Role Management System With Machine-Learning Role Recommendations
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Solution Overview
Problem
Existing software systems lack efficient management of user roles and permissions, leading to security vulnerabilities and compliance issues, particularly in the context of role-based access control systems.
Innovation Solution
Implement a role management system (RMS) with an integrated role recommendation engine (RRE) that uses machine learning to analyze user profile data, authorization usage, and role definitions to provide intelligent role management across heterogeneous software products, automate role assignment, and enforce strict restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users are granted multiple diverse roles to access different services and applications, then user functionality and task completion capability are improved, but system security and access control complexity worsen
Solution Approach 1:
The patent combines multiple role management functions into a unified system that centrally manages user roles across heterogeneous software products. The system merges role definition, assignment, recommendation, and optimization functions into a single integrated platform, reducing the complexity of managing diverse roles across multiple applications.
Solution Approach 2:
The patent introduces an intermediary role management system that sits between users and multiple software applications. This intermediary automatically manages role assignments, provides recommendations, and enforces access control policies, eliminating the need for users to manually manage roles in each application and reducing overall system complexity.
2Ease of operation
If manual role management is performed by administrators, then role assignment flexibility is improved, but administrative workload and time consumption worsen
Solution Approach 1:
The patent implements self-service capabilities where the system automatically performs role management tasks. The recommendation engine autonomously analyzes user behavior and system requirements to generate role assignment recommendations, and the system can automatically optimize role definitions based on usage patterns, reducing administrative workload while maintaining flexibility.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors role usage, access patterns, and system performance. This feedback is used by the recommendation engine to automatically adjust and optimize role assignments, reducing the need for manual administrative intervention while maintaining flexible and appropriate role management.
3Device complexity
If traditional role management systems are used without machine learning, then system simplicity is improved, but role optimization and security detection capability worsen
Solution Approach 1:
The patent replaces traditional mechanical rule-based role management systems with an intelligent system that uses machine learning algorithms. The recommendation engine analyzes historical data, user behavior patterns, and system requirements to automatically generate optimized role assignments and detect security anomalies, significantly improving security detection capability while maintaining reasonable system simplicity through automated processes.
Data Source
AI summary
In an implementation, a request to compute a new machine-learning model for storage in a data lake is received by a role management system (RMS). A role history data is read by the RMS from a role management application and trace history data from a trace application. Metadata read from a plurality of persistencies is stored into the data lake as metadata history. The historic data and metadata history is read by a role recommendation engine (RRE) integrated into the RMS. The RRE, computes the new machine-learning model.