Risk Engine Combining Weighted Factors for Access Score
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Solution Overview
Problem
Existing risk engine systems do not adequately reflect the actual risk of access attempts and are not intuitive, making it difficult for administrators to configure applications to manage access effectively.
Innovation Solution
A method and system for combining risk factors to produce a total risk score, allowing administrators to define risk policies with percentages for each factor and mitigating factors, which are then adjusted and combined to generate a percentage-based risk score between 0% and 100%, enabling more accurate risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk scoring methodology is used, then the risk score can be generated by combining multiple conditions, but the risk score does not adequately reflect the actual risk and is not intuitive for administrators
Solution Approach 1:
The risk assessment is segmented into discrete risk factors (e.g., geographic location, device type, time of access) and mitigating factors, each assigned a specific weight percentage. This segmentation allows administrators to configure and understand individual components while the system combines them to produce an accurate overall risk score.
Solution Approach 2:
The system changes the parameters of risk assessment by introducing weighted percentages for each risk factor and mitigating factor. Administrators can adjust these parameters to reflect organizational risk tolerance, making the system both accurate and intuitive for configuration.
2Reliability
If multiple risk factors are combined to generate a risk score, then the assessment can be more comprehensive, but the methodology becomes complex and difficult to configure
Solution Approach 1:
The comprehensive risk assessment is broken down into separate, configurable risk factors and mitigating factors. Each factor is independently weighted and can be configured separately, reducing the perceived complexity while maintaining comprehensive coverage.
Solution Approach 2:
The system provides dynamic configuration capabilities where administrators can adjust weights and thresholds based on changing organizational needs. The risk engine dynamically calculates scores based on these configurable parameters, maintaining reliability while adapting to different complexity requirements.
3Measurement precision
If mitigating factors are applied to reduce risk factor impact, then the risk assessment becomes more accurate, but the calculation methodology becomes more complex
Solution Approach 1:
Mitigating factors act as counterweights to risk factors, reducing their impact on the overall risk score. For example, strong authentication or trusted device status can offset the risk associated with certain geographic locations or access patterns, improving precision through balanced assessment.
Solution Approach 2:
The system manages calculation complexity by using standardized parameter changes where mitigating factors apply predefined reduction percentages to risk factor scores. This approach maintains precision while keeping the calculation methodology configurable and understandable.
Data Source
AI summary
A risk engine can be configured to produce a total risk score by combining a set of risk factors. A risk policy can define a percentage that is to be assigned to each risk factor that is present in a request to access a web-accessible application. The percentage can represent the amount of risk that can be attributed to the access request when the risk factor is present in the request. The risk policy can also define which mitigating factors apply to each risk factor. Each mitigating factor can also be assigned a percentage by which the mitigating factor will reduce the risk factor when the mitigating factor and risk factor are present in the access request. The risk factors can then be combined to produce the total risk score. The total risk score can be generated as a percentage between 0% and 100%.