Employee Segmentation for Cybersecurity Risk Reduction
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Solution Overview
Problem
Conventional cybersecurity tools are inadequate in addressing the evolving threat landscape, particularly in targeting individuals and groups within organizations, as they lack a people-centric approach, fail to consider user-specific characteristics, and are inefficient in resource allocation and threat monitoring.
Innovation Solution
Implementing a people-centric cybersecurity approach that uses machine learning models to segment employees based on seniority and functional roles, identifying potential targets and applying tailored risk reduction protocols to mitigate customized attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cybersecurity tools are used to monitor and protect all users and devices, then comprehensive security coverage is achieved, but IT personnel time and resources are excessively consumed
Solution Approach 1:
The patent segments the user population into distinct groups based on job characteristics, seniority levels, and functional roles. This segmentation allows security resources to be focused on high-risk groups rather than uniformly applied to all users, thereby maintaining comprehensive security coverage while significantly reducing the time and resources required for monitoring and protection.
Solution Approach 2:
The patent applies different security protocols and monitoring intensities to different user segments based on their specific risk profiles. High-risk groups receive enhanced security measures and closer monitoring, while low-risk groups receive standard protection. This localized approach to security quality ensures adequate protection across all users while optimizing resource allocation and reducing overall personnel time requirements.
2Ease of operation
If conventional cybersecurity tools are used with a non-granular approach, then implementation and maintenance are simpler, but the ability to address specific people-oriented threats is insufficient
Solution Approach 1:
The patent automatically segments users into risk-based groups using machine learning models that analyze job characteristics, seniority, and functional roles. This automated segmentation provides granular, people-centric security capabilities without requiring complex manual configuration or implementation, thereby maintaining ease of operation while significantly improving adaptability to specific threat scenarios.
Solution Approach 2:
The system employs machine learning models that automatically analyze user data, determine risk profiles, and assign users to appropriate security groups without requiring manual intervention from IT personnel. This self-service approach enables granular, adaptive security responses while keeping the system easy to operate and maintain.
3Stability of the object's composition
If security protocols are applied uniformly to all users, then security policy consistency is maintained, but the ability to alter security measures based on individual user behavior and situation is lost
Solution Approach 1:
The patent implements security protocols that are consistently applied within each user segment while allowing different segments to have different security measures. This ensures policy consistency within groups while enabling user-specific adjustments based on risk profiles, seniority levels, and functional roles. The machine learning models continuously monitor user behavior and situations to dynamically adjust security measures appropriately.
Solution Approach 2:
The system dynamically adjusts security protocols based on user behavior, situation, and risk profile changes while maintaining overall policy consistency. The machine learning models continuously evaluate user activities and automatically modify security measures for individual users or groups as needed, balancing consistency with adaptability.
4Reliability
If device-specific security installations and updates are performed for all devices, then comprehensive device protection is achieved, but a large amount of IT personnel time and resources are expended
Solution Approach 1:
The patent segments devices into groups based on the security risk profiles of their users and the sensitivity of accessed data. This segmentation allows IT personnel to focus device-specific security installations and updates on high-risk devices and user groups, maintaining comprehensive protection for critical systems while reducing overall resource expenditure on low-risk devices.
Solution Approach 2:
The system applies different levels of device protection and monitoring intensity to different device segments based on their risk profiles. High-risk devices receive enhanced security measures and frequent updates, while low-risk devices receive standard protection. This localized approach ensures adequate device protection across the organization while significantly improving IT resource efficiency.
Data Source
AI summary
Systems, methods, and apparatuses directed to implementations of an approach and techniques for more effectively preparing for, detecting, and responding to cybersecurity threats directed at people or at groups of people. Embodiments are directed to classifying or segmenting employees by “predicting” what are believed to be two attributes of an employee that contribute to making them at a higher risk of being a target of a cybersecurity attack. These attributes are the employee's seniority level (e.g., employee, contractor, manager, executive, board member) and the employee's primary function or role in an organization (e.g., HR, Legal, Operations, Finance, Marketing, Sales, R&D, etc.