Predictive AI Security Event Analysis With Micro-Macro Patterning
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Solution Overview
Problem
Existing systems lack effective tools and processes for analyzing micro and macro security event patterns to predict future incidents and develop mitigation strategies, often relying on macro patterns that obscure underlying trends and failing to address historical data effectively.
Innovation Solution
A predictive AI system using machine learning models to analyze historical security incidents, identify patterns and trends, and generate mitigation strategies by integrating data from various sources, including news articles, social networks, and internal reports, to forecast future events and implement proactive security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If macro patterns are used for forecasting security events, then the analysis can be simplified and faster, but the underlying micro data trends are obfuscated and lost
Solution Approach 1:
The system segments the security event data into multiple levels: macro-level patterns (regional, national trends) and micro-level patterns (specific incident types, perpetrator methodologies). This segmentation allows the system to process and analyze both levels simultaneously without one obscuring the other, resolving the contradiction between forecasting speed and information preservation.
Solution Approach 2:
The patent introduces a multi-dimensional analysis framework that simultaneously operates at different levels of granularity (macro and micro). By adding this dimensional layer, the system can maintain both the overview needed for fast forecasting and the detailed micro-trends that drive accurate predictions, eliminating the trade-off between speed and information loss.
2Measurement precision
If historical security event data is analyzed to identify patterns, then predictive accuracy improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The system divides historical data into structured categories (macro patterns and micro patterns) and processes them through separate analytical modules. This segmentation reduces the complexity of handling the entire dataset at once while maintaining comprehensive analysis capability, thereby improving predictive accuracy without proportionally increasing processing complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw historical data into structured pattern representations. This intermediary layer simplifies the data processing complexity by pre-organizing and indexing data in meaningful ways, enabling accurate predictive analysis without requiring complex real-time processing of the entire historical dataset.
3Reliability
If multiple data sources are integrated for comprehensive analysis, then the quality of security event prediction improves, but the system complexity and data integration requirements increase
Solution Approach 1:
The system employs a universal data integration framework that handles multiple data sources (internal security events, external news, social media) through a common processing architecture. This multi-functional approach allows the system to ingest and analyze diverse data types using unified methods, improving prediction quality while controlling system complexity through standardization.
Solution Approach 2:
The patent introduces data normalization and standardization as intermediary steps that bridge multiple data sources. By transforming various data formats and sources into a common representation scheme, the system integrates diverse information without requiring complex source-specific processing logic, thereby improving prediction quality while managing system complexity.
4Loss of time
If real-time security incident information is processed, then response time to predicted events is reduced, but the computational resources and processing speed requirements increase
Solution Approach 1:
The system performs preliminary processing of historical data to pre-identify and store relevant patterns and relationships. This advance preparation allows the system to quickly match new real-time incidents against pre-computed patterns, reducing real-time processing requirements and computational resource consumption while maintaining fast response times.
Solution Approach 2:
The system segments the real-time processing task into pattern matching (using pre-computed micro and macro patterns) and prediction generation. This segmentation allows the computationally intensive pattern analysis to be performed offline on historical data, while real-time processing only requires lightweight pattern matching, thereby reducing instantaneous computational resource requirements while maintaining fast response.
Data Source
AI summary
Predictive analysis for potential future security events may be performed using machine learning and an artificial intelligence model. Machine learning may be based on historical security event data, and analyzed on micro and macro levels to determine possible future events and a likelihood that those future events may occur. Further, machine learning and artificial intelligence models may be used to determine mitigation strategies to address possible future events.