Can LLMs Make (Personalized) Access Control Decisions?
📰 ArXiv cs.AI
Learn how LLMs can make personalized access control decisions to reduce cognitive burden on users and improve security
Action Steps
- Evaluate the feasibility of using LLMs for access control decisions
- Assess the complexity of access control decisions in your system
- Configure an LLM to make personalized access control decisions
- Test the LLM's decision-making accuracy and security
- Compare the LLM's performance to traditional access control methods
Who Needs to Know This
Security engineers and AI researchers can benefit from understanding how LLMs can be used to make access control decisions, improving the security of traditional and agent-based systems
Key Insight
💡 LLMs can potentially reduce the cognitive burden on users and improve security by making precise access control decisions
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🔒 Can LLMs make personalized access control decisions? 🤖
Key Takeaways
Learn how LLMs can make personalized access control decisions to reduce cognitive burden on users and improve security
Full Article
Title: Can LLMs Make (Personalized) Access Control Decisions?
Abstract:
arXiv:2511.20284v2 Announce Type: replace-cross Abstract: Precise access control decisions are crucial for the security of both traditional applications and emerging agent-based systems. Typically, these decisions are made by users during app installation or at runtime. However, due to the increasing complexity and automation of systems, making access control decisions can impose a significant cognitive burden on users, often overwhelming them and leading to suboptimal or even arbitrary choices.
Abstract:
arXiv:2511.20284v2 Announce Type: replace-cross Abstract: Precise access control decisions are crucial for the security of both traditional applications and emerging agent-based systems. Typically, these decisions are made by users during app installation or at runtime. However, due to the increasing complexity and automation of systems, making access control decisions can impose a significant cognitive burden on users, often overwhelming them and leading to suboptimal or even arbitrary choices.
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