Event-Based Network Micro-Segmentation for Policy Impact Modeling
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Solution Overview
Problem
Current systems lack the ability to automatically and proactively identify the potential impacts of real-time events or policy changes on an enterprise-wide level, and they do not effectively combine analysis of technology stack changes and policy/regulation changes.
Innovation Solution
A system that monitors real-time data inputs across multiple channels, determines potential impacts on a network or entity policy taxonomy, and takes automated responsive actions. This system utilizes machine learning and artificial intelligence to provide predictive and proactive feedback to enterprise workflows, optimizing processes and protections in response to current events, policy changes, or predicted measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a system monitors real-time data inputs across multiple channels and uses machine learning to analyze events and policy changes, then the ability to proactively identify potential impacts is improved, but the system complexity and computational resources required increase
Solution Approach 1:
The system segments the complex analysis task into distinct modules: an event monitoring component that collects real-time data from multiple channels, a machine learning engine that processes and analyzes events, and an impact assessment module that evaluates potential effects on enterprise workflows. This segmentation allows each component to specialize in specific functions, improving overall reliability while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary layer between raw event data and impact conclusions. The machine learning engine acts as a mediator that processes unstructured event data, extracts meaningful patterns, and translates them into structured impact assessments. This intermediary layer simplifies the connection between complex data sources and decision-making processes.
2Productivity
If the system provides comprehensive predictive feedback on enterprise workflows, then the productivity and optimization capabilities are improved, but the time required for analysis and response increases
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring and preprocessing event data in real-time before impacts occur. The machine learning engine pre-processes incoming data streams, identifying patterns and potential risks proactively. This allows the system to provide immediate impact assessments when events occur, rather than requiring lengthy retrospective analyses.
Solution Approach 2:
The patent replaces manual workflow analysis and impact assessment with automated machine learning algorithms. The ML engine automatically processes events, evaluates their potential impacts on enterprise workflows, and generates predictive feedback without human intervention. This substitution eliminates time-consuming manual analysis while maintaining comprehensive assessment capabilities.
3Loss of information
If the system integrates analysis of both technology stack changes and policy/regulation changes, then the comprehensiveness of impact assessment is improved, but the difficulty of detecting and measuring impacts increases
Solution Approach 1:
The patent implements a universal event monitoring framework that handles multiple types of inputs through a single system. The machine learning engine is designed to process diverse event types (technology changes, policy changes, regulatory updates) using unified algorithms and data structures. This multi-functional approach enables comprehensive impact assessment across different domains while maintaining consistent detection and measurement methodologies.
Solution Approach 2:
The system transforms qualitative event descriptions into quantifiable parameters that can be measured and compared. The machine learning engine extracts key features from diverse events and represents them as standardized parameters, enabling systematic impact assessment. This parameter transformation allows the system to detect and measure impacts across different event types using consistent metrics.
Data Source
AI summary
A system is provided for monitoring real-time data inputs across multiple channels, determining potential impacts to a network or entity policy taxonomy, and taking one or more automated, responsive actions based on determined impacts. In this way, the system described herein is able to optimize system processes and system protections on an enterprise-wide scale in response to current events, policy or regulation changes, or predicted measures taken by private or public entities that may necessitate adaptation of one or more workflow processes or technology taxonomies, either upstream or downstream of the system itself.