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

2026-04-12

AI Summary

This study addresses the lack of identifiability and traceability in current AI systems within digital enterprises, which hinders transparent governance and accountability. To overcome this limitation, the authors propose an integrated AI identity framework that synergistically combines technical and governance mechanisms. The framework introduces a novel dual-layer identification system—comprising a machine-verifiable primary hash and a human-readable secondary identifier—and incorporates model fingerprinting, blockchain-based registration, zero-knowledge proofs (ZKPs), and structural change detection via Lempel-Ziv Jaccard Distance (LZJD). Together, these components enable persistent identity anchoring throughout the AI lifecycle and support selective compliance verification. By providing an auditable and enforceable identity infrastructure, the framework facilitates regulatory alignment, institutional accountability, and sustainable digital innovation.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• AI identification
• sustainable governance
• digital enterprises
• traceability
• regulatory oversight

Innovation

• AI identification
• zero-knowledge proof
• blockchain-based registration
• Lempel–Ziv Jaccard Distance
• dual-layer identifier