NGFW Cyber Twin for Firewall Rule Risk and Priority Analysis
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Solution Overview
Problem
Current firewall management systems are labor-intensive, error-prone, and inadequate in addressing misconfigurations, policy sprawl, and policy intent gaps, leading to a high risk of security breaches.
Innovation Solution
An automated system using advanced algorithms and machine learning techniques to analyze firewall rules, identify security risks, inconsistencies, and suggest optimization strategies, providing actionable insights to administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of firewall rule sets is performed, then security risks can be identified, but the process is labor-intensive and error-prone
Solution Approach 1:
The patent replaces manual mechanical analysis of firewall rules with an automated machine learning system. The ML model automatically analyzes firewall rule sets, identifies security risks, and provides recommendations without human intervention, thereby eliminating labor-intensive manual processes while maintaining high accuracy through algorithmic analysis.
Solution Approach 2:
The system enables self-service firewall rule analysis by automatically processing rule sets and generating security assessments. The automated ML-based analysis engine continuously monitors and evaluates firewall configurations without requiring manual initiation, providing ongoing security insights autonomously.
2Reliability
If comprehensive firewall rule analysis is performed to identify all security risks, then security posture improves, but the complexity of managing and maintaining rules increases
Solution Approach 1:
The patent introduces an intermediary machine learning system that mediates between complex firewall rule sets and security analysts. The ML model simplifies the complexity by automatically processing intricate rule configurations, identifying patterns, and presenting simplified security risk assessments and recommendations, thereby reducing the perceived complexity for administrators.
Solution Approach 2:
The system segments the complex firewall rule analysis into distinct analytical components handled by the ML model, such as identifying shadowed rules, detecting security risks, analyzing rule efficiency, and generating recommendations. This segmentation breaks down the overwhelming complexity into manageable, automated analytical tasks.
3Productivity
If automated machine learning analysis is implemented, then analysis speed and accuracy improve, but the system complexity increases
Solution Approach 1:
The patent implements a universal machine learning platform that performs multiple firewall analysis functions simultaneously - risk identification, shadowed rule detection, efficiency analysis, and recommendation generation. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single unified platform, managing complexity through integration rather than proliferation of components.
Data Source
AI summary
The present application discloses a method, system, and computer system for managing policy configurations. The method includes (i) receiving a set of predefined security policy rules, (ii) determining, based at least in part on the set of predefined security policy rules, one or more security policy rules that do not satisfy one or more predefined requirements, (iii) performing a priority or position analysis to determine a relationship among a plurality of security policy rules, and (iv) providing a report pertaining to the one or more security policy rules.