Biblio

The Principle of Least Privilege is a security objective of granting users only those accesses they need to perform their duties. Creating least privilege policies in the cloud environment with many diverse services, each with unique privilege sets, is significantly more challenging than policy creation previously studied in other environments. Such security policies are always imperfect and must balance between the security risk of granting over-privilege and the effort to correct for under-privilege. In this paper, we formally define the problem of balancing between over-privilege and under-privilege as the Privilege Error Minimization Problem (PEMP) and present a method for quantitatively scoring security policies. We design and compare three algorithms for automatically generating policies: a naive algorithm, an unsupervised learning algorithm, and a supervised learning algorithm. We present the results of evaluating these three policy generation algorithms on a real-world dataset consisting of 5.2 million Amazon Web Service (AWS) audit log entries. The application of these methods can help create policies that balance between an organization's acceptable level of risk and effort to correct under-privilege.