ZTNA Micro-Segmentation Policy Generation for Incremental Firewall Testing
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Solution Overview
Problem
Current network security approaches face challenges in managing complex network environments with dynamic workloads, as manual firewall rule setting is time-consuming and prone to errors, and existing forward testing methods are inefficient for smaller scale network elements.
Innovation Solution
Implementing systems and methods for determining zero trust network access (ZTNA) policies based on intent-defined groups of workloads, using machine learning and automation to enhance granularity and efficiency of security parameter setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual firewall rule modification is performed to secure numerous applications, then security coverage is improved, but administrative time and complexity increase significantly
Solution Approach 1:
The patent segments the firewall rule management process into automated discovery, policy generation, and incremental deployment phases. The system divides complex security policy creation into manageable components by automatically identifying workloads, generating rules in groups, and applying them incrementally, thereby reducing administrative time while maintaining comprehensive security coverage.
Solution Approach 2:
The system enables self-service automation where the firewall management system automatically discovers workloads, generates security policies, and deploys rules without requiring manual operator intervention for each rule. This automated self-service approach significantly reduces administrative time while ensuring consistent security coverage across all applications.
2Reliability
If comprehensive firewall rules are implemented across all workloads, then security reliability is improved, but system complexity and error susceptibility increase
Solution Approach 1:
The patent segments comprehensive security coverage into incremental deployments. Instead of implementing all firewall rules at once which would create high complexity, the system divides rules into manageable batches, applies them incrementally, and validates each segment before proceeding, thereby maintaining security reliability while reducing management complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where each incremental rule deployment is automatically tested and validated before full implementation. This feedback loop allows the system to verify security effectiveness at each stage while identifying and correcting potential errors early, reducing overall complexity and error susceptibility.
3Reliability
If incremental forward testing is performed at application level, then deployment resilience is improved, but testing process duration increases
Solution Approach 1:
The patent segments the testing process into application-level incremental stages rather than performing monolithic testing. By dividing the testing into smaller, manageable application-specific segments that can be executed in parallel and validated incrementally, the system improves deployment resilience while actually reducing total testing duration through efficient resource utilization.
4Manufacturing precision
If manual security rule testing is performed, then rule accuracy is improved, but downtime and operational disruption increase
Solution Approach 1:
The patent performs preliminary automated testing and validation of security rules in a staging environment before deploying them to production. This preliminary action ensures rule accuracy is verified in advance, allowing for accurate rule deployment without causing operational downtime, as the rules are pre-validated and ready for immediate production deployment.
Solution Approach 2:
The system introduces an intermediary automated testing and validation layer between rule generation and production deployment. This intermediary process automatically verifies rule accuracy through simulated testing and analysis, eliminating the need for manual testing that would cause downtime, while ensuring high rule accuracy through systematic validation.
Data Source
AI summary
Systems, devices, and methods are discussed for determining zero trust network access policy based upon intent defined groups of workloads.