In Situ User Training for Cybersecurity Incident Resolution
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Solution Overview
Problem
Current cybersecurity incident resolution systems in enterprise environments face challenges in efficiently detecting and addressing security threats, particularly those involving potentially unwanted programs (PUPs), due to high volumes of alerts, delays, and human error.
Innovation Solution
A system and method that communicate training data to user computing devices to in situ train users on identifying potential security incidents, track user performance, and store profiles indicating their level of performance, allowing for targeted actions on security incidents based on user expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security analysts manually review all security threats at the SOC, then security incidents can be detected, but the process suffers from delays and human error due to high volumes of alerts
Solution Approach 1:
The system enables end users to autonomously detect and report security incidents on their own devices without requiring manual review by SOC analysts. Users receive real-time notifications about suspicious activities and can directly report them, making the security detection process self-service oriented and eliminating the bottleneck of manual analyst review
Solution Approach 2:
The system performs preliminary detection and filtering of security threats at the end user device level before escalating to the SOC. By pre-identifying and categorizing suspicious activities locally, the system reduces the volume of alerts requiring manual review and prepares information in advance for faster analyst response
2Productivity
If end users are involved in security incident detection, then more security threats can be identified, but users lack the training and tools to effectively identify advanced security incidents
Solution Approach 1:
The system introduces an intermediary layer of automated monitoring software and AI-driven analysis tools that bridge the gap between end users and security experts. These tools automatically analyze suspicious activities, provide users with simplified reporting interfaces, and filter out false positives, enabling users to effectively participate in detection without requiring expert knowledge
Solution Approach 2:
The system performs preliminary analysis and classification of security threats using automated tools before presenting them to users. By pre-processing complex technical data and highlighting only the most relevant suspicious activities, the system enables users to focus on what matters without being overwhelmed by technical complexity
3Reliability
If monitoring software is installed on user devices, then security incidents can be detected in real-time, but users are unwilling to allow installation due to privacy concerns
Solution Approach 1:
The system implements transparent feedback mechanisms that continuously inform users about what data is being collected, why it is needed for security, and how it protects them. Users receive regular security reports and can see the direct value of the monitoring software in protecting their devices, building trust and acceptance through visible benefits
Solution Approach 2:
The monitoring software is designed to be transparent and user-controllable, allowing users to review and manage the data being collected. By giving users control over their own security monitoring and enabling them to see the value it provides, the system transforms monitoring from an intrusive imposition into a user-requested security service
Data Source
AI summary
A method comprises communicating training data to a computing device to in situ train a user on how to identify potential security incidents; tracking performance of the user based on the training data; and storing a profile of the user in a database, the profile indicating a level of performance of the user.