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

Abstract: As 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.

• The paper introduces a lifecycle-bound, cryptographically anchored AI identification method that fills the gap in persistent model traceability.
• The framework integrates model fingerprinting, blockchain registration, zero-knowledge proofs, and drift detection for robust governance.
• The solution enhances efficiency by reducing redundant audits while ensuring compliance in dynamic digital enterprise environments.

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

Introduction and Motivation

The paper "AI Identification: An Integrated Framework for Sustainable Governance in Digital Enterprises" (2604.10473) advances the technical and governance literature by proposing a comprehensive framework for lifecycle-bound, cryptographically anchored AI identification. As AI systems, particularly MLLMs, are increasingly deployed in critical domains and embedded in the core of digital infrastructure, their identification and traceability are pivotal for regulatory oversight, institutional accountability, and sustainable innovation. Existing approaches—such as Model Cards, documentation, watermarks, and behavioral fingerprinting—are inadequate for persistent, system-level identification across dynamic lifecycles and heterogeneous deployment environments. This work addresses a critical gap: ensuring that AI systems can be cryptographically and organizationally anchored to enduring, verifiable identities that enable both technical and regulatory operations throughout their lifecycle.

Technical Architecture

The proposed framework integrates five core technical and governance mechanisms:

1. Model Fingerprinting: The unique configuration of a model’s learned weights is designated as its cryptographic fingerprint. Every stable state of an AI model—defined by its parameter vector $W$—receives a unique identifier, with updates, fine-tuning, or architectural alterations triggering recomputation and/or re-registration.
2. Cryptographic Hashing: The model's weight tensor is deterministically serialized and hashed (SHA-256), producing a primary, machine-verifiable commitment $H_W = \text{SHA-256}(W)$. To achieve issuer-aware namespacing, a secondary hash combines $H_W$ with the developer or registry’s code, yielding $AI\_ID = \text{SHA-256}(\text{company}||H_W)$.
3. Blockchain-Based Registration: Hashes and associated minimal metadata are immutably recorded on a tamper-resistant distributed ledger. The registry provides a cross-organizational, auditable backbone for provenance, while authority for approval and registration remains with designated governance entities.
4. Zero-Knowledge Proof (ZKP) Verification: ZKP protocols enable registered AI systems to cryptographically prove possession of their corresponding $W$ (and thus, registered identity) at governance checkpoints, without disclosing proprietary model internals. Due to complexity limitations (especially for contemporary model sizes), proof circuits are scoped to commitment possession rather than in-circuit hashing of $W$ itself.
5. Post-Deployment Drift Detection: To accommodate operationally realistic lifecycle management, the framework incorporates Lempel-Ziv Jaccard Distance (LZJD) as a scalable metric for weight-level similarity. LZJD is used as a governance signal to detect structural divergence, triggering re-registration or review if a model drifts beyond defined thresholds.

The framework also introduces a dual-layer identifier design, with a machine-verifiable primary hash underpinning governance assurance and a human-readable secondary identifier to support institutional visibility, transparency, and compliance processes.

Governance Integration and Lifecycle Management

AI identification is embedded directly into enterprise digital transformation frameworks, such as AIDAF and FSAO, mapping to communication, integration, adaptation, and digitalization phases. Persistent, lifecycle-bound identifiers facilitate:

• Integrated risk management and data governance,
• Efficient model certification and compliance audits,
• Minimization of redundant retraining and recertification,
• Continuous post-deployment validation.

By anchoring identity at the technical level and linking it to governance checkpoints, the framework decouples authorization and audit trails from mutable organizational records, addressing longevity, continuity, and cross-jurisdictional integrity.

Security, Threat Model, and Enforcement

The threat model presumes adversaries may copy, modify, or deploy unregistered/tampered models. Within regulated or enterprise-governed environments, AI systems are required to validate their registered identity—deterring illicit or non-compliant deployment. Any alteration to weights invalidates the original hash-based identifier, blocking unauthorized instances from passing verification. The system is not intended to prevent offline attacks in uncontrolled domains, but rather to provide enforceable identity assurance in enterprise and regulatory settings.

Limitations

Several limitations are acknowledged:

• The framework is conceptual, not empirically validated for benchmarked performance, scalability, or attack resistance under adversarial threat models.
• Current ZKP performance limits preclude in-circuit verification for large models; proofs address only possession of the original commitment.
• Similarity-based drift detection (e.g., LZJD) does not establish behavioral or semantic equivalence—its use is strictly as a governance trigger for re-registration rather than as a proxy for safety or performance.
• Incremental adoption is assumed to proceed in regulated, high-risk environments, with broader diffusion conditioned on institutional incentives and regulatory mandates.

Implications and Future Directions

Practical Implications

• Efficiency Gains: The framework reduces duplicated audits, certification, and retraining, lowering compute and organizational costs associated with compliance and risk management.
• Regulatory Compliance: Persistent, machine-anchored identity enables both cross-jurisdictional regulatory oversight and organizational accountability. The ledger-based registry system provides an immutable audit trail, resistant to post hoc alteration.
• Confidentiality Preservation: Use of ZKP-based attestation preserves proprietary IP while maintaining transparency and enforceability, crucial in B2B and multi-party ecosystem settings.

Theoretical Implications

• Separation of Identity and Authorization: The architecture delineates identity anchoring from governance approval, allowing for robust audit frameworks even under centralized registrations.
• Scalability and Interoperability: By adopting standardized serialization, namespaces, and modular verification, the solution is agnostic to underlying model architectures and deployment platforms.

Forward Looking Considerations

• Advances in ZKML and efficient commitment schemes may in the future enable full in-circuit verification even for large-scale models, potentially increasing assurance levels.
• Empirical evaluation of similarity-based thresholds, real-world performance under deployment churn (quantization, pruning, continual fine-tuning), and economic/institutional adoption incentives are necessary for operationalization.
• The framework can serve as foundational infrastructure for legally mandated AI registries, sectoral oversight systems, and cross-border compliance protocols, provided adequate regulatory adoption.

Conclusion

This work presents an integrated, lifecycle-oriented AI identification framework that fuses cryptographic, decentralized, and governance-aligned elements to satisfy institutional requirements for traceability, accountability, and sustainable innovation. By distinguishing identity from both behavioral documentation and compliance controls, and by supporting selective, scalable verification, the proposal directly addresses the foundational gap in existing governance architectures. The framework establishes foundational infrastructure for the enforceable and transparent governance of AI systems as digital organizations and regulatory regimes mature, and its modular, policy-agnostic architecture is well-positioned for adaptation to evolving technical and legal requirements.