mlsanityData to 13 Oct 2026

The Privacy Guardian Agent: Towards Trustworthy AI Privacy Agents

arXiv 2604.21455 ↗First posted 23 Apr 2026cs.HCcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

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.

Key facts. No citing papers in the map yet (data through 13 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.

Builds on

7 of 14 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents15 Oct

Other references · 6

  1. 2024CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applicationscited by 33
  2. 2020Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influencecited by 27
  3. 2025Helping Johnny Make Sense of Privacy Policies with LLMscited by 5
  4. 2024Motivating Users to Attend to Privacy: A Theory-Driven Design Studycited by 1
  5. 2025Explainable AI in Usable Privacy and Security: Challenges and Opportunitiescited by 1
  6. 2026A Scoping Review and Guidelines on Privacy Policy's Visualization from an HCI Perspectivecited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management
  2. 2026PrivacyAlign: Contextual Privacy Alignment for LLM Agents
  3. 2026SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
  4. 2024Privacy Leakage Overshadowed by Views of AI: A Study on Human Oversight of Privacy in Language Model Agent
  5. 2024Here's Charlie! Realising the Semantic Web vision of Agents in the age of LLMs
  6. 2024Operationalizing Contextual Integrity in Privacy-Conscious Assistants
  7. 2025Personalizing Agent Privacy Decisions via Logical Entailment
  8. 2025Contextual Integrity in LLMs via Reasoning and Reinforcement Learning