Bipartite Graph Risk Assessment for Application Access Control
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Solution Overview
Problem
Existing methods for granting temporary enhanced application access introduce security risks due to their simplicity and inefficiency, as they rely on static rules or manual vetting, potentially allowing malicious users to access sensitive information.
Innovation Solution
A similarity analysis using bipartite graphs is employed to assess user risk by comparing transaction records, calculating similarity scores, and classifying users as low or high risk, which informs approval or denial of enhanced access requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static rules or manual vetting are used to grant enhanced access, then the process is simple and easy to operate, but security risks increase and reliability decreases
Solution Approach 1:
The system performs automatic risk assessment by analyzing user behavior patterns and transaction records, allowing the access management system to make its own security decisions without requiring manual security review for each access request. The system self-evaluates users based on their historical behavior and automatically determines approval or denial.
Solution Approach 2:
The system continuously monitors and analyzes user behavior patterns, transaction records, and access histories to dynamically adjust risk assessments. This feedback loop allows the system to learn from past behavior and improve its security decisions over time, moving away from static rules to adaptive, data-driven risk management.
2Reliability
If manual vetting is used to assess user risk, then security can be maintained, but the process becomes time-consuming and productivity decreases
Solution Approach 1:
The system automatically performs risk assessment by analyzing user behavior patterns and transaction records, eliminating the need for manual security review. The access management system independently evaluates each user request based on their historical behavior and makes automated decisions, significantly reducing processing time while maintaining security.
Solution Approach 2:
The patent replaces manual human review processes with automated computer-based analysis. The system uses algorithms to process user behavior data, transaction records, and access patterns, substituting the mechanical manual vetting process with an automated information processing system that operates continuously without human intervention.
3Device complexity
If simple static rules are used for access control, then device complexity is reduced, but the system becomes less adaptable to different user behaviors and scenarios
Solution Approach 1:
The system transitions from static, fixed access control rules to dynamic, adaptive risk assessment. The system continuously learns from user behavior patterns and adjusts its security decisions in real-time based on individual user characteristics, context, and historical behavior, making the access control mechanism flexible and adaptable to diverse scenarios.
Solution Approach 2:
The system changes the parameters of access control from fixed static rules to dynamic risk-based thresholds. Instead of asking whether a user meets predetermined criteria, the system evaluates continuous parameters such as behavior patterns, transaction frequencies, and access contexts, adjusting security decisions based on real-time risk assessment rather than fixed parameters.
Data Source
AI summary
Systems, methods, and computer media are described for user risk assessment using similarity analysis. Records of transactions performed by a user while in previous enhanced application access sessions can be evaluated against records of transactions performed by other users in previous sessions. The more similar a user is to other users, the more likely it is the user was acting in a typical manner, and the less likely the user poses a security risk. A similarity analysis can be performed using a bipartite graph linking a group of users and a group of application transactions. By examining an edge between a user and a performed transaction, other edges (and corresponding other users) can be identified that also performed the transaction. A similarity score can be calculated based on the bipartite graph and can be used to determine a risk classification and allow or deny an enhanced application access session request.