Sim-to-real transfer is the problem of getting a system that works in simulation to behave reliably in the real world. In AI, robotics, and automation, models are often trained or tested in controlled virtual environments before they are deployed on physical devices. The difficulty is that real conditions rarely match the simulator exactly: sensors drift, timing changes, surfaces behave differently, and unexpected inputs appear.
This matters in cyber security because weak sim-to-real transfer can make autonomous systems easier to mislead, disrupt, or safely fail. An attacker may exploit mismatches between training data and live conditions, while defenders use simulation to test resilience, identify brittle assumptions, and harden control logic. In practice, the goal is not just high simulated accuracy, but dependable behavior when the system leaves the lab and enters an unpredictable environment.