The advent of 6G networks necessitates innovative solutions to address the limitations of traditional terrestrial wireless communications, particularly in remote and challenging terrains. The space-air-ground integrated network (SAGIN) combining satellite, aerial, and terrestrial segments, is a promising architecture to provide seamless, high-speed, and reliable connectivity. However, ensuring secure communication across these heterogeneous layers remains a challenge. This paper explores the integration of reconfigurable intelligent surfaces (RISs) in SAGIN, presenting a comprehensive strategy to enhance physical layer security (PLS) across the network. The paper first analyzes existing PLS challenges in SAGIN and explores how RISs integration and enabling technologies can address these security concerns. A practical case study demonstrates the application of RISs in the aerial layer to enhance SAGIN security. The findings demonstrate RISs' potential to mitigate eavesdropping risks, ensuring reliable communication systems. Finally, the paper identifies open research directions and challenges for future investigations.

In light of the revolutionary requirements of the sixth generation (6G) and beyond wireless networks, reconfigurable intelligent surface (RIS) and rate-splitting multiple access (RSMA) have emerged as pivotal technologies due to their potential for improving spectral efficiency, user fairness, and interference management. This survey explores the theoretical foundations, architectural frameworks, and design strategies of RIS-assisted RSMA, emphasizing the combined adaptability of RIS's wireless propagation control and RSMA's multiuser flexibility for dynamic spectrum management. The article first discusses the fundamental concepts of RSMA and RIS technologies. Then, we investigate various enabling technologies for RIS-RSMA networks, highlighting key advancements in interference mitigation, energy efficiency, and security for future networks. Subsequently, some optimization techniques crucial for enhancing RIS-RSMA network performance are presented. Additionally, we examine advanced machine learning (ML) approaches that enable RIS configurations to dynamically adapt to changing network requirements. Techniques such as deep reinforcement learning support real-time adjustments, creating more scalable and resilient RIS-RSMA architectures. Finally, we discuss open research directions for advancing RIS-assisted RSMA in emerging 6G applications. We also consider the potential of advanced ML techniques, including quantum-based ML and large language models, to handle the complexities of large-scale network optimization. This comprehensive survey addresses critical challenges and current advancements. It offers a roadmap for future research in RIS-assisted RSMA networks, paving the way for robust, intelligent, and adaptive 6G wireless communication systems.