Bayesian Risk Model for Cyber and Geographic Threat Analysis
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Solution Overview
Problem
Current risk modeling tools for businesses are inadequate in collecting and collating data from diverse sources, particularly for rare events like cyber-attacks, and fail to model dependencies between risk factors, resulting in an unclear and technical representation of combined risk, making it difficult for organizations to get a clear, fact-based picture of their risk without significant additional work.
Innovation Solution
The solution employs Bayesian networks to model complex interdependencies between risk factors, combining data from various sources, including dark web, open sources, and internal intelligence, and provides a user-friendly interface for creating and customizing risk models tailored to specific industries and geographic locations, allowing for real-time decision support and risk mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical models are built from past data to extrapolate future risk, then historical data utilization is improved, but the ability to model rare events like cyber-attacks deteriorates due to insufficient historical data
Solution Approach 1:
The patent combines multiple data sources including dark web sources, open source intelligence, proprietary data, and internal business intelligence to create a comprehensive risk model. This merging of diverse data sources enables the system to model rare events like cyber-attacks by aggregating signals from multiple channels rather than relying solely on insufficient historical internal data.
Solution Approach 2:
The patent introduces a Bayesian network as an intermediary computational framework that processes and integrates data from multiple disparate sources. The Bayesian network serves as a mediator that combines probabilistic reasoning with multi-source data to produce reliable risk assessments for rare events, bridging the gap between limited historical data and the need for robust rare event modeling.
2Device complexity
If risk registers are used to model risk, then structured risk documentation is improved, but the ability to provide proper risk measurement and model dependencies between risk factors deteriorates
Solution Approach 1:
The patent transforms the static, qualitative nature of traditional risk registers into a dynamic, quantitative Bayesian network model. By changing the parameters from simple categorical entries to probabilistic variables with defined relationships, the system enables precise measurement of risk and modeling of dependencies between risk factors while maintaining structured documentation.
3Productivity
If technical risk modeling tools are used, then modeling capability is improved, but ease of understanding and accessibility to non-technical users deteriorates
Solution Approach 1:
The patent introduces a user interface as an intermediary layer between the complex Bayesian network modeling engine and the end user. This interface translates technical probabilistic models into visually intuitive risk heat maps, gauges, and actionable insights, enabling non-technical users to understand and interact with sophisticated risk models without needing to grasp the underlying mathematical complexity.
Data Source
AI summary
Methods and apparatus consistent with the invention provide the ability to combine data from multiple different sources of risk data, to create a weighted risk model using Bayesian networks and Monte-Carlo simulation to advance a quantified risk outlook. Based on the client risk configuration file, a risk user interface (UI) template is selected and the modelled risk is generated into a graphical user interface (GUI) using the selected risk template to display the GUI at multiple summary levels starting at a high-level overview which will include cyber, economic, legal, brand, operational and geographic risks. The GUI of modelled risk is displayed on the client device using the selected UI template. The system enables the user to drill down into the GUI for any of the categories available in the selected UI template to further examine risk characteristics as well as the actual sources of the risk in the modelled risk.