Abstract
Access control lists (ACLs) are a commonly used mechanism to limit the distribution of data in an organization and to protect data from improper access. As ACL size grows one task to be solved is to keep the lists up-to-date and validate that each person in the ACL has appropriate levels of access to data. Currently, managing ACLs and detecting the presence of anomalous individuals in the lists is a manual task. This disclosure describes the application of a trained machine learning model that utilizes as input various factors such as employee roles and organization structure to detect and flag ACLs with dispersion patterns that are likely indicative of potential improper access.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Sutro, Alejo Grigera, "Machine-Learning Based Evaluation of Access Control Lists to Identify Anomalies", Technical Disclosure Commons, (January 16, 2020)
https://www.tdcommons.org/dpubs_series/2870