Automated Driving Systems (ADS) represent a highly complex System-of-Systems (SoS), posing significant challenges in validation and verification. Traditional validation approaches are becoming insufficient to ensure the safety and approval of ADS. This chapter provides an overview of key challenges in ADS validation and outlines the paradigm shifts required to address them. The increasing complexity of ADS necessitates a modular validation approach, where subsystems and relevant environments are systematically mapped based on specific validation objectives. Human factors and societal interactions play a crucial role, requiring novel validation methods that incorporate behavioral models and immersive technologies. The chapter highlights the growing reliance on virtual testing environments, large-scale simulations, and cloud-based validation, enabling efficient regression testing and scenario generation. Additionally, it discusses the need for new organizational structures, roles, and competencies to manage the complexity of multi-domain system models, along with the integration of iterative development paradigms such as CI/CD into the validation process. Finally, the importance of establishing a credible validation framework, ensuring traceability, and implementing robust data management solutions is emphasized, particularly as ADS progress toward regulatory approval and deployment.