The escalating security risks in hybrid cloud environments present a critical bottleneck to broader adoption. This study proposes an innovative risk management strategy to maximize provider revenue. By modeling the cloud service architecture as a queueing system, where user requests are “customers” and resources are “servers”, we quantitatively analyze user decision-making under dynamic risk perceptions. Integrating queueing game theory with a Stackelberg framework, we systematically model the customer trade-off between service efficiency and security assurance. Our analysis reveals Nash equilibrium patterns under diverse data breach liability allocations, deriving an optimized provider risk-control model. This strategy guides market behavior through policy optimization to achieve risk-revenue equilibrium. The work advances theoretical foundations for hybrid cloud risk mitigation and provides actionable strategies for cloud providers.