Non-linear Risk Assessment for Heterogeneous Enterprise Assets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing risk assessment methods for enterprises are inefficient as they rely on pre-defined formulas that do not effectively adapt to different types of assets, leading to inconsistent risk calculations across various asset categories.
Innovation Solution
A compliance management system that includes a risk management module using non-linear statistical data models, such as neural networks, to analyze multiple sources of information and calculate a risk score for assets, allowing for flexible policy management and asset discovery, configuration, and risk factor mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-defined formulas are used for risk calculation, then the risk assessment process is simple and fast, but the accuracy and adaptability to different asset types deteriorates
Solution Approach 1:
The patent transforms the rigid pre-defined formulas into dynamic parameter sets that can be adjusted based on asset type and risk context. The system allows modification of risk factors, their weights, and calculation parameters to match specific asset characteristics, thereby maintaining calculation speed while improving accuracy across diverse asset types.
Solution Approach 2:
The patent introduces dynamic adaptability to the risk assessment system by allowing the formula parameters to change based on the asset being assessed. Different asset types (physical, digital, intellectual, biological) have different applicable risk factors and calculation parameters, making the system flexible rather than static.
2Ease of operation
If pre-defined formulas are used for risk calculation, then the system is easy to operate, but the adaptability to different asset types deteriorates
Solution Approach 1:
The patent creates a universal risk assessment framework that can handle multiple asset types (physical, digital, intellectual, biological) through a single integrated system. The same basic formula structure is used across all asset types, but with configurable parameters that adapt to each specific asset category, achieving both ease of operation and versatility.
Solution Approach 2:
The system maintains ease of operation by keeping the overall formula structure consistent across asset types, while allowing specific parameters (risk factors, weights, thresholds) to change based on the asset being assessed. This parameter-based flexibility enables the system to adapt to different asset types without requiring users to learn completely different calculation methods.
3Measurement precision
If comprehensive risk factors are collected and analyzed, then the risk assessment accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent segments the comprehensive risk assessment into distinct components: identification of asset-specific risk factors, determination of appropriate weights, selection of relevant controls, and calculation of risk scores. This segmentation allows the system to handle complexity in a structured, manageable way while maintaining high accuracy through consideration of multiple factors.
Solution Approach 2:
The system manages complexity by using parameter-based configuration rather than hard-coded complex logic. The comprehensive risk factors are represented as configurable parameters that can be adjusted based on asset type and organizational requirements, allowing accuracy to be improved without proportionally increasing system complexity.
Data Source
AI summary
A modern business enterprise will have a large number of heterogeneous assets. The risk associated with a selected asset from the heterogeneous assets can be assessed. In one embodiment, the present invention includes selecting the asset from a plurality of heterogeneous assets for risk analysis, and collecting a plurality of risk factors associated with the selected asset. The risk associated with the asset can be determined by providing the plurality of risk factors to a non-linear statistical data model to derive a risk score associated with the asset.