This chapter explores the critical role of safety barrier management in hydrogen refueling stations. Using Acoustic Emission Testing (AET) for Type IV composite overwrapped pressure vessels (COPVs) as a case study, the chapter presents the integration of Human Reliability Analysis (HRA) and Bayesian Network (BN) modeling in continuous evaluation and support of safety barriers. The study identifies tasks such as sensor performance evaluation and test completion as critical points sensitive to human errors and performance-shaping factors. These findings highlight the need for robust operator training, intuitive interfaces, and adaptive environmental controls to enhance barrier effectiveness. Additionally, the study emphasizes the importance of maintaining barrier independence and robustness, as well as fostering a proactive safety culture within organizations. The integration of digital technologies such as machine learning and real-time data analytics is proposed to improve defect detection and predictive insights, providing a pathway for advancing hydrogen safety management. By aligning these methodologies with established safety theories, the chapter offers a comprehensive framework for improving safety barrier management in hydrogen storage systems and other high-risk industrial applications.