Entitlement Prediction System for Access Review Automation
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Solution Overview
Problem
Large enterprises face challenges in conducting access reviews due to the time-consuming nature of verifying thousands of entitlements, which can limit managerial duties and increase the risk of overlooking unauthorized access.
Innovation Solution
A computer-implemented method and system that predicts entitlements by analyzing user attributes to determine an entitlement probability value, allowing for the exclusion of predicted authorized entitlements from access reviews, thereby reducing the number of entitlements requiring manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual access reviews are conducted for all entitlements, then security accuracy is improved, but time consumption and manager workload increase significantly
Solution Approach 1:
The patent segments entitlements into different risk categories based on sensitivity levels. High-risk entitlements require manual review while low-risk entitlements are handled automatically, dividing the review process into manageable segments that prioritize security-critical areas.
Solution Approach 2:
The system implements self-service automated review for low-risk entitlements using machine learning models that can independently evaluate and approve certain entitlements without human intervention, freeing managers to focus only on high-risk cases.
2Reliability
If manual access reviews are conducted for all entitlements, then security coverage is improved, but manager productivity deteriorates
Solution Approach 1:
The patent segments entitlements into different risk categories based on sensitivity levels. High-risk entitlements require manual review while low-risk entitlements are handled automatically, dividing the review process into manageable segments that prioritize security-critical areas.
Solution Approach 2:
The system introduces an automated machine learning-based intermediary review process that handles the majority of entitlements, allowing managers to focus exclusively on high-risk cases that require human judgment, thus maintaining security coverage while preserving manager productivity.
3Productivity
If automated entitlement prediction is implemented, then review efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces an automated machine learning-based intermediary review process that handles the majority of entitlements, allowing managers to focus exclusively on high-risk cases that require human judgment, thus maintaining security coverage while preserving manager productivity.
4Loss of time
If automated entitlement prediction is implemented, then time for access reviews is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent applies different review quality levels to different entitlements based on their risk characteristics. High-risk entitlements receive thorough manual review with high precision requirements, while low-risk entitlements use automated review with acceptable precision levels, optimizing the trade-off between time and accuracy.
Data Source
AI summary
Systems, methods, and devices for predicting entitlements to computing resources are described. An entitlement associated with a user of a computer system may be identified. The entitlement may indicate a computing resource of the computer system that is accessible to the user. A set of attributes associated with the user may be selected, and an entitlement probability value may be obtained. The entitlement probability value may be based on the set of attributes and indicate a probability that the user is authorized to have the entitlement. The entitlement probability value may be used to determine whether to include the entitlement in an access review. Depending on the entitlement probability value the entitlement may be included in the access review or excluded from the access review.