Automated Access Review System for Data Environments
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Solution Overview
Problem
Modern enterprises face challenges in ensuring accurate and efficient access reviews for data environments, as existing methods are tedious and prone to errors due to the complexity of managing access decisions across multiple systems and platforms.
Innovation Solution
An automated access review system that uses baseline rules to determine rejected access decisions, allows user input for creating new rules, and identifies patterns to improve the accuracy and efficiency of access reviews, thereby reducing the workload for human reviewers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated access review system is implemented, then productivity and accuracy of access reviews are improved, but device complexity increases
Solution Approach 1:
The access review system is segmented into distinct functional modules: an access review system component that obtains and processes access decisions, a rule engine that applies baseline rules to determine rejected decisions, and a machine learning component that generates new access-review rules. This segmentation allows each module to be independently developed, maintained, and optimized, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary access review system that acts as a mediator between raw access decisions from data environments and the final access control policies. This intermediary layer processes access decisions through baseline rules and machine learning models, transforming unstructured access data into structured, actionable insights without requiring direct integration between all system components.
2Manufacturing precision
If baseline rules are used to automatically reject access decisions, then manufacturing precision of access control is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements feedback mechanisms where access review outcomes and user corrections are continuously fed back into the rule engine. When users manually review and correct automated decisions, these corrections are used to refine and update baseline rules, creating a closed-loop system that continuously improves access control accuracy while maintaining ease of operation through automated adjustments.
Solution Approach 2:
The access-review rules are designed to be dynamic rather than static. The system automatically adapts rules based on changing access patterns, user feedback, and new security requirements. This dynamic nature allows the system to maintain high precision in access control while automatically adjusting to operational changes without requiring manual reconfiguration.
3Measurement precision
If machine learning is used to determine rejected access decisions, then measurement precision of access review is improved, but loss of time in rule creation is reduced
Solution Approach 1:
The system performs preliminary actions by pre-establishing baseline rules and machine learning models that can automatically evaluate access decisions before human review. These pre-configured rules and models rapidly assess access requests, providing high-precision preliminary decisions that reduce the time required for manual rule creation and review, while maintaining accuracy through subsequent validation.
Data Source
AI summary
The technology disclosed herein enables automated approval and denial of access decisions responsive to access requests to data environments. In a particular example, a method provides obtaining access decisions responsive to access requests to a plurality of data environments. The method provides determining, based on baseline rules, a subset of the access decisions that should be rejected. The method further provides receiving user input indicating additional ones of the access decisions for inclusion in the subset and determining a new access-review rule based on the user input.