The End Of Universal Lifelong Identifiers: Identity Systems For The AI Era

TL;DR

This paper argues that universal lifelong identifiers are incompatible with AI advancements due to privacy risks, proposing a cryptographic framework for new identity systems that maintain essential functions and facilitate migration.

Contribution

It introduces a cryptographic framework for AI-era identity systems that replaces ULIs, ensuring privacy and compatibility with existing workflows.

Findings

Traditional safeguards are insufficient against modern AI capabilities.

The proposed framework preserves auditability and delegation.

A practical migration path beyond ULIs is outlined.

Abstract

Many identity systems assign a single, static identifier to an individual for life, reused across domains like healthcare, finance, and education. These Universal Lifelong Identifiers (ULIs) underpin critical workflows but now pose systemic privacy risks. We take the position that ULIs are fundamentally incompatible with the AI era and must be phased out. We articulate a threat model grounded in modern AI capabilities and show that traditional safeguards such as redaction, consent, and access controls are no longer sufficient. We define core properties for identity systems in the AI era and present a cryptographic framework that satisfies them while retaining compatibility with existing identifier workflows. Our design preserves institutional workflows, supports essential functions such as auditability and delegation, and offers a practical migration path beyond ULIs.