ZTNA Policy Scoring for Rule Accuracy and Manageability
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Solution Overview
Problem
There is no systematic scoring method to compare access control policy lists (ACLs) in Zero Trust Network Access (ZTNA) based on both security efficacy and manageability, leading to inefficiencies in network security and complexity.
Innovation Solution
A method for generating an efficacy scoring metric that combines ZTNA rule accuracy and manageability, using machine learning to adjust rules and reduce false positives and negatives, while optimizing the number of rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more access control policies are added to improve ZTNA security coverage, then security efficacy is improved, but ACL complexity increases and manageability deteriorates
Solution Approach 1:
The patent transforms the ACL from a static rule set to a dynamic system where rules are automatically adjusted based on ZTNA policy parameters. The efficacy scoring mechanism continuously evaluates and optimizes rule parameters, transforming manual ACL configuration into an adaptive process that balances security coverage with manageability.
Solution Approach 2:
The system implements self-service through automated ACL generation and optimization. The efficacy scoring mechanism autonomously evaluates ACL performance, identifies optimization opportunities, and adjusts rules without manual intervention, reducing the burden on network administrators while maintaining high security standards.
2Ease of operation
If access control policies are simplified to improve manageability, then ACL complexity is reduced, but ZTNA security coverage may be insufficient
Solution Approach 1:
The efficacy scoring mechanism establishes a feedback loop that continuously monitors ACL performance against ZTNA security requirements. This feedback system detects when simplified rules create security gaps and triggers automatic rule adjustments, ensuring that manageability improvements do not compromise security coverage.
Solution Approach 2:
The system performs preliminary optimization by pre-evaluating ACL configurations before deployment. The efficacy scoring mechanism assesses potential rule sets in advance, identifying configurations that achieve both simplicity and adequate security coverage, preventing security gaps before they occur.
3Adaptability or versatility
If manual ACL configuration is used to maintain security policies, then flexibility is maintained, but time consumption and operational overhead increase
Solution Approach 1:
The patent replaces manual mechanical ACL configuration with an automated intelligent system. The efficacy scoring mechanism uses algorithmic evaluation and machine learning to generate and optimize ACL rules, substituting human operators with an automated process that maintains policy flexibility while dramatically reducing time consumption.
Solution Approach 2:
The system achieves multi-functionality by combining ZTNA policy interpretation, ACL generation, efficacy evaluation, and automatic optimization into a single unified mechanism. This universal approach handles diverse security requirements while maintaining flexibility, eliminating the need for separate manual processes for each ACL management task.
Data Source
AI summary
An ZTNA efficacy score representing a combination of ZTNA rule accuracy and ZTNA rule manageability is periodically determined. The ZNTA rule accuracy is scored based on allowed legitimate flows and blocked illegitimate flows in relation to allowed illegitimate flows and blocked legitimate flows. The ZNTA rule manageability is scored based on a volume of the ZTNA rules. Responsive to the ZNTA efficacy score, the ZTNA efficacy score is raised by automatically adjusting the ZTNA rules, using machine learning, to maximize ZTNA rule accuracy and ZTNA rule manageability by reducing false positives and false negatives and by reducing the number of rules. The updated ZTNA rule set can be applied to real-time traffic.