DIRF: A Framework for Digital Identity Protection and Clone Governance in Agentic AI Systems
This addresses identity protection for individuals and organizations in AI systems, but it is incremental as it builds on existing security and governance concepts.
The paper tackles the problem of digital identity threats from generative AI, such as cloning and impersonation, by introducing the Digital Identity Rights Framework (DIRF), a structured model with nine domains and 63 controls to protect identity attributes and enforce rights.
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.