From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management

2026-02-04

AI Summary

This study addresses the fragmented and manual nature of privacy management that users face across multiple platforms and contexts, which hinders effective control over their digital footprints. Adopting a “user-centric” perspective, the research identifies cross-contextual privacy challenges through semi-structured interviews and proposes nine AI agent concepts to support privacy regulation. Through speed-dating prototyping sessions and large-scale evaluations, the design space and user preferences for such agents are explored. Findings reveal strong user trust in AI’s accuracy for post-sharing intervention, with a marked preference for semi-automated or fully automated privacy remediation tools. The work introduces a novel paradigm for unified privacy management centered on AI agents, emphasizing post-hoc automation and human-AI collaboration to overcome the limitations of siloed, platform-specific controls.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Managing one's digital footprint is overwhelming, as it spans multiple platforms and involves countless context-dependent decisions. Recent advances in agentic AI offer ways forward by enabling holistic, contextual privacy-enhancing solutions. Building on this potential, we adopted a''human-as-the-unit''perspective and investigated users'cross-context privacy challenges through 12 semi-structured interviews. Results reveal that people rely on ad hoc manual strategies while lacking comprehensive privacy controls, highlighting nine privacy-management challenges across applications, temporal contexts, and relationships. To explore solutions, we generated nine AI agent concepts and evaluated them via a speed-dating survey with 116 US participants. The three highest-ranked concepts were all post-sharing management tools with half or full agent autonomy, with users expressing greater trust in AI accuracy than in their own efforts. Our findings highlight a promising design space where users see AI agents bridging the fragments in privacy management, particularly through automated, comprehensive post-sharing remediation of users'digital footprints.

Problem

• privacy management
• digital footprint
• AI agents
• human-as-the-unit
• cross-context privacy

Innovation

• AI agents
• privacy management
• human-as-the-unit
• post-sharing remediation
• digital footprint