Risk Score Determination for Data Access Monitoring
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Solution Overview
Problem
Conventional monitoring systems in data access lack the necessary resources to effectively monitor entities with varying levels of access, leading to inefficiencies in system monitoring.
Innovation Solution
A method for determining a risk score for entities by identifying parameters such as access levels, permissions, location, and history, which are then used to assess and mitigate potential risks, enabling targeted monitoring and action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional monitoring systems monitor all entities with different access levels, then comprehensive monitoring coverage is achieved, but system resources are insufficient and monitoring efficiency deteriorates
Solution Approach 1:
The patent applies local quality by differentiating monitoring intensity based on entity risk profiles. Instead of uniform monitoring across all entities, the system adjusts monitoring parameters and resource allocation according to the specific risk characteristics of each entity, thereby optimizing the balance between comprehensive coverage and efficient resource utilization.
Solution Approach 2:
The system dynamically changes monitoring parameters such as frequency, depth, and type of monitoring based on calculated risk scores. Entities with higher risk scores receive intensified monitoring while lower-risk entities receive reduced monitoring, allowing the system to maintain comprehensive coverage while improving overall monitoring efficiency through parameter adaptation.
2Measurement precision
If the system monitors all data access parameters in detail, then monitoring precision is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent extracts and prioritizes the most critical access parameters that contribute to risk assessment. By identifying and focusing monitoring efforts on the most significant parameters rather than all possible parameters, the system achieves high measurement precision for risk-relevant data while reducing the complexity of the monitoring infrastructure.
Solution Approach 2:
The monitoring system segments data access parameters into different categories based on their risk implications. This segmentation allows the system to apply different monitoring precision levels to different parameter types, maintaining high precision for critical security-related parameters while using simpler monitoring for less critical parameters, thus managing overall system complexity.
3Adaptability or versatility
If the system allocates monitoring resources uniformly across all entities, then fair monitoring coverage is achieved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system changes resource allocation parameters dynamically based on entity risk profiles. Entities with higher risk scores receive proportionally more monitoring resources while lower-risk entities receive fewer resources, optimizing overall resource utilization efficiency while maintaining fairness through transparent, criteria-based allocation decisions.
Solution Approach 2:
The risk assessment system automatically determines monitoring resource requirements for each entity based on their inherent risk characteristics. This self-service approach eliminates the need for manual resource allocation decisions, ensuring fair and efficient resource distribution by allowing entities to self-qualify for appropriate monitoring levels based on their risk profiles.
Data Source
AI summary
In accordance with embodiments, there are provided mechanisms and methods for determining a risk score for an entity. These mechanisms and methods for determining a risk score for an entity can enable more effective monitoring of a system, can create more relevant data associated with the entity, etc.