This paper introduces SAFER, a continual unlearning framework designed to address critical issues like knowledge erosion and forgetting reversal in repeated machine unlearning scenarios. It aims to maintain model utility and prevent the reactivation of forgotten information, making AI systems more reliable and privacy-compliant over their lifecycle.