Neural Network SOAR Playbook Generation
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Solution Overview
Problem
Existing SOAR systems require manual intervention by analysts to create playbooks for new incidents, leading to delays, inefficiencies, and error-prone processes, as conditions triggering playbooks are static and do not account for novel incidents.
Innovation Solution
Training a neural network to learn from historical incidents and playbooks, allowing it to recommend and automatically execute appropriate playbooks for new incidents by analyzing attributes and features, thereby reducing manual effort and increasing response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis and playbook creation by analysts is used for new incidents, then playbook accuracy and reliability are improved, but response time and productivity deteriorate
Solution Approach 1:
The system enables self-service by allowing the neural network to automatically generate playbooks for new incident types without requiring manual analyst intervention. The neural network learns from historical incident data and playbooks to autonomously create appropriate response procedures for previously unseen incidents, eliminating the bottleneck of waiting for analyst availability while maintaining reliable playbook quality through continuous learning and validation mechanisms.
Solution Approach 2:
The neural network performs preliminary learning and pattern recognition during training phases using historical incident data and existing playbooks. This preliminary action prepares the system in advance to quickly generate accurate playbooks for new incident types when they occur, reducing response time while maintaining reliability through pre-learned knowledge patterns.
2Ease of operation
If static triggering conditions are used for playbook selection, then system simplicity and ease of operation are improved, but adaptability to new incidents deteriorates
Solution Approach 1:
The system transitions from static triggering conditions to dynamic, adaptive playbook selection using a neural network. The neural network continuously learns from new incident data and adjusts its playbook recommendations accordingly, enabling the system to adapt to novel incident types while maintaining operational simplicity through automated machine learning processes that require minimal manual reconfiguration.
Solution Approach 2:
The system changes the parameters of playbook selection from fixed static conditions to dynamic parameters learned by the neural network from historical data. This allows the system to adapt to new incident types by learning new patterns and relationships in the data, improving versatility while maintaining ease of operation through automated learning rather than manual rule updates.
3Manufacturing precision
If manual playbook creation is used for new incidents, then playbook quality and precision are improved, but loss of time and efficiency deteriorate
Solution Approach 1:
The neural network performs self-service by automatically generating playbooks for new incident types without requiring manual analyst creation. It learns from historical incident data and existing playbooks to autonomously produce high-quality playbooks, eliminating the time loss associated with waiting for analyst availability while maintaining playbook quality through continuous learning and validation.
Solution Approach 2:
The neural network creates new playbooks by learning patterns and structures from existing historical playbooks and incident responses. It effectively copies and adapts proven playbook patterns to new incident types, maintaining high playbook quality through pattern replication while dramatically reducing the time required compared to manual creation from scratch.
4Measurement precision
If human analysts manually create playbooks, then error detection and measurement precision are improved, but device complexity and automation level deteriorate
Solution Approach 1:
The neural network enables self-service by automatically detecting errors and validating playbook quality without requiring human analyst review. Through continuous learning from historical data and feedback mechanisms, the system autonomously identifies and corrects errors in playbook generation, maintaining high measurement precision for error detection while maximizing automation and eliminating manual intervention.
Data Source
AI summary
A security orchestration, automation, and response (SOAR) playbook is often selected to address an incident, such as a fault or attack (e.g., malware, a phishing attack, etc.) on a computer system or component. However, when the incident is new, manual resolution is often utilized to address the incident. By utilizing a neural network trained to identify similarities in a new incident, the neural network can select a SOAR playbook and optionally automatically deploy the playbook to address the incident.