Robust AI Personalization Will Require a Human Context Protocol

This position paper argues that robust AI personalization requires a Human Context Protocol (HCP): a user-owned, secure, and interoperable preference layer that grants individuals granular, revocable control over how their data steers AI systems. By replacing siloed, behavior-inferred signals with direct preference articulation, HCP unifies fragmented data, lowers switching costs, and enables seamless portability across AI services, fostering a more competitive ecosystem. We outline core design principles – natural-language preference storage, scoped sharing, and strong authentication with revocation – that extend earlier personal-data architectures to the scale and stakes of modern generative AI. Centering control in users, HCP is not merely a technical convenience but a necessary foundation for AI systems that are genuinely personal, interoperable, and aligned with diverse human values.