mlsanityData to 14 Oct 2026

SoK: Access Control Policy Generation from High-level Natural Language Requirements

arXiv 2310.03292 ↗First posted 5 Oct 2023cs.CR cs.AIcited by 5

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Abstract (arXiv)

Administrator-centered access control failures can cause data breaches, putting organizations at risk of financial loss and reputation damage. Existing graphical policy configuration tools and automated policy generation frameworks attempt to help administrators configure and generate access control policies by avoiding such failures. However, graphical policy configuration tools are prone to human errors, making them unusable. On the other hand, automated policy generation frameworks are prone to erroneous predictions, making them unreliable. Therefore, to find ways to improve their usability and reliability, we conducted a Systematic Literature Review analyzing 49 publications, to identify those tools, frameworks, and their limitations. Identifying those limitations will help develop effective access control policy generation solutions while avoiding access control failures.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 14 of its 116 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

14 of 116 references.

References · 14

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2 more
  1. 2022Machine Learning in Access Control: A Taxonomy and Surveycited by 3
  2. 2021Human Factors in Security Research: Lessons Learned from 2008-2018cited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.