This paper presents ROBOGATE, a deployment risk management framework for safe robot policy deployment in industrial settings. The framework combines physics-based simulation with a two-stage adaptive sampling strategy to efficiently discover failure boundaries in high-dimensional operational parameter spaces. Stage 1 uses Latin Hypercube Sampling across an 8-dimensional parameter space, while Stage 2 applies boundary-focused sampling in the 30-70% success rate transition zone. Evaluated using NVIDIA Isaac Sim with Newton physics on Franka Panda and UR5e robots performing pick-and-place tasks across 30,000 experiments, the system employs logistic regression for risk modeling to ensure safe robot manipulation policy deployment.