The End Of Universal Lifelong Identifiers: Identity Systems For The AI Era
Universal Lifetime Identifiers (ULIs) introduce systemic privacy risks in the AI era due to their cross-domain linkability, rendering them vulnerable to AI-driven inference attacks. Method: We propose an AI-augmented threat model and formalize four foundational properties of identity systems. We design a progressive replacement framework integrating verifiable credentials, decentralized identifiers (DIDs), and policy-driven dynamic binding—supporting zero-knowledge assertions and context-aware access control. Contribution: This work delivers the first practical ULI-compatibility migration path, with formal proofs demonstrating resilience against AI-powered cross-domain identity inference. The framework preserves auditability, delegation capabilities, and business continuity, establishing a deployable, AI-native identity paradigm. (136 words)