ML Access Rights Pattern Matching for Proactive Permission Updates
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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, particularly in time-critical situations.
Innovation Solution
An automated proactive system that monitors user access patterns and adjusts access rights dynamically using machine learning models and trigger events to ensure timely and accurate access management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a reactive access right management system is used where users manually request access through forms or emails, then the system is simple to implement and operate, but the response time is slow and access rights may be incorrect or incomplete
Solution Approach 1:
The system proactively monitors user activities and pre-determines access rights before users need to request them. By continuously observing access patterns and automatically granting appropriate permissions, the system eliminates the need for manual requests and significantly reduces response time while maintaining manageable complexity through automation
Solution Approach 2:
The system automatically monitors user behavior, analyzes access patterns, and grants or revokes access rights without human intervention. This self-service approach allows the system to manage its own access control decisions based on observed patterns, reducing both response time and the need for complex manual approval workflows
2Adaptability or versatility
If static access rights are assigned to users based on their roles, 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 role-based access rights to dynamic access control by continuously monitoring user activities and adjusting permissions in real-time. Access rights automatically adapt to changing user needs based on observed behavior patterns, providing versatility while maintaining simplicity through automated decision-making algorithms
Solution Approach 2:
The system implements continuous feedback loops by monitoring user access patterns and using this information to automatically adjust access rights. The monitoring component feeds data back to the access control mechanism, enabling the system to adapt to changing requirements without increasing operational complexity
3Reliability
If manual approval processes are used for access requests, then the system maintains security through human review, but it creates inefficiencies and potential security risks due to delays
Solution Approach 1:
The system automatically monitors user activities and makes access control decisions without human intervention, maintaining security through automated pattern recognition and anomaly detection. This self-service approach eliminates delays inherent in manual approval processes while preserving security through continuous automated monitoring and adaptive access control
Solution Approach 2:
The system proactively grants access rights based on monitored patterns before security incidents can occur, rather than waiting for manual approval requests. By continuously analyzing user behavior and pre-emptively adjusting access rights, the system maintains high security reliability while dramatically improving access management efficiency
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