Graph Database Identity Management with ML Permission Recommendations

The system enables self-service permission management where the machine learning model automatically generates permission recommendations based on user profiles, role similarities, and historical data. Managers only need to review and approve recommendations rather than manually configuring each permission from scratch, significantly reducing the time and effort required for permission assignment while maintaining system accessibility.

The patent replaces manual mechanical processes with an automated machine learning system. Instead of managers manually reviewing and configuring permissions for each user, the system uses ML models to automatically analyze user data, identify appropriate permissions based on role similarities, and generate recommendations. This substitution dramatically improves productivity while the modular architecture keeps system complexity manageable.

The system transitions from one-dimensional isolated permission lists to a multi-dimensional graph database structure. Nodes represent users, roles, permissions, and applications, while edges capture relationships between them. This dimensional expansion enables the ML model to analyze contextual relationships such as role hierarchies, permission dependencies, and user similarities, dramatically improving recommendation accuracy despite increased data structure complexity.

The graph database functions as a composite data structure combining multiple types of information (user profiles, role definitions, permission sets, application metadata, historical access patterns) into a unified framework. This composite structure allows the system to leverage diverse data sources simultaneously, improving the precision of permission recommendations by considering multiple contextual factors that isolated lists cannot provide.

The system extracts and stores pre-computed features and relationships in the graph database during off-peak times. When a new user is onboarded, the ML model retrieves relevant pre-processed data (role definitions, permission templates, historical patterns) rather than processing all raw user data from scratch. This extraction strategy maintains high recommendation accuracy while significantly reducing the computing resources and energy required during the onboarding process.

The system performs preliminary processing of user data, roles, and permissions in advance, building the graph database structure and pre-computing relationship metrics before actual permission assignment occurs. Historical access patterns and role similarities are pre-analyzed and stored as structured data. When a new user needs permissions, the system leverages this pre-computed information rather than performing comprehensive analysis in real-time, reducing energy consumption while maintaining precision.

The system incorporates feedback loops where manager approvals and rejections of ML-generated recommendations are fed back into the training data. This allows the model to learn from actual decision patterns and improve its understanding of specific user role requirements over time. The feedback mechanism maintains high adaptability to individual user needs while progressively reducing the time managers spend reviewing recommendations as the model becomes more accurate.

The ML model performs self-service by automatically analyzing user profiles, comparing roles, and generating personalized permission recommendations tailored to each user's specific needs. This eliminates the need for manual individual permission selection while maintaining adaptability, as the model learns to customize recommendations based on the unique characteristics of each user role and organizational context.