Using constraint programming and graph representation learning for generating interpretable cloud security policies

• Main Authors: KAZDAGLI, Mikhail, TIWARI, Mohit, KUMAR, Akshat
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7717; https://ink.library.smu.edu.sg/context/sis_research/article/8720/viewcontent/Using_constraint_programming_and_graph_representation_learning_for_generating_interpretable_cloud_security_policies.pdf
• Institution: Singapore Management University

Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs signifcant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly confgure and periodically update. Security negligence and human errors often lead to misconfguring IAM policies which may open a backdoor for attackers. To address these challenges, frst, we develop a novel framework that encodes generating optimal IAM policies using constraint programming (CP). We identify reducing dormant permissions of cloud users as an optimality criterion, which intuitively implies minimizing unnecessary datastore access permissions. Second, to make IAM policies interpretable, we use graph representation learning applied to historical access patterns of users to augment our CP model with similarity constraints: similar users should be grouped together and share common IAM policies. Third, we describe multiple attack models and show that our optimized IAM policies signifcantly reduce the impact of security attacks using real data from 8 commercial organizations, and synthetic instances.