Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies

Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies

Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 1850-1858. https://doi.org/10.24963/ijcai.2022/257

Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs significant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly configure and periodically update. Security negligence and human errors often lead to misconfiguring IAM policies which may open a backdoor for attackers. To address these challenges, first, 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 significantly reduce the impact of security attacks using real data from 8 commercial organizations, and synthetic instances.
Keywords:
Constraint Satisfaction and Optimization: Applications
Constraint Satisfaction and Optimization: Constraint Programming
Constraint Satisfaction and Optimization: Constraints and Machine Learning
Constraint Satisfaction and Optimization: Modeling
Multidisciplinary Topics and Applications: Security and Privacy