SOAR Platform Misconfiguration Detection via ML
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Solution Overview
Problem
Existing cybersecurity management systems struggle to effectively detect and address misconfigurations in cybersecurity threat protection applications, leading to increased vulnerability and security breaches.
Innovation Solution
A computer-implemented method using a security orchestration, automation, and response (SOAR) platform to access and manage cybersecurity threat protection applications, accumulate threat indications, analyze for abnormalities, and infer misconfigurations, with the option for machine learning-driven remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection and analysis of misconfigurations in cybersecurity applications is performed, then detection precision can be maintained through expert analysis, but productivity is reduced due to time-consuming manual processes
Solution Approach 1:
The system enables automated self-detection and self-analysis of misconfigurations through machine learning models that automatically process threat indications and generate remediation recommendations without requiring manual expert intervention for each case
Solution Approach 2:
Manual expert analysis is replaced with automated machine learning-based analysis systems that process threat indications, detect abnormalities, and generate remediation recommendations through computational algorithms rather than human experts
2Reliability
If comprehensive monitoring of cybersecurity threat protection applications is implemented, then reliability of security protection is improved, but device complexity increases due to multiple applications and data sources
Solution Approach 1:
The system provides universal functionality by handling multiple types of threat indications from various cybersecurity applications through a single unified platform that performs accumulation, analysis, and remediation recommendation generation
Solution Approach 2:
Multiple data sources and threat indication streams from different cybersecurity applications are merged and consolidated into a unified analysis process that detects misconfigurations across the entire security infrastructure
3Productivity
If automated remediation actions are implemented, then productivity in addressing misconfigurations is improved, but reliability may be reduced due to potential false positives from automated inference
Solution Approach 1:
The system incorporates feedback mechanisms where remediation recommendations are generated based on analyzed threat indications, and the outcomes of these remediations are fed back to improve the accuracy of future automated detections and reduce false positives
Data Source
AI summary
A computer-implemented method for cybersecurity management is disclosed. One or more cybersecurity threat protection applications deployed across a managed network are accessed. The cybersecurity threat protection applications are managed using a security orchestration, automation, and response (SOAR) platform. One or more threat protection indications from the cybersecurity threat protection applications are accumulated and analyzed. The analyzing determines an indication abnormality, inferring a cybersecurity threat protection application misconfiguration. The misconfiguration can be based on false positive indications, conflicting indications from two or more cybersecurity threat protection applications, or time-sequenced indications from one or more cybersecurity threat protection applications. The analyzing and inferring are performed using machine learning which is embedded in the SOAR platform. Remedial actions based on the inferred misconfiguration are provided to personnel staffing a security operations center and/or ingested by the SOAR for automatic reconfiguration.