Automated Security Scoring Ontology for Third-Party Risk
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Solution Overview
Problem
Securing large computer networks against third-party risks is challenging despite the availability of security ratings and tools, as network administrators face difficulties in detecting and assessing risks in real-time and understanding the underlying factors contributing to these risks.
Innovation Solution
A computer security monitoring method and system that continuously aggregates risk scores for monitored entities by gathering machine-readable facts, deriving risk profiles, and using an ontology to associate entities, providing visual and interactive reports to users, and issuing alerts when predetermined criteria are met, allowing for real-time risk assessment and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security ratings and tools are used to evaluate third-party risks, then security assessment capability is improved, but the difficulty of detecting and measuring risks in real-time increases
Solution Approach 1:
The system segments risk assessment into multiple components: entity-level risk scores, relationship-level risk scores, and aggregated organizational risk scores. This segmentation allows real-time detection of individual entity risks while maintaining comprehensive organizational security assessment capability.
Solution Approach 2:
The patent introduces an intermediary risk score mechanism that translates complex security facts into standardized risk profiles. These risk profiles serve as intermediaries between raw security data and final risk assessments, enabling real-time detection while maintaining assessment reliability.
2Speed
If continuous monitoring and aggregation of risk scores is implemented, then real-time risk detection speed is improved, but device complexity increases
Solution Approach 1:
The system merges multiple risk assessment functions into a unified continuous monitoring process. By combining entity risk scoring, relationship risk scoring, and aggregation operations into a single coordinated system, real-time detection speed is improved without proportionally increasing complexity.
Solution Approach 2:
The risk profile structure serves multiple functions: it stores entity-level risks, enables relationship mapping, supports aggregation operations, and provides the basis for alert generation. This multi-functionality reduces overall system complexity while maintaining real-time detection capability.
3Ease of operation
If detailed ontological relationships and triggering conditions are disclosed to users, then ease of operation and understanding is improved, but loss of information increases
Solution Approach 1:
The system extracts only the necessary ontological relationship details and triggering conditions when users request explanations for risk scores. This selective extraction provides ease of operation and understanding while minimizing information loss by only disclosing relevant details on demand.
Solution Approach 2:
The information disclosure mechanism is dynamic, adapting the level of detail based on user needs. The system can provide high-level summaries or detailed ontological relationships depending on user interaction, balancing ease of operation with information preservation.
Data Source
AI summary
Computer security systems and methods are disclosed. In one general aspect, a computer security monitoring method is disclosed that includes continuously gathering machine-readable facts relating to a number of topics and continuously deriving and storing risk profiles for a plurality of monitored entities based on at least some of the facts. This method also includes providing an ontology that associates a different subset of the monitored entities to each of a plurality of organizational entities possessing digital assets, aggregating the risk scores for the scored entities for each of the organizational entities based on the associations in the ontology to derive an aggregated risk score, and electronically reporting the aggregated risk score to an end user.