Processing of user content—during regular feature usage and model training—could expose sensitive user data to the parties who own the AI models. Furthermore, even if an AI feature could somehow process user content in an encrypted manner (e.g., if FHE were to become practical for this task), training AI models with E2EE data raises an additional security concern: it is well-known that AI models often inadvertently “memorize” training data, which can lead to reproduction of this data during model responses, or even deliberate extraction by actors who can query the model in the form of “adversarial attacks.” Privacy Settings and Granularity : Messaging services should provide granular privacy settings that let users control what specific data is used for AI features and how much is stored…