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

arXiv: https://arxiv.org/abs/2508.01997 · HTML: https://mlsanity.com/paper/2508.01997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing](https://mlsanity.com/paper/2310.13715/) (2023)
- [Logic layer Prompt Control Injection (LPCI): A Novel Security Vulnerability Class in Agentic Systems](https://mlsanity.com/paper/2507.10457/) (2025)
- [QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI](https://mlsanity.com/paper/2507.15330/) (2025)

## Built on by (most cited first, 2 of 2)

- [Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats](https://mlsanity.com/paper/2508.12259/) (2025)
- [AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI](https://mlsanity.com/paper/2510.25863/) (2025)

## Abstract (arXiv)

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.