As autonomous systems are increasingly integrated into safety-critical applications, ensuring their reliable performance under uncertainty is crucial. Traditional control methods often face difficulties balancing high performance with risk management, especially in dynamic environments with incomplete or evolving data. This workshop will address these challenges by exploring the integration of data-driven approaches with risk-aware control strategies. It will feature both theoretical advancements and real-world applications, focusing on balancing the uncertainty inherent in data-driven methods with the conservatism required for risk-aware control. Invited speakers will present state-of-the-art advancements, progressing from theoretical frameworks to successful implementations in autonomous systems. Case studies from domains such as autonomous vehicles, medical robots, and industrial processes will demonstrate how risk-aware control can enhance system safety and reliability in unpredictable environments.