People-Centric Cybersecurity Segmentation for Risk Reduction
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Solution Overview
Problem
Conventional cybersecurity tools are inadequate in addressing the increasing threat landscape, particularly in targeting individuals rather than devices, as they fail to provide a granular, people-centric approach to security, leading to inefficiencies and resource misallocation.
Innovation Solution
Implementing a people-centric cybersecurity approach that segments users into groups based on risk levels, using machine learning models to assess individual risk scores and apply tailored security protocols, focusing on characteristics like behavior and access to sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional cybersecurity tools are used to protect all users uniformly, then security coverage is comprehensive, but resource allocation is inefficient and security measures are not optimized for individual risk levels
Solution Approach 1:
The patent segments users into different risk groups (e.g., high-risk, medium-risk, low-risk) based on their behavior patterns, device characteristics, and access requirements. This segmentation enables the security system to apply different security protocols to different user groups, improving adaptability while managing complexity through standardized group-based policies rather than individual customization.
Solution Approach 2:
The patent implements local quality by tailoring security measures to specific user groups rather than applying uniform security across all users. High-risk users receive enhanced security controls such as multi-factor authentication and stricter access policies, while low-risk users experience streamlined security processes, optimizing security effectiveness for each local context.
2Reliability
If granular, people-centric security monitoring is implemented for all users, then security effectiveness is improved, but IT personnel time and resources are excessively consumed
Solution Approach 1:
The patent applies partial action by focusing intensive security monitoring and personnel attention only on high-risk user groups identified through automated risk assessment. Low-risk users receive automated, lighter-touch security measures, reducing the time and resources required from IT personnel while maintaining adequate security coverage across the entire organization.
Solution Approach 2:
The patent implements self-service through automated risk assessment systems that continuously monitor user behavior and automatically adjust security protocols without requiring manual IT intervention. The system autonomously identifies high-risk users, applies appropriate security controls, and alerts personnel only when human judgment or intervention is necessary, significantly reducing IT personnel time consumption.
3Measurement precision
If device-specific security installations and updates are performed manually, then security control is precise, but productivity is reduced due to extensive manual effort
Solution Approach 1:
The patent creates universal security protocols that can be automatically applied to multiple user groups simultaneously based on their risk classification. Instead of manually configuring security settings for each device individually, the system defines reusable security templates for different risk levels that can be automatically deployed across numerous users, maintaining precise security control while dramatically improving deployment productivity.
Solution Approach 2:
The patent replaces manual mechanical processes of security installation and updates with automated electronic systems. Risk assessment algorithms automatically analyze user behavior and device characteristics, then trigger automated security policy deployment and updates through system interfaces, eliminating the need for manual device-by-device security configuration while maintaining precise security control.
Data Source
AI summary
Systems, apparatuses, and methods for more effectively preparing for and responding to cybersecurity threats directed at people or at groups of people. A segmentation process is described that evaluates multiple characteristics of a person that may make them a potential target or that may make a cybersecurity attack on that person more likely to be successful. Based on the segmentation, a security analyst can apply an appropriate risk reduction or security protocol to each person or group of similarly situated people to reduce the likelihood of an attack and/or the likelihood of a successful attack.