Edge-Centric Sovereignty: A Privacy-Preserving Framework for Generative AI in Digital Mental Health

The rapid proliferation of Generative AI in mental healthcare offers unprecedented accessibility but introduces critical privacy risks regarding Data Sovereignty and Personal Identifiable Information (PII) leakage. Traditional cloud-centric architectures inherently expose sensitive user affect data, such as journal entries and art therapy outputs, to third-party inference risks. This paper proposes a Local-First, Hybrid-Storage Framework designed to align with the US Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence [1]. Using a production-grade Multimodal Affective Computing Framework (MACF) as a case study, we demonstrate an architecture where immediate input sanitization and output filtration occur on the device (the "Edge"), ensuring that raw, sensitive data is de-identified before transmission. We detail a "Sanitization Layer" that strips non-standard characters client-side and a "Just-in-Time" (JIT) consent model that grants hardware permissions only at the moment of intent. Furthermore, we explore a cryptographic approach utilizing Presigned URLs and pseudonymized Object Storage to decouple user identity from stored media. By shifting the locus of control from centralized servers to the user’s device through strict type-safety and local caching, this framework provides a blueprint for secure, scalable, and ethically compliant digital mental health interventions. This research argues that on-device inference and strict transport security are not merely technical preferences but ethical imperatives for the future of US digital healthcare infrastructure.