Neural networks enable high-performance decision-making, but their deployment in safety-critical settings requires formal guarantees that may become invalid after changes in the system dynamics. This research studies how such guarantees can be recovered using information from the initial verification, enabling post-deployment analysis to assess preservation, quantify degradation, and restore compliant behavior without recomputing verification from scratch.