AI Identification: An Integrated Framework for Sustainable Governance in Digital Enterprises

TL;DR

This paper presents a comprehensive framework for AI identification and traceability in digital enterprises, integrating technical and governance mechanisms to support sustainable and accountable AI lifecycle management.

Contribution

It introduces a novel integrated architecture combining cryptographic, blockchain, and zero-knowledge proof techniques for verifiable AI identity and lifecycle oversight.

Findings

Framework supports lifecycle accountability and transparency.

Dual-layer identifiers enable verifiable and human-readable AI identities.

Post-deployment change screening enhances governance and security.

Abstract

As artificial intelligence (AI) systems grow more powerful, autonomous, and embedded in critical infrastructure, their identification and traceability become foundational to regulatory oversight and sustainable digital governance. In digitally transformed enterprises, long-term sustainability depends on transparent, accountable, and lifecycle-governed AI systems, all of which require verifiable identity. This study proposes a conceptual and architectural framework for AI identification, combining technical and governance mechanisms to support lifecycle accountability. The framework integrates five components: model fingerprinting, cryptographic hashing, blockchain-based registration, zero-knowledge proof (ZKP)-based proof of possession, and post-deployment structural change screening. We introduce a dual-layer identifier, consisting of a machine-verifiable primary hash and a human-readable secondary identifier, anchored in a tamper-resistant registry. Identity validation is supported by selective ZKP-based verification at governance-defined checkpoints, while post-deployment changes are monitored using Lempel--Ziv Jaccard Distance (LZJD) as a governance-oriented screening signal rather than a semantic performance metric. The framework establishes an enforceable and transparent identity infrastructure that enables continuity, auditability, and policy-aligned oversight across AI system lifecycles. By embedding AI identification within enterprise architecture and governance processes, the proposed approach supports sustainable innovation, strengthens institutional accountability, and provides a foundation for selective, policy-defined verification during digital transformation.