Automated Security Message Response for SOC Email Triage
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Solution Overview
Problem
Security operations center (SOC) analysts are burdened by the inefficient and error-prone manual process of managing user-reported emails, which can lead to fatigue and increased errors in triaging and remediating suspicious messages.
Innovation Solution
Implementing an automatic security message interaction system that uses an artificial intelligence conversationalist to monitor and analyze suspicious messages, provide automated responses, and facilitate ongoing conversations to educate users, while integrating with a threat detection platform for remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual triage and remediation processes are used by SOC analysts, then security monitoring can be performed, but analyst workload increases and error rates rise due to fatigue
Solution Approach 1:
The system enables self-service automation where the SOC platform automatically performs triage, analysis, and remediation of security messages without requiring manual analyst intervention for each message. The automated responder independently manages the workflow from detection to resolution, reducing analyst workload while maintaining security monitoring accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of analyst review with an automated AI-driven system that uses machine learning models and natural language processing to analyze security messages, generate responses, and perform remediation actions automatically, eliminating human fatigue and errors.
2Measurement precision
If manual processing of each security message is performed, then detailed analysis can be conducted, but processing speed decreases and efficiency is reduced
Solution Approach 1:
The system segments the security message processing workflow into distinct automated components: initial triage, threat analysis using machine learning models, automated response generation, and remediation execution. Each segment is handled by specialized automated functions, enabling detailed analysis to be performed rapidly without manual bottlenecks.
Solution Approach 2:
The automated responder operates continuously without interruption, processing security messages in real-time as they arrive. The system maintains continuous monitoring and analysis capabilities, eliminating the delays and interruptions inherent in manual processing while sustaining deep analysis quality through automated machine learning evaluation.
3Productivity
If automated responders are implemented, then analyst workload is reduced and processing efficiency improves, but the system complexity increases
Solution Approach 1:
The automated responder is designed as a universal multi-functional system that can handle various types of security messages (phishing, malware, spam), perform multiple operations (analysis, response generation, remediation), and adapt to different threat scenarios. This consolidation of multiple functions into a single platform manages complexity while delivering high productivity across diverse security challenges.
Solution Approach 2:
The system introduces an automated responder as an intermediary layer between security message reception and analyst intervention. This intermediary automatically handles routine triage, analysis, and remediation tasks, managing the complexity of security operations by filtering and preprocessing messages before they reach human analysts, thereby improving overall efficiency.
4Reliability
If manual triage processes are used, then contextual understanding can be applied, but response time increases and fatigue-related errors occur
Solution Approach 1:
The automated responder performs preliminary triage, analysis, and classification of security messages immediately upon receipt, before analyst review is needed. By conducting initial evaluation and preparation work automatically, the system reduces response time while maintaining accuracy through machine learning-based contextual analysis of message content and patterns.
Solution Approach 2:
The system implements feedback loops where the automated responder continuously learns from analyzed messages and outcomes, refining its triage accuracy over time. Machine learning models are trained on historical data and updated based on results, enabling the system to improve contextual understanding and maintain high accuracy while operating at automated speeds without human fatigue.
Data Source
AI summary
In various embodiments, a process for providing automatic security message interaction includes receiving an indication of a suspicious message, and using one or more threat analysis machine learning models to analyze the suspicious message to determine a threat analysis result of the suspicious message. The process includes automatically generating a prompt for a machine learning large-language model to generate a responsive message communicating about the suspicious message, wherein the prompt is based at least in part on a result of the threat analysis result, security policies of an entity, and a communication preference of the entity. The process includes providing the generated responsive message to a recipient of the suspicious message.