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

TL;DR

This paper introduces DIRF, a comprehensive framework designed to protect digital identities and govern clone-related risks in AI systems through multi-domain controls and enforcement mechanisms.

Contribution

The paper presents a novel structured security and governance framework, DIRF, integrating legal, technical, and hybrid controls for digital identity protection in AI.

Findings

DIRF covers nine domains with 63 controls for identity protection.

The framework supports enforcement of identity rights across AI systems.

DIRF informs platform builders, legal entities, and regulators.

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.