From Alignment to Access Control: A Framework for GenAI Policy Enforcement
TL;DR
A methodology to systematically analyze and dissect existing approaches to define and enforce policy found in the wild is proposed to define and enforce policy found in the wild in GenAI applications.
Abstract
Generative AI (GenAI) applications have flourished enabling users to chat with large language models, and to create agents to act on their behalf for a variety of tasks. The pace of development of capabilities in this field is incredibly fast with security and safety taking a back seat. Unfortunately, the slower pace at which security and safety mechanisms have evolved has led to real incidents. Policy enables the definition of desirable behavior of applications, and for that reason, it is a cornerstone of making systems secure and compliant. Policy however means different things to different practitioners creating confusion and siloed solutions that are not adequate for compliance. This paper takes a tour of the good, the bad and the ugly when it comes to policy enforcement in GenAI applications. We propose a methodology to systematically analyze and dissect existing approaches to define and enforce policy found in the wild. Based on this principled analysis, we provide recommendations and call for action for the community to address. This paper is a companion extension of USENIX Security 2026 Enigma talk titled"From Alignment to Access Control: A Unified View of GenAI Policy Enforcement"by the author Nathalie Baracaldo.
