of sensors, including non-stationary sensor-platforms such as drones. These methods are designed for applications in CIP, like evacuation planning after contaminant releases, where timely and reliable information is essential; (iv) Open-access datasets were used to detect structural damages in wind turbine blades by developing a convolutional neural network that extracts information from aerodynamic pressure time series measurements and provides promising results in experimental validation; (v) lastly, OS software and data have been used as a robust starting basis to extend the inflow rate forecasting model of a wastewater treatment plant, to also predict the inflow chemical composition.