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

TL;DR

This paper presents a novel framework combining constraint programming and graph representation learning to generate interpretable, optimal IAM policies that minimize security risks in cloud environments, validated on real and synthetic data.

Contribution

It introduces a new method for generating optimal and interpretable cloud security policies by integrating constraint programming with graph-based user similarity analysis.

Findings

Optimized IAM policies reduce security attack impact.

Graph learning enhances policy interpretability.

Framework validated on real-world cloud data.

Abstract

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 dark 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.