Data Privacy

Data privacy in AI concerns the protection of personal information throughout the model lifecycle — collection, training, fine-tuning, and inference. Models trained on user data can memorize and reproduce sensitive text; query logs reveal user intent; fine-tuning datasets may contain confidential records. Privacy-preserving techniques include differential privacy during training, data minimization, anonymization of training corpora, and contractual controls on how inference logs are retained and accessed.