# AI Identity: Standards, Gaps, and Research Directions for AI Agents

arXiv: https://arxiv.org/abs/2604.23280 · HTML: https://mlsanity.com/paper/2604.23280/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 25 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control](https://mlsanity.com/paper/2505.19301/) (2025)
- [Personhood credentials: Artificial intelligence and the value of privacy-preserving tools to distinguish who is real online](https://mlsanity.com/paper/2408.07892/) (2024)
- [A Pragmatic View of AI Personhood](https://mlsanity.com/paper/2510.26396/) (2025)
- [SVIP: Towards Verifiable Inference of Open-source Large Language Models](https://mlsanity.com/paper/2410.22307/) (2024)
- [Towards a Theory of AI Personhood](https://mlsanity.com/paper/2501.13533/) (2025)
- [Authenticated Workflows: A Systems Approach to Protecting Agentic AI](https://mlsanity.com/paper/2602.10465/) (2026)

## Built on by (most cited first, 5 of 5)

- [SoulAuth: An Actor-native Identity Architecture and Rust Reference Implementation for Humans and Long-lived AI Actors](https://mlsanity.com/paper/2609.11258/) (2026)
- [Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness](https://mlsanity.com/paper/2607.20729/) (2026)
- [HANSARD: A Reference Architecture for Forensic Readiness, Runtime Witnessing, and Graded Attribution in Autonomous Multi-Agent AI Systems](https://mlsanity.com/paper/2608.22512/) (2026)
- [Five Primitives for Governing Autonomous AI Agents at Runtime](https://mlsanity.com/paper/2608.26696/) (2026)
- [SoK: Decentralized Agent Economic Infrastructure](https://mlsanity.com/paper/2610.01756/) (2026)

## Abstract (arXiv)

AI agents are now running real transactions, workflows, and sub-agent chains across organizational boundaries without continuous human supervision. This creates a problem no current infrastructure is equipped to solve: how do you identify, verify, and hold accountable an entity with no body, no persistent memory, and no legal standing? We define AI Identity as the continuous relationship between what an AI agent is declared to be and what it is observed to do, bounded by the confidence that those two things correspond at any given moment. Through a structured survey of industry trends, emerging standards, and technical literature, we conduct a gap analysis across the full agent identity lifecycle and make three contributions: (1) a structural comparison of human and AI identity across four dimensions (substrate, persistence, verifiability, and legal standing) showing that the asymmetry is fundamental and that extending human frameworks to agents without structural modification produces systematic failures; (2) an evaluation of current technical and regulatory documents against the identity requirements of autonomous agents, finding that none adequately address the challenge of governing nondeterministic, boundary-crossing entities; and (3) identification of five critical gaps (semantic intent verification, recursive delegation accountability, agent identity integrity, governance opacity and enforcement, and operational sustainability) that no current technology or regulatory instrument resolves. These gaps are structural; more engineering effort alone will not close them. Foundational research on AI identity is the central conclusion of this report.