Hierarchical Risk Forecasting Model for Network Systems
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Solution Overview
Problem
Integrating and analyzing risk data from separate systems in a network is challenging due to differing formats and scales, making it difficult to compare and mitigate risks effectively, and requires significant computing power.
Innovation Solution
A system that organizes risk factors into a hierarchy, determines inherent risk values, applies control strength values to generate residual risk forecasting models, and outputs these models for display on a graphical user interface, allowing for efficient risk analysis and mitigation recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If risk data from separate systems are integrated and analyzed in detail, then measurement precision and reliability of risk assessment is improved, but computing power requirements and device complexity increase significantly
Solution Approach 1:
The patent segments risk data into hierarchical groupings (e.g., risk categories, sub-categories, and individual risk factors) that can be processed independently. This segmentation allows the system to analyze risk data at appropriate levels of detail without requiring all data to be processed simultaneously with full computational intensity, thereby reducing overall computing power requirements while maintaining assessment precision.
Solution Approach 2:
The system transforms raw risk data into standardized parameters and metrics that facilitate comparison across different systems. By changing the parameter representation of risk data (e.g., normalizing scales, converting to common formats), the system improves measurement precision without requiring proportional increases in computing power, as the transformation enables more efficient processing.
2Reliability
If risk data from separate systems are integrated and analyzed in detail, then reliability of risk assessment is improved, but device complexity increases significantly
Solution Approach 1:
The patent divides the complex risk assessment system into modular components organized in hierarchies (risk categories, sub-categories, control frameworks). This segmentation allows each module to handle specific aspects of risk data independently, improving reliability through specialized processing while reducing overall system complexity by avoiding monolithic design.
Solution Approach 2:
The system introduces intermediary layers (such as standardized risk parameters, control strength metrics, and hierarchical aggregation levels) that mediate between raw risk data from different systems and the final assessment. These intermediaries enable reliable integration of diverse data sources without requiring direct complex interactions between all system components.
Data Source
AI summary
Simulated models for forecasting relative risk in a network system can be determined according to some examples. For example, a computing system can receive a set of risk data associated with a set of risk factors that are organized into a hierarchy of groupings. Each risk factor can be associated with one or more risk controls that each have a control strength value for reducing riskiness of the risk factor. The computing system can determine an inherent risk value for each grouping based on risk data associated with the grouping. The computing system can generate a risk forecasting model of residual risk for each grouping. The residual risk can be an amount of riskiness remaining after control strength values of the risk controls are applied to the inherent risk value. The computing system can output the risk forecasting model for display on a graphical user interface.