Prioritized Security Recommendations via Collaborative Filtering
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Solution Overview
Problem
Security analysts face challenges in providing relevant and timely recommendations to clients for mitigating or preventing security incidents, often resulting in ineffective responses and increased costs due to the volume of log data and the need for customized actions.
Innovation Solution
A method utilizing a trained action model and collaborative filtering model to determine and prioritize recommended actions based on historical success and similarity to target organizations, reducing the time and expense of security experts by providing tailored and contextually relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If security analysts manually analyze log data and create customized recommendations for each client, then the recommendations are tailored to specific client needs, but the time and cost required increases significantly
Solution Approach 1:
The system creates templates for security recommendations based on patterns from historical incidents. These templates can be automatically instantiated and customized for specific clients, reducing the time analysts spend creating recommendations from scratch while maintaining relevance and effectiveness
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on client-specific factors such as industry, organization size, and historical incident data. This allows automated generation of customized recommendations by changing key parameters rather than manually creating each recommendation
2Reliability
If security analysts provide comprehensive recommendations for all potential security incidents, then client satisfaction increases, but the volume of recommendations becomes overwhelming and may be ignored
Solution Approach 1:
The system prioritizes recommendations based on client-specific risk factors, industry characteristics, and historical incident patterns. This ensures that the most relevant and high-impact recommendations are presented first, rather than providing an undifferentiated list of all possible recommendations
Solution Approach 2:
The system provides a prioritized subset of the most critical recommendations rather than all possible recommendations. This partial action approach ensures clients receive the essential security measures without being overwhelmed by excessive information
3Productivity
If security recommendations are generated quickly using automated systems, then response time decreases, but the relevance and effectiveness of recommendations may be reduced
Solution Approach 1:
The system incorporates feedback loops that continuously learn from client responses to recommendations and historical incident outcomes. This allows the automated system to improve recommendation relevance over time by adjusting based on what actions are actually taken and what incidents occur
Solution Approach 2:
The system pre-processes and analyzes client data, industry patterns, and historical incidents before generating recommendations. This preliminary preparation enables the system to quickly generate relevant recommendations when needed without sacrificing accuracy or relevance
Data Source
AI summary
Embodiments are disclosed for a method. The method includes determining multiple recommended actions based on a security incident using an action model trained to make recommendations. The method also includes determining multiple similar targets to a target of the security incident using a collaborative filtering model trained to assign a confidence value of similarity between two targets. The method further includes assigning a plurality of weights to the recommended actions based on one or more actions taken by the similar targets and the confidence value, and a success or failure of the recommended actions. Additionally, the method includes generating a prioritized list of the recommended actions that is sorted based on the assigned weights.