DIRF: A Framework for Digital Identity Protection and Clone Governance in Agentic AI Systems

• URL: http://arxiv.org/abs/2508.01997v1
• Date: Mon, 04 Aug 2025 02:27:14 GMT
• Title: DIRF: A Framework for Digital Identity Protection and Clone Governance in Agentic AI Systems
• Authors: Hammad Atta, Muhammad Zeeshan Baig, Yasir Mehmood, Nadeem Shahzad, Ken Huang, Muhammad Aziz Ul Haq, Muhammad Awais, Kamal Ahmed, Anthony Green,
• Abstract summary: 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.
• Score: 2.4147135153416195
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid advancement and widespread adoption of generative artificial intelligence (AI) pose significant threats to the integrity of personal identity, including digital cloning, sophisticated impersonation, and the unauthorized monetization of identity-related data. Mitigating these risks necessitates the development of robust AI-generated content detection systems, enhanced legal frameworks, and ethical guidelines. 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 to address this critical need. Structured across nine domains and 63 controls, DIRF integrates legal, technical, and hybrid enforcement mechanisms to secure digital identity consent, traceability, and monetization. We present the architectural foundations, enforcement strategies, and key use cases supporting the need for a unified framework. This work aims to inform platform builders, legal entities, and regulators about the essential controls needed to enforce identity rights in AI-driven systems.

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