Bi-directional ML Network Compatibility Engine
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Solution Overview
Problem
Entities face challenges in ensuring compliance of third-party networks with their own security and control requirements, especially when these requirements are unknown or not readily available, which can jeopardize the digital environment if not met.
Innovation Solution
A bi-directional machine-learning-based network compatibility engine scans both the host and target networks to determine control requirements, generates a compliance report with a compatibility score, and provides an executable file to update the target network to conform to the host network's standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a third party network is integrated with the host network to expand service capabilities, then network functionality and service coverage are improved, but security risks and compliance violations increase
Solution Approach 1:
The system performs preliminary scanning and assessment of the third-party network before integration, identifying security vulnerabilities and compliance gaps in advance. Control requirements are determined and communicated to the third party before they implement changes, preventing security issues rather than reacting to them afterward.
Solution Approach 2:
The system continuously monitors the third-party network for compliance with control requirements, providing ongoing feedback to both the host and third-party networks. When violations are detected, the system generates notifications and tracks remediation progress, creating a closed-loop feedback mechanism that maintains security compliance over time.
2Measurement precision
If comprehensive scanning and analysis of the host network is performed to determine control requirements, then security compliance accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The scanning and analysis process is divided into distinct modules: a scanning module that collects network data, a machine learning module that determines control requirements, and a compliance assessment module that evaluates third-party networks. This segmentation allows each component to specialize in specific tasks, improving accuracy while managing complexity through modular design.
Solution Approach 2:
A machine learning model serves as an intermediary between raw network scan data and control requirement determinations. The ML model processes complex network configurations and automatically infers appropriate control requirements, reducing the need for manual configuration and simplifying the overall system while maintaining high accuracy.
3Measurement precision
If detailed compliance assessment and scanning is conducted to identify all violations, then compliance detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs targeted scanning focused on specific control requirements and high-risk areas rather than exhaustive scanning of every network element. The machine learning model identifies which control requirements are most relevant to the specific third-party network, allowing the system to concentrate assessment efforts where they will have the greatest impact on compliance accuracy while reducing overall processing time.
Data Source
AI summary
An ML-based method for conforming a target network to control requirements of a host network is provided. The method may include running a first digital scan of the host network and determining the host network's control requirements based on the first digital scan. The method may include identifying, based on the second digital scan, elements of the target network that violate the control requirements. The method may include generating a compliance report and/or an executable file. The compliance report may include a compatibility score of the target network vis-à-vis the host network, and a compatibility plan that includes steps which improve the compatibility score and conform the target network to the control requirements of the host network. The executable file, when executed at the target network, may execute the compatibility plan.