Risk-Based System Adjustment Framework
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Solution Overview
Problem
Existing approaches to assessing security risks in communication systems are inadequate as they rely on manual evaluations or simplistic metrics, failing to accurately capture the complex interdependencies and evolving nature of modern communication systems, and neglecting user-specific risks.
Innovation Solution
A method and system that adjust system properties based on a risk score associated with an entity, using a framework comprising a server with a risk determinator, APIs for communication, and storage for risk profiles and scores, allowing for tailored risk management by considering unique entity requirements and vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluations or simplistic metrics are used for risk assessment, then the system is easy to operate, but the measurement precision of security risks is insufficient
Solution Approach 1:
The system automatically performs risk assessments by having entities self-report their risk factors through standardized forms. The risk determinator autonomously calculates risk scores based on submitted data, eliminating the need for manual evaluator intervention while maintaining high measurement precision through systematic data collection and automated computation.
Solution Approach 2:
The system transforms qualitative risk factors into quantitative parameters by assigning numerical values to different risk categories (e.g., data sensitivity levels, entity trust scores). This parameterization enables precise mathematical computation of overall risk scores while keeping the interface simple for users who only need to select from predefined options rather than perform complex analyses.
2Reliability
If comprehensive risk profiles considering multiple factors are generated, then the reliability of risk assessment is improved, but the device complexity increases
Solution Approach 1:
The risk assessment framework is segmented into distinct modular components: risk factor identification modules, data collection forms, a risk determinator engine, and adjustment recommendation generators. Each module handles specific aspects of risk assessment independently, allowing comprehensive multi-factor analysis while maintaining manageable system complexity through clear separation of concerns and independent module development.
Solution Approach 2:
The risk determinator serves multiple functions simultaneously: it collects data from various sources, processes different types of risk factors, calculates risk scores, and generates adjustment recommendations. This multi-functional design consolidates what could be separate complex systems into a single unified component, improving reliability through comprehensive assessment while avoiding the complexity of multiple independent systems.
3Adaptability or versatility
If system properties are adjusted based on entity-specific risk scores, then the adaptability of the system is improved, but the ease of operation deteriorates
Solution Approach 1:
The system dynamically adjusts properties such as communication limits, data access restrictions, and monitoring intensity based on calculated risk scores. These adjustments are not static but automatically update as risk profiles change over time. The system adapts to each entity's specific risk level while presenting a unified interface that automatically applies appropriate controls without requiring manual configuration for each scenario.
Solution Approach 2:
The system implements continuous feedback loops where risk assessments are periodically updated based on new data, and system property adjustments are made accordingly. Entities receive feedback about their risk scores and the resulting property adjustments, creating a self-regulating system that adapts to changing conditions while maintaining operational simplicity through automated decision-making based on established risk thresholds and adjustment rules.
Data Source
AI summary
A method and system for adjusting properties of another system based on a risk score associated with an entity, using a framework comprising a server having a risk determinator, an API, and storage for storing a risk profile associated with the entity and a risk property associated the risk profile. The method is performed by the server, and comprises obtaining, from the storage, the risk profile associated with the entity, where the risk profile is generated by a risk determinator, and based on at least one risk property. The risk determinator generates the risk score associated with the entity based on the risk profile, and the server determines an adjustment to the properties of the system based on at least the risk score. The adjustment is output, through the API to the other system.