AI Identification: An Integrated Framework for Sustainable Governance in Digital Enterprises
For enterprises and regulators managing AI systems, this framework offers a structured approach to AI identity and traceability, but it is conceptual and lacks empirical validation, making it an incremental contribution.
This paper proposes a conceptual framework for AI identification that integrates model fingerprinting, cryptographic hashing, blockchain registration, zero-knowledge proofs, and post-deployment change screening to enable lifecycle accountability and sustainable governance in digital enterprises. The framework introduces a dual-layer identifier and uses LZJD for governance-oriented change detection, aiming to support transparent oversight without providing concrete performance numbers.
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.