Zero Trust Policy Recommendation via Entity Similarity Graphs
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Solution Overview
Problem
Current zero trust architectures face challenges in maintaining an up-to-date set of policies due to their dynamic nature, especially in large and complex organizations, leading to potential gaps in protecting entities within the environment.
Innovation Solution
A method involving community detection algorithms to partition a zero trust environment into sub-graphs, identify common policies within communities, and recommend policies based on community similarities, ensuring timely updates and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual policy management is used in large and complex organizations, then policy definitions can be established, but the policies cannot be kept updated and aligned with the dynamic environment due to the sheer number of inventories and policies beyond manual capacity
Solution Approach 1:
The system enables automated policy management where the Zero Trust environment self-updates policies through machine learning algorithms that continuously analyze entity relationships, communication patterns, and threat intelligence, eliminating the need for manual policy updates and keeping policies aligned with dynamic environmental changes
Solution Approach 2:
The patent replaces manual mechanical policy management with an automated machine learning-based system that uses algorithms to detect entities, analyze relationships, and generate policy recommendations, substituting human operators with intelligent automated systems capable of handling large-scale policy management
2Reliability
If policies are applied according to detected activity with least privileged access, then security is enhanced, but the chances of missing policies to protect each entity increase due to the complexity and scale of the environment
Solution Approach 1:
The system segments the complex Zero Trust environment into manageable entity groups and relationship clusters, allowing policies to be applied systematically to each segment while ensuring comprehensive coverage across the entire environment through hierarchical policy management
Solution Approach 2:
The system implements continuous feedback loops where policy application results, security events, and entity behavior data are constantly monitored and fed back into the machine learning model, enabling automatic policy refinement and ensuring no entity is left unprotected through iterative improvement
Data Source
AI summary
One example method includes transforming a structured data file into a graph structure that represents a zero trust (ZT) environment, where entities in the ZT environment are represented as nodes in the graph structure, and connections between the entities are represented in the graph structure as edges, performing a community detection process on the graph structure to identify a community of entities, performing a community characterization process that comprises identifying the ZT policies that are most commonly applied to the entities in the community, and based on the ZT policies identified, generating a recommendation as to whether one of the ZT policies should be applied to one of the entities in the community and, if so, which ZT policy or ZT policies should be applied.