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

• URL: http://arxiv.org/abs/2604.10473v1
• Date: Sun, 12 Apr 2026 05:59:47 GMT
• Title: AI Identification: An Integrated Framework for Sustainable Governance in Digital Enterprises
• Abstract summary: In digitally transformed enterprises, long-term sustainability depends on transparent, accountable, and lifecycle-governed AI systems.<n>This study proposes a conceptual and architectural framework for AI identification, combining technical and governance mechanisms.<n>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.
• Score: 3.2625979527355784
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification [38.209854965937495]
  Embodied AI systems are increasingly deployed in open-world environments.<n>This article argues that reliability in embodied AI is inherently a lifecycle assurance problem.
  arXiv  Detail & Related papers  (2026-06-02T12:58:40Z)
• Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era [2.113921717639734]
  Generative artificial intelligence has fundamentally changed how content is produced.<n>This paper presents a comprehensive, multi-dimensional taxonomy of generative AI harms and attack vectors.<n>We argue that no single mechanism is sufficient in open, adversarial, and evolving environments.
  arXiv  Detail & Related papers  (2026-05-30T08:51:55Z)
• A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms [49.66022971508878]
  We argue that reasoning should be elevated from a modular component to the system's cognitive core.<n>We derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning.<n>We identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control.
  arXiv  Detail & Related papers  (2026-03-11T07:40:53Z)
• Composable Attestation: A Generalized Framework for Continuous and Incremental Trust in AI-Driven Distributed Systems [4.2822349607372265]
  This paper presents composable attestation as a generalized cryptographic framework for Continuous and Incremental Trust in Distributed Systems.<n>We establish a rigorous mathematical foundation which is defining core properties of such attestation systems.<n>The framework's utility extends to applications such as secure AI model integrity verification, federated learning, and runtime trust assurance.
  arXiv  Detail & Related papers  (2026-03-02T22:45:26Z)
• Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection [41.377612554013474]
  Origin Lens is a privacy-first mobile framework that targets visual disinformation through a layered verification architecture.<n>Unlike server-side detection systems, Origin Lens performs cryptographic image provenance verification and AI detection locally on the device.
  arXiv  Detail & Related papers  (2026-02-03T11:49:00Z)
• Interoperable Architecture for Digital Identity Delegation for AI Agents with Blockchain Integration [0.0]
  We introduce a unified framework that enables bounded, auditable, and least-privilege delegation across heterogeneous identity ecosystems.<n>The framework includes four key elements: Delegation Grants (DGs), first-class authorization artefacts that encode revocable transfers of authority with enforced scope reduction.<n>It also includes a layered reference architecture that separates trust anchoring, credential and proof validation, policy evaluation, and protocol mediation via a Trust Gateway.
  arXiv  Detail & Related papers  (2026-01-21T13:29:23Z)
• Binding Agent ID: Unleashing the Power of AI Agents with accountability and credibility [46.323590135279126]
  BAID (Binding Agent ID) is a comprehensive identity infrastructure establishing verifiable user-code binding.<n>We implement and evaluate a complete prototype system, demonstrating the practical feasibility of blockchain-based identity management and zkVM-based authentication protocol.
  arXiv  Detail & Related papers  (2025-12-19T13:01:54Z)
• Secure Autonomous Agent Payments: Verifying Authenticity and Intent in a Trustless Environment [0.0]
  Artificial intelligence (AI) agents are increasingly capable of initiating financial transactions on behalf of users or other agents.<n>Traditional payment systems assume human authorization, but autonomous, agent-led payments remove that safeguard.<n>This paper presents a blockchain-based framework that cryptographically authenticates and verifies the intent of every AI-initiated transaction.
  arXiv  Detail & Related papers  (2025-11-08T19:53:51Z)
• DIRF: A Framework for Digital Identity Protection and Clone Governance in Agentic AI Systems [2.8257837699423933]
  Digital cloning, sophisticated impersonation, and the unauthorized monetization of identity-related data pose significant threats to the integrity of personal identity.<n>Mitigating these risks requires the development of robust AI-generated content detection systems, enhanced legal frameworks, and ethical guidelines.<n>This paper introduces the Digital Identity Rights Framework (DIRF), a structured security and governance model designed to protect behavioral, biometric, and personality-based digital likeness attributes.
  arXiv  Detail & Related papers  (2025-08-04T02:27:14Z)
• Endorsement-Driven Blockchain SSI Framework for Dynamic IoT Ecosystems [0.39462888523270856]
  Self-Sovereign Identity (SSI) offers significant potential for managing identities in the Internet of Things (IoT)<n>Existing SSI frameworks limit issuance credential and revocation to trusted entities, such as IoT manufacturers.<n>We propose a blockchain-based SSI framework that allows any individual with a verifiable trust linkage to act as a credential issuer.
  arXiv  Detail & Related papers  (2025-07-14T02:03:14Z)
• Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
  Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
  arXiv  Detail & Related papers  (2025-05-26T06:44:31Z)
• Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems [39.23499993745249]
  We introduce semantic integrity constraints (SICs) for specifying and enforcing correctness conditions over LLM outputs in semantic queries.<n>SICs generalize traditional database integrity constraints to semantic settings, supporting common types of constraints, such as grounding, soundness, and exclusion.<n>We present a system design for integrating SICs into query planning and runtime and discuss its realization in AI-augmented DPSs.
  arXiv  Detail & Related papers  (2025-03-01T19:59:25Z)
• Combining Decentralized IDentifiers with Proof of Membership to Enable Trust in IoT Networks [44.99833362998488]
  The paper proposes and discusses an alternative (mutual) authentication process for IoT nodes under the same administration domain. The main idea is to combine the Decentralized IDentifier (DID)-based verification of private key ownership with the verification of a proof that the DID belongs to an evolving trusted set.
  arXiv  Detail & Related papers  (2023-10-12T09:33:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.