Machine unlearning provides a principled approach to selectively remove sensitive or harmful information from Large Language Models while preserving their overall utility.

• Eliminates undesirable data influences without requiring costly full retraining
• Addresses key privacy and security concerns in AI deployment
• Offers practical methods to comply with regulations like 'right to be forgotten'
• Maintains model performance while removing specific knowledge

This research is crucial for security professionals as it provides frameworks to mitigate privacy breaches, reduce exposure to illegal content, and develop more trustworthy AI systems.

A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models