Differential privacy is a technique that adds noise to datasets to mask individual contributions while still enabling accurate model training. This method protects individual privacy by making it difficult to identify specific individuals based on their data. It is particularly effective for complying with privacy regulations like GDPR, HIPAA, and CCPA, as it ensures the protection of sensitive data without significantly diminishing the dataset's utility for AI model training. Other methods, such as data masking or encryption, may either reduce the data's usefulness or be computationally impractical for certain AI models.