Automated Skill Vector Privilege Assignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inexperienced users accessing computing environments can lead to resource wastage and outages due to unchecked configuration changes, making manual skill level review time-consuming and error-prone.
Innovation Solution
A system that generates user vectors based on user characteristics to automatically estimate skill levels, using machine learning algorithms to make privilege decisions and SMT solvers for secure access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of user experience level is performed, then access control security is improved, but time consumption and error rate increase
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning system. The ML model automatically evaluates user characteristics, generates skill vectors, and determines appropriate access privileges without human intervention, thereby eliminating time consumption and human error while maintaining security.
Solution Approach 2:
The system enables self-service by allowing the automated ML-based evaluation system to independently assess user skills and assign privileges without requiring manual review. The system serves itself by automatically making access control decisions based on objective data analysis.
2Adaptability or versatility
If inexperienced users are granted access to computing environment, then operational flexibility is improved, but resource wastage and system outages increase
Solution Approach 1:
The patent changes the parameter of user assessment from subjective manual evaluation to objective automated skill vector generation. By transforming user characteristics into quantifiable skill vectors through ML processing, the system can dynamically adjust access privileges based on actual demonstrated competence rather than fixed manual assessments.
Solution Approach 2:
The system implements feedback by continuously monitoring user actions and outcomes within the computing environment. The ML model uses this feedback to refine skill vector assessments and adjust privileges dynamically, allowing experienced users greater flexibility while restricting inexperienced users before they can cause resource wastage.
3Ease of operation
If configuration changes are allowed without checks, then ease of operation is improved, but system stability and security worsen
Solution Approach 1:
The patent applies preliminary action by evaluating user skill levels and determining appropriate privileges before configuration changes are made. The ML-based system pre-assesses whether a user is qualified to perform specific actions, preventing unauthorized or erroneous changes before they can impact system stability.
Solution Approach 2:
The system introduces an intermediary ML-based evaluation layer between the user and the configuration change execution. This intermediary automatically assesses user competence and mediates access decisions, allowing easy operation for qualified users while blocking potentially harmful changes from unqualified users.
Data Source
AI summary
According to some embodiments, a user vector generator may access information about a user (e.g., a software deployment developer or operator) in a user data store that contains electronic records each associated with different user. Each record may include, for example, a user identifier and user characteristics. Based on the user characteristics, the system may automatically generate a user vector indicating a computing environment skillset level for that user (e.g., beginner, intermediate, or expert). A machine learning privilege assignment platform may receive an indication of the user vector for the user and, based on the user vector and a machine learning algorithm, generate a privilege decision for that user (e.g., when the user attempts to update the system). An indication of the privilege decision may be output, according to some embodiments, to an SMT solver to review the privilege decision before granting the user access to the computing environment.