Automated Spear Phishing Simulation for Enterprise Security
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Solution Overview
Problem
Large enterprise organizations face challenges in training users to recognize spear phishing attacks due to their personalized nature, which complicates network security and resource management, especially in balancing user training with computing resources like processing power and bandwidth.
Innovation Solution
A computing platform uses machine learning to identify susceptible users, generate simulated spear phishing messages based on historical data, and provide targeted training by dynamically initiating and managing automated spear phishing simulations, optimizing resource usage by focusing on high-risk individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spear phishing training is provided to all enterprise users, then network security awareness is improved, but computing resources (processing power and bandwidth) are excessively consumed
Solution Approach 1:
The system applies different training approaches to different user segments based on their risk profiles. High-risk users receive comprehensive spear phishing training, while low-risk users receive minimal or no training, optimizing resource allocation according to local needs rather than applying uniform training across all users
Solution Approach 2:
The enterprise user base is segmented into different risk categories using machine learning analysis of communication patterns, device usage, and security incident history. This segmentation enables targeted training delivery to specific segments, reducing overall resource consumption while maintaining effective security awareness where most needed
2Reliability
If personalized spear phishing training is provided to each user, then training effectiveness is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The system automatically generates personalized training content by having users complete assessments that reveal their specific knowledge gaps and risk factors. The system then self-configures appropriate training modules based on assessment results, eliminating the need for complex manual personalization while maintaining individualized effectiveness
Solution Approach 2:
The training system dynamically adjusts training parameters (content, duration, intensity) based on user responses to assessment questions and observed behavior patterns. This automated parameter adjustment achieves personalized training effectiveness without requiring complex system configuration for each user
3Reliability
If comprehensive user training is delivered to large enterprise organizations, then security awareness is improved, but network bandwidth consumption increases
Solution Approach 1:
Instead of delivering complete training programs to all users, the system provides partial training only to those users who demonstrate specific risk factors or knowledge gaps through assessment. This partial action approach reduces bandwidth consumption while maintaining security effectiveness by focusing resources on users who need training most
Data Source
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AI summary
Aspects of the disclosure relate to dynamic and automated spear phishing management. A computing platform may identify users to receive a simulated spear phishing message. In some instances, the computing platform may receive (515) a very attacked persons (VAP) list and may identify (520) the users to receive the simulated spear phishing message based on the VAP list. Based on historical message data associated with a first user, the computing platform may identify (525) message features associated with the first user. Using a predetermined template and for a first user account linked to the first user, the computing platform may generate (545, 550) a first spear phishing message based on the message features. The computing platform may then send (555), to the first user account, the first spear phishing message.