Cloud Resource Relationship Graphs for Security Impact Analysis
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Solution Overview
Problem
Existing security solutions for cloud resources focus on individual resource types, failing to identify the impact of misconfigurations or compromises on related resources, leading to incomplete security analysis and remediation.
Innovation Solution
A system builds a graph representation of cloud resource relationships using graph analytics to analyze the impact of misconfigurations or compromises, employing algorithms like PageRank centrality, SCC, and betweenness centrality to identify critical and related resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If configuration checks are performed on individual cloud resource types, then misconfigurations of specific resources can be identified, but the impact of misconfigurations on related resources cannot be determined
Solution Approach 1:
The system segments cloud resources into discrete nodes within a graph structure, where each resource type (storage buckets, compute instances, databases) is represented as an individual node. This segmentation allows precise identification of misconfigured resources while maintaining their contextual relationships to other resources through graph edges, thereby resolving the contradiction between detecting specific misconfigurations and understanding their broader impact.
Solution Approach 2:
The patent introduces a graph database as an intermediary structure that connects cloud resources and their relationships. This intermediary enables the system to trace the propagation paths of misconfigurations across different resource types, allowing analysts to understand both the specific misconfiguration and its cascading impact on related resources that would otherwise remain hidden in isolated checks.
2Loss of information
If comprehensive relationship tracking among all cloud resources is implemented, then complete impact analysis can be achieved, but system complexity increases significantly
Solution Approach 1:
The system implements a universal graph database structure that can represent multiple types of cloud resources and their diverse relationships using a single unified model. This multi-functional approach allows the same graph infrastructure to handle different resource types (storage, compute, networking, databases) and various relationship kinds (depends_on, communicates_with, shares_credentials) without requiring separate tracking mechanisms, thereby managing complexity while maintaining comprehensiveness.
Solution Approach 2:
The patent utilizes graph analytics parameters such as PageRank scores, centrality measures, and path analysis metrics to transform complex relationship data into simplified risk assessments. By changing the representation from raw relationship graphs to analytical parameters, the system maintains complete impact analysis capability while presenting results in a manageable and interpretable format that reduces operational complexity.
3Loss of information
If graph analytics algorithms are used to analyze cloud resource relationships, then critical affected resources can be identified, but computational processing time increases
Solution Approach 1:
The system performs preliminary construction of the cloud resource relationship graph during periods when complete resource inventories are available, pre-computing the graph structure and storing it in a graph database. This preliminary action allows the graph topology to be established beforehand, so that when security events occur, the system only needs to query the pre-built graph rather than constructing it in real-time, significantly reducing analysis processing time while maintaining identification accuracy.
Solution Approach 2:
The patent replaces traditional mechanical scanning and enumeration methods with graph analytics algorithms that leverage the structured relationships already captured in the graph database. Instead of performing exhaustive checks across all resources, the system uses graph traversal and analytics to efficiently identify affected resources by following pre-established relationship paths, substituting computational brute force with mathematically optimized graph-based queries that reduce processing time.
Data Source
AI summary
A graph representation of cloud resources and their relationships is generated and maintained to provide insights into impact of incidents affecting cloud resources on others in the cloud environment. Cloud resource data for the cloud resources are obtained and relationships among the cloud resources are determined. Relationships among the cloud resources are determined based on analysis of configuration data associated with the cloud resources from which relationships among cloud resources of different types can be inferred, and external sources may also be utilized to facilitate identification of relationships. A graph representation of the cloud resources and their determined relationships is built where the cloud resource data are stored in vertices with directed edges between the vertices representing the identified relationships. The graph can be analyzed based on various graph algorithms to analyze impact of misconfigured or compromised resources to identify related cloud resources that are or would be affected.