Automated Access Recommendation System for Identity Management
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Solution Overview
Problem
Identity management systems face inefficiencies in determining which access items should be added or removed from identities, leading to productivity losses and inaccuracies in access models due to the time-consuming process of manual approval and decision-making.
Innovation Solution
The implementation of graph database queries and advanced machine learning models to automatically generate access recommendations for identities, utilizing confidence scores and concurrency measures to recommend access items based on related identities and attributes, and combining these approaches through ensemble methods for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual approval and decision-making processes are used to determine access items for identities, then accuracy and control are maintained, but productivity is reduced due to time-consuming processes
Solution Approach 1:
The system enables self-service by automatically generating access recommendations using machine learning models that analyze identity attributes and access patterns. The system autonomously identifies suitable access items without requiring manual intervention for each access request, thereby improving productivity while maintaining accuracy through continuous learning and validation mechanisms.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing access recommendations based on identity attributes and historical access patterns. When an access request is made, the system quickly retrieves pre-computed recommendations rather than performing manual analysis, significantly reducing the time required for access decision-making while maintaining high accuracy.
2Productivity
If automated systems are implemented to recommend access items, then productivity is boosted and time is reduced, but system complexity increases
Solution Approach 1:
The automated system is segmented into distinct functional modules: identity attribute analysis module, access pattern recognition module, recommendation generation module, and validation module. Each module performs a specific function and can be independently developed, tested, and maintained, reducing overall system complexity while enabling high productivity through parallel processing and specialized optimization of each component.
3Measurement precision
If graph database queries and machine learning models are used to generate access recommendations, then accuracy of access models is improved, but computational resources and system complexity increase
Solution Approach 1:
An intermediary layer is introduced between the graph database and machine learning models, consisting of feature extraction and preprocessing components. This intermediary transforms complex graph data into simplified feature vectors that can be efficiently processed by ML models, thereby maintaining high recommendation accuracy while reducing the computational complexity and resource requirements of the overall system.
Data Source
AI summary
Systems and methods for embodiments of graph based and machine learning artificial intelligence systems for generating access item recommendations in an identity management system are disclosed. Embodiments of the identity management systems disclosed herein may utilize a graph based approach, a machine learning based approach, and hybrid combinations thereof for generating access item recommendations.