Threat-Based Cybersecurity Training for User-Specific Risk Profiles
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Solution Overview
Problem
Existing cybersecurity training systems struggle to provide personalized and efficient training to users based on their specific threat encounters, which are often determined by limited processing power and network bandwidth, making it difficult to identify susceptible users and optimize resource consumption.
Innovation Solution
A computing platform receives user-specific threat information from a TAP server, identifies users for targeted training topics, and dynamically adapts training modules based on individual threat profiles, including 'top clickers' and 'very attacked persons', to deliver tailored cybersecurity training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cybersecurity training is provided to all users, then user awareness is improved, but resource consumption increases
Solution Approach 1:
The system transitions from uniform training for all users to personalized training based on individual threat profiles. Each user receives training content tailored to their specific threat exposures, susceptibility factors, and role-based risks, thereby optimizing resource allocation while maintaining effectiveness.
Solution Approach 2:
The user base is segmented into distinct groups based on threat profile characteristics, susceptibility metrics, and exposure levels. This segmentation enables the system to deliver targeted training modules to specific segments rather than treating all users uniformly, reducing overall resource consumption.
2Reliability
If personalized training is provided based on threat profiles, then training effectiveness is improved, but system complexity increases
Solution Approach 1:
The system employs a unified framework that handles multiple functions: threat profile analysis, susceptibility assessment, training module selection, and delivery. This multi-functional approach manages complexity by consolidating operations into a cohesive system rather than separate components.
Solution Approach 2:
The system continuously monitors user interactions with training modules and updates threat profiles based on this feedback. This closed-loop approach improves training effectiveness by adapting to user performance while managing complexity through automated adjustment mechanisms.
3Productivity
If training modules are dynamically loaded based on user identification, then resource efficiency is improved, but processing time increases
Solution Approach 1:
The system pre-identifies users who require training based on their threat profiles and susceptibility metrics before training delivery. This preliminary identification allows for proactive preparation of training modules, reducing actual processing time when training is delivered.
Solution Approach 2:
The system dynamically loads training modules based on real-time user identification and profile matching. This dynamic approach optimizes resource efficiency by loading only necessary modules while managing processing time through efficient matching algorithms and cached profile data.
Data Source
AI summary
Aspects of the disclosure relate to dynamically providing cybersecurity training based on user-specific threat information. A computing platform may receive, from a targeted attack protection (TAP) server, user-specific threat information indicating at least one threat that has been encountered by at least one user. The computing platform may identify one or more users to receive cybersecurity training in a first cybersecurity training topic based on the user-specific threat information indicating the at least one threat that has been encountered by the at least one user. Subsequently, the computing platform may load one or more cybersecurity training modules based on identifying the one or more users to receive the cybersecurity training in the first cybersecurity training topic. Then, the computing platform may provide the one or more cybersecurity training modules to one or more user computing devices.