AI Identity Manipulation Detection Using Linked Risk Graphs
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Solution Overview
Problem
Existing systems struggle to accurately determine whether identities involved in interactions are legitimate, allowing malicious interactions to proceed due to the difficulty in identifying identity manipulation.
Innovation Solution
Utilizing artificial intelligence techniques, including machine-learning models and graph mining, to generate risk signals from entity and interaction data, which are applied to a linked graph structure to determine a risk indicator for controlling access to interactive computing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial intelligence models and graph mining techniques are implemented to identify manipulated identities, then measurement precision of identity legitimacy is improved, but device complexity increases
Solution Approach 1:
The system segments the identity verification process into multiple specialized AI models (machine learning models, graph mining models) that each handle specific aspects of identity analysis. This segmentation allows complex verification tasks to be divided into manageable components, improving measurement precision while organizing device complexity into modular, maintainable units
Solution Approach 2:
The patent introduces an intermediary risk indicator as a mediator between raw entity data and final access control decisions. This intermediary layer processes and synthesizes information from multiple AI models, providing a unified measure of identity legitimacy that simplifies the overall system complexity while maintaining high measurement precision
2Reliability
If comprehensive entity data and interaction data are analyzed using AI models, then reliability of access control is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing entity data and interaction data into structured formats before AI model analysis. Graph structures are pre-computed and stored, allowing the AI models to quickly query and analyze pre-processed information, thereby improving access control reliability while reducing real-time processing time
Solution Approach 2:
The patent implements partial action by analyzing only the most relevant features and data points from entity data and interaction data using AI models. Rather than processing all possible data comprehensively, the system focuses on critical indicators of identity manipulation, maintaining high reliability while significantly reducing processing time
3Measurement precision
If AI models process large volumes of entity data and interaction data, then measurement precision of risk assessment is improved, but use of energy increases
Solution Approach 1:
The system extracts and focuses only on the most critical features and patterns from large volumes of entity data and interaction data that are relevant to risk assessment. By taking out and analyzing only the essential data elements using AI models, the system improves measurement precision of risk assessment while reducing computational energy consumption associated with processing unnecessary data
Data Source
AI summary
A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.