ML Access Rights Matching for Faster Project Authorization
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Solution Overview
Problem
Existing access right management systems are reactive, leading to inefficiencies, incorrect or incomplete access rights, and potential security risks due to static user needs and changing access patterns, especially in time-critical situations.
Innovation Solution
An automated proactive system that monitors user access patterns and adjusts access rights using machine learning models and trigger events to dynamically update access rights based on user profiles and group affiliations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a reactive access right management system is used where users manually request access, then the system is simple to implement, but the response time is slow and access rights may be incorrect or incomplete
Solution Approach 1:
The system proactively grants access rights before users need to request them by monitoring access patterns and predicting future needs. The automated machine learning model analyzes user behavior, resource usage trends, and contextual factors to anticipate required access rights, eliminating the need for manual requests and reducing response time.
Solution Approach 2:
The system automatically manages access rights without requiring user initiation. The automated machine learning model continuously monitors access patterns and autonomously adjusts access rights based on detected changes, allowing the system to serve itself rather than relying on users to request access.
2Adaptability or versatility
If static access rights are assigned to users, then the system is easy to manage, but it cannot adapt to changing user needs and access patterns
Solution Approach 1:
The system transitions from static access rights to dynamic access rights that automatically adjust based on real-time monitoring of user behavior and contextual factors. The automated machine learning model continuously analyzes access patterns and modifies access rights accordingly, enabling the system to adapt to changing needs while maintaining manageable complexity through automation.
Solution Approach 2:
The system implements continuous feedback loops where access patterns are monitored, analyzed by the machine learning model, and used to automatically adjust access rights. This closed-loop system constantly adapts to changing user needs by feeding back observed behavior patterns into the decision-making process.
3Reliability
If manual access request processes are used, then the system requires minimal automation, but it leads to security risks and incomplete access rights
Solution Approach 1:
The automated machine learning model autonomously manages access rights without human intervention. It monitors access patterns, predicts future needs, and automatically grants or revokes access rights, eliminating manual processes and reducing security risks associated with human error or oversight.
Solution Approach 2:
The system replaces manual mechanical processes (users filling out forms, administrators reviewing requests) with an automated machine learning-based system. This substitution eliminates human error, ensures consistent application of access policies, and provides more reliable and accurate access right management.
Data Source
AI summary
A method may include receiving an event notification that a user identifier has been added to a project identifier in a database, the user identifier associated with resource access rights for a plurality of resources; querying a datastore for a first resource access patterns associated with the user identifier and the project identifier for a first period of time; inputting the first resource access patterns into a machine learning model; receiving an output from the machine learning model identifying a cluster identifier associated with user identifiers with similar access patterns as the first resource access patterns; and based on the output, modifying the resource access rights for the user identifier with respect to the project identifier