Graph Neural Role Inspection for Cloud IAM Trust Vulnerabilities
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Solution Overview
Problem
Identity and Access Management (IAM) in cloud environments is complex, particularly in platforms like AWS, GCP, and Azure, making it difficult to determine who or what has access to cloud resources and what permissions they have, with cross-account role endorsement features like AssumeRole exacerbating this complexity and leading to potential privilege escalation and security risks.
Innovation Solution
A knowledge graph-based approach using a graph neural network (GNN) to analyze trust relationships between IAM roles, enabling static analysis and proactive security measures to identify and correct security vulnerabilities by determining similarity scores and correcting misconfigurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cross-account role endorsement features like AssumeRole are enabled, then access flexibility and versatility are improved, but IAM complexity increases making it harder to determine access relationships
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary representation layer between IAM roles and cloud resources. This knowledge graph models trust relationships, permission inheritance chains, and access paths, transforming the complex IAM configuration into a structured graph representation that can be systematically analyzed to determine effective access relationships.
Solution Approach 2:
The patent transitions from traditional flat IAM policy evaluation to a multi-dimensional graph-based analysis. By representing IAM relationships as a knowledge graph with nodes (roles, users, resources) and edges (trust relationships, permission assignments), the system adds structural and relational dimensions to access control analysis, enabling comprehensive visibility into complex permission inheritance chains.
2Ease of operation
If traditional IAM analysis methods are used, then ease of operation is maintained, but security vulnerability detection capability is insufficient
Solution Approach 1:
The system performs self-service security analysis by automatically traversing the knowledge graph to identify privilege escalation paths, overly permissive roles, and misconfigurations. The graph-based approach enables the system to autonomously detect security vulnerabilities without requiring manual policy review or complex analysis procedures, maintaining ease of operation while significantly improving detection capability.
Solution Approach 2:
The patent implements feedback mechanisms where security analysis results feed back into IAM policy recommendations and alerts. By continuously monitoring the knowledge graph for changes in trust relationships and permission configurations, the system provides real-time feedback on potential security risks, enabling proactive identification and remediation of vulnerabilities.
Data Source
AI summary
Systems and methods are disclosed for implementing graph-based role inspection for roles in a cloud environment based on a graph neural network (GNN). In certain embodiments, a method may comprise performing graph-based role similarity inspection using a GNN, the graph-based role similarity inspection configured to identify roles, in a graph representation of relationships between identity and access management (IAM) roles of a cloud environment, that are most similar to a target role. The method may include determining a graph structure of the graph representation, identifying the target role, performing a similarly calculation between the target role and other roles in the graph structure to determine similarity scores for the other roles; identifying a similar role having a same security vulnerability as the target role based on the similarity calculation, and correcting the security vulnerability in the similar role based on the identification.