AI-Driven SOAR Workflow Modification for Cybersecurity
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Solution Overview
Problem
Existing cybersecurity systems struggle to effectively detect and respond to constantly evolving and adapting cybersecurity threats, relying on traditional methods that are often delayed and prone to false positives.
Innovation Solution
The implementation of an AI-driven cybersecurity management system that utilizes a SOAR platform to access and manage cybersecurity threat protection applications, executing dynamic workflows that include antivirus analysis, threat hunting, and incident lifecycle case management, and automatically updating remedial steps in real-time based on machine learning analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cybersecurity detection methods are used, then system simplicity is maintained, but detection speed and accuracy deteriorate due to delays and false positives
Solution Approach 1:
The patent introduces an AI-driven workflow modification system as an intermediary layer between threat detection and response. This intermediary analyzes detection accuracy and automatically modifies workflows to improve precision without requiring complete system redesign, thus enhancing detection accuracy while managing complexity through modular integration
Solution Approach 2:
The system implements feedback mechanisms where detection results and workflow execution outcomes are continuously analyzed by AI models. This feedback loop enables automatic refinement of detection workflows, improving accuracy over time while maintaining system manageability through automated optimization rather than manual reconfiguration
2Speed
If manual cybersecurity workflow management is used, then ease of operation is maintained, but response time deteriorates due to human delay in detecting and responding to threats
Solution Approach 1:
The patent applies preliminary action by pre-configuring AI-driven workflows that automatically execute upon threat detection. Instead of waiting for manual intervention, the system has pre-established response protocols that activate immediately, significantly reducing response time while maintaining operational clarity through structured automation
Solution Approach 2:
The system dynamically adjusts automation levels based on threat severity and workflow context. The AI models continuously optimize automation parameters, enabling flexible automation that adapts to different scenarios. This dynamic approach enhances response speed for critical threats while preserving manual control for complex situations, balancing speed and automation extent
3Adaptability or versatility
If static cybersecurity workflows are used, then ease of operation is maintained, but adaptability deteriorates against constantly evolving threats
Solution Approach 1:
The patent implements dynamic workflows where AI models continuously learn from new threat data and automatically adjust workflow parameters. This enables the system to adapt to evolving threats in real-time without requiring manual workflow redesign. The dynamic adjustment mechanism enhances adaptability while managing complexity through automated optimization rather than manual reconfiguration
Solution Approach 2:
The system applies self-service by enabling workflows to automatically optimize themselves based on detected patterns and threat evolution. The AI models analyze execution results and self-tune workflow parameters, eliminating the need for manual intervention to maintain adaptability. This self-service approach enhances versatility against new threats while keeping complexity manageable through autonomous optimization
4Reliability
If comprehensive cybersecurity monitoring is implemented, then security coverage is improved, but false positives increase due to system overload
Solution Approach 1:
The patent implements feedback mechanisms where the AI models continuously analyze security events and workflow outcomes. By learning from false positives and adjusting detection thresholds accordingly, the system maintains comprehensive coverage while reducing false positive rate through data-driven optimization of monitoring sensitivity
Solution Approach 2:
The system dynamically changes monitoring parameters based on threat context and historical data. The AI models adjust detection sensitivity, priority thresholds, and alert criteria to optimize the balance between coverage and false positives. This parameter adaptation enables comprehensive security monitoring while minimizing false alarms through intelligent parameter tuning
Data Source
AI summary
Disclosed embodiments provide techniques for cybersecurity AI-driven workflow modifications. A security orchestration, automation, and response (SOAR) platform used to manage a plurality of cybersecurity threat protection applications deployed across a cybersecurity network is accessed. A cybersecurity workflow is executed using the SOAR platform and one or more cybersecurity actions related to the workflow are captured and analyzed for workflow relevance. The cybersecurity actions can include steps taken by security operations center staff and automated cybersecurity threat protection applications. The analysis can be performed by machine learning, and can include evaluations of repeated cybersecurity incidents, operation regression exercises, and suggested remedial steps. The workflow analysis can include identifying recidivistic security operations responses. Based on the analysis, the cybersecurity workflow is updated to improve workflow quality. The updating can include reordering the workflow steps, automating responses of operations staff, or executing actions recommended by separate AI cybersecurity systems.