IAM Trust Graph Analysis for Cloud Privilege Escalation
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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 is implemented to analyze trust relationships between IAM roles, using a graph neural network (GNN) to identify misconfigurations and privilege escalations by generating a graph representation of trust relationships and combining it with runtime event data to proactively identify overly permissive roles and policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cross-account role endorsement features like AssumeRole are enabled to enhance cloud resource access flexibility, then adaptability is improved, but IAM complexity increases and security risks worsen
Solution Approach 1:
The patent segments the IAM trust relationship graph into manageable components by identifying and analyzing individual role endorsement paths. It breaks down the complex web of cross-account roles into discrete trust chains, allowing systematic examination of each segment for security violations while maintaining the overall flexibility of the IAM system.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between IAM roles and security analysis. This knowledge graph mediates the complexity by visually representing and organizing the trust relationships, enabling security teams to understand and manage the complex interconnections without directly navigating the raw IAM configuration data.
2Productivity
If IAM roles are granted extensive privileges to ensure operational capability, then productivity is improved, but privilege escalation risks increase
Solution Approach 1:
The patent performs preliminary analysis by proactively scanning the IAM trust relationship graph to identify potential privilege escalation paths before they are exploited. It pre-empts security issues by detecting roles that can assume other roles with higher privileges, allowing corrective action to be taken before actual harm occurs.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring IAM role usage and comparing it against the trust relationship graph. When a role attempts to assume another role that would grant excessive privileges, the system provides feedback to block the operation or alert administrators, creating a closed-loop security control system.
3Reliability
If a comprehensive analysis of all trust relationships is performed to ensure security, then reliability is improved, but analysis time increases
Solution Approach 1:
The patent segments the trust relationship graph into individual role endorsement paths, allowing parallel processing of security analysis across multiple independent segments. This segmentation enables comprehensive security checking while reducing sequential analysis time by processing multiple trust chains simultaneously.
Solution Approach 2:
The patent applies partial action by focusing the security analysis on the most critical trust relationships first - those involving high-privilege roles or frequently accessed accounts. This prioritized approach ensures that the most important security risks are addressed while reducing overall analysis time by not equally intensive scanning of all relationships.
Data Source
AI summary
Systems and methods are disclosed for implementing a system to generate a knowledge graph of trust relationships between roles in a cloud environment, and to identify misconfigurations that may lead to privilege escalation. In certain embodiments, a method may comprise implementing a graph-based role permission inspection system for identity and access management (IAM) roles in a cloud environment, including generating a graph representation of trust relationships between roles, where a first role having a first set of privileges can endorse a second role having a second set of privileges. The method may further include determining whether the second set of privileges includes a permission not available in the first set of privileges, and generating an indicator that the first role violates a policy when the second set of privileges includes the permission not available in the first set of privileges.