Role Level Classifier for Resource Allocation
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Solution Overview
Problem
Role-based titles in modern enterprises do not accurately reflect an employee's actual role and importance, leading to misallocation of computing resources for security operations, which can result in either wasteful over-allocation or risky under-allocation, compromising security and operational efficiency.
Innovation Solution
A method and system that utilize a role level classifier to generate and refine effective titles based on behavioral, managerial, and organizational characteristics, allowing for accurate identification of organizational roles and dynamic allocation of resources, ensuring appropriate security measures are applied to computing assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If role-based titles are used to identify employee groups for security operations, then resource allocation can be simplified and automated, but the accuracy of role identification deteriorates leading to misallocation of security resources
Solution Approach 1:
The patent segments the role identification process into multiple independent components: extracting multiple features (title, department, seniority, location, job function) and processing them through separate analysis modules to generate multiple role level scores, which are then aggregated to determine the final role level. This segmentation allows each feature to be evaluated independently for accuracy while maintaining overall system efficiency.
Solution Approach 2:
The patent changes from using a single parameter (job title) to using multiple parameters (title, department, seniority, location, job function) to identify employee role levels. Each parameter is weighted and scored independently, with the final role level determined by aggregating these multiple parameter scores, thereby improving identification accuracy without sacrificing automation.
2Reliability
If stricter security requirements are applied to all employees using role-based titles, then security coverage is improved, but resource waste increases due to over-allocation to lower-risk positions
Solution Approach 1:
The patent applies local quality by tailoring security requirements to the specific role level of each employee rather than applying uniform security measures across all employees. The system determines individual role levels based on multiple features and assigns security requirements locally to each employee's actual risk profile, ensuring adequate security coverage for high-risk positions while reducing resource allocation for lower-risk positions.
Solution Approach 2:
The patent uses partial action by applying security measures proportional to the determined role level rather than excessive uniform protection. The system calculates a role level score and applies only the necessary security requirements for that level, avoiding the waste of applying maximum security measures to all employees regardless of their actual risk profile.
3Measurement precision
If multiple features are extracted and processed to determine effective title, then role identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal feature extraction framework that handles multiple different features (title, department, seniority, location, job function) through a single multi-functional system. The same processing pipeline extracts, normalizes, scores, and aggregates various types of employee data, reducing system complexity compared to having separate processing systems for each feature type.
Solution Approach 2:
The patent incorporates feedback mechanisms where the extracted features are processed through iterative scoring and aggregation, with the results fed back to refine the role level determination. The system continuously evaluates the consistency of multiple features and adjusts the final role level assignment based on the aggregated evidence, improving accuracy while maintaining manageable complexity through structured feedback loops.
Data Source
AI summary
Disclosed herein are methods, systems, and processes to optimize role level identification for computing resource allocation to perform security operations in networked computing environments. A role level classifier to process a training dataset that corresponds to a clean title is generated from a subset of entities associated with the clean title. An initial effective title determined by the role level classifier based on processing the training dataset is assigned to an entity. A new effective title based on feature differences between the initial effective title and the clean title is re-assigned to the entity. Performance of the generating, the assigning, and the re-assigning is repeated using the new effective title instead of the clean title.