# The Privacy Guardian Agent: Towards Trustworthy AI Privacy Agents

arXiv: https://arxiv.org/abs/2604.21455 · HTML: https://mlsanity.com/paper/2604.21455/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 7 of 7)

- [CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications](https://mlsanity.com/paper/2410.13387/) (2024)
- [Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence](https://mlsanity.com/paper/2001.02479/) (2020)
- [Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents](https://mlsanity.com/paper/2410.11906/) (2024)
- [Helping Johnny Make Sense of Privacy Policies with LLMs](https://mlsanity.com/paper/2501.16033/) (2025)
- [Motivating Users to Attend to Privacy: A Theory-Driven Design Study](https://mlsanity.com/paper/2405.03915/) (2024)
- [Explainable AI in Usable Privacy and Security: Challenges and Opportunities](https://mlsanity.com/paper/2504.12931/) (2025)
- [A Scoping Review and Guidelines on Privacy Policy's Visualization from an HCI Perspective](https://mlsanity.com/paper/2601.17368/) (2026)

## Abstract (arXiv)

The current "notice and consent" paradigm is broken: consent dialogues are often manipulative, and users cannot realistically read or understand every privacy policy. While recent LLM-based tools empower users seeking active control, many with limited time or motivation prefer full automation. However, fully autonomous solutions risk hallucinations and opaque decisions, undermining trust. I propose a middle ground - a Privacy Guardian Agent that automates routine consent choices using user profiles and contextual awareness while recognizing uncertainty. It escalates unclear or high-risk cases to the user, maintaining a human-in-the-loop only when necessary. To ensure agency and transparency, the agent's reasoning on its autonomous decisions is reviewable, allowing for user recourse. For problematic cases, even with minimal consent, it alerts the user and suggests switching to an alternative site. This approach aims to reduce consent fatigue while preserving trust and meaningful user autonomy.