Automated Risk-Based Network Security Focus
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Solution Overview
Problem
Securing complex network environments with hundreds of software applications is challenging due to the manual complexity of setting firewall rules and determining high-risk areas, leading to inefficiencies and increased risk of lateral attacks.
Innovation Solution
Implementing a system that automatically determines risk-based focus areas by assigning risk scores to applications, application tiers, and workloads, using machine learning and automation to prioritize segmentation and protection efforts, and generating and testing access control lists to secure network access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual firewall rule configuration is used to secure network applications, then security coverage can be achieved, but the complexity of operation and time consumption increase significantly
Solution Approach 1:
The system performs self-service by automatically discovering applications, generating firewall rules, and configuring security policies without human intervention. The automated rule generation engine analyzes application traffic patterns and creates appropriate firewall rules, while the forward testing module validates rules before deployment, eliminating manual configuration efforts
Solution Approach 2:
The patent replaces manual mechanical operations with automated systems. Instead of operators manually creating and testing firewall rules, an automated engine generates rules based on application behavior analysis, and a forward testing system validates them programmatically, substituting human mechanical work with automated computational processes
2Reliability
If comprehensive security rules are implemented across all applications, then security coverage improves, but the time required for testing and implementation increases
Solution Approach 1:
The system performs preliminary actions by conducting forward testing in a simulated environment before deploying firewall rules to production. The forward testing module validates rule correctness and identifies potential issues beforehand, preventing costly rework and reducing overall implementation time
Solution Approach 2:
The patent segments the security implementation process into distinct phases: application discovery, rule generation, forward testing, and deployment. This segmentation allows parallel processing of multiple applications and enables incremental implementation, reducing the overall time required for comprehensive security coverage
3Adaptability or versatility
If manual security rule modification is performed to adapt to network changes, then security policies can be updated, but the difficulty of detecting and measuring impacts on other applications increases
Solution Approach 1:
The system implements feedback mechanisms where forward testing results provide information about rule impacts on application functionality. This feedback loop allows the system to detect and measure the effects of security rules on network applications, enabling informed adjustments and reducing the difficulty of impact analysis
Data Source
AI summary
Systems, devices, and methods are discussed for automatically determining a risk-based focus in determining zero trust network access policy on one or more network elements.