Vulnerability Risk Scoring Across Network Assets Over Time

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Solution Overview

Problem

Existing systems struggle to effectively characterize and manage vulnerabilities across assets in a computer network, particularly in identifying and prioritizing vulnerabilities based on asset associations and risk scores, and tracking changes in vulnerability presence over time.

Innovation Solution

A method that aggregates objects from various sources to extract attributes of assets and vulnerabilities, calculates risk scores, and generates visualizations to prioritize remediation, while tracking asset and vulnerability changes over time, using asset and vulnerability containers to consolidate and normalize data across different sources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If vulnerability data is collected from multiple sources and assets are tracked over time, then vulnerability identification completeness is improved, but system complexity increases

Engineering Contradiction:

Improvevulnerability identification completenessVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments vulnerability management by creating separate container objects for different asset types (devices, applications, data) and organizing vulnerability data into structured groups. Each asset type has its own container with specific attributes, allowing the system to handle complex multi-source vulnerability data through modular organization rather than monolithic processing.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces container objects as intermediary data structures that mediate between multiple vulnerability sources and the analysis system. These containers normalize and standardize vulnerability attributes from different sources, serving as an intermediate layer that simplifies data integration and reduces system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If risk scores are calculated based on multiple factors including asset criticality and vulnerability severity, then risk assessment accuracy is improved, but computational requirements increase

Engineering Contradiction:

Improverisk assessment accuracyVSAvoidcomputational requirements

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system changes parameters by assigning different weights to various risk factors based on asset criticality levels. Instead of uniform risk calculation, the system dynamically adjusts the importance of different vulnerability attributes according to the criticality of affected assets, enabling accurate risk assessment while optimizing computational resources through selective parameter emphasis.

Inventive Principle:

Principle #35Parameter changes

3Stability of the object's composition

If vulnerability data is normalized and stored in structured containers, then data consistency is improved, but data processing time increases

Engineering Contradiction:

Improvedata consistencyVSAvoiddata processing time

Core Design Contradiction:

Stability of the object's compositionVSLoss of time

Solution Approach 1:

The system performs preliminary normalization and structuring of vulnerability data into standardized containers during data ingestion. By pre-organizing data into consistent formats with defined attributes and relationships before analysis, the system eliminates the need for repeated data processing and validation, ultimately reducing total processing time despite initial normalization overhead.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12563086B2Method for characterizing and managing vulnerabilities across assets in a computer network

Publication Date: 2026.02.24 ARCTIC WOLF NETWORKS INC

AI summary

A method includes: storing attributes represented in objects, generated by sources during a time interval, into a container storing a set of attributes characterizing the first asset during the time interval; accessing a combination of attributes defining a first vulnerability in a set of vulnerabilities; identifying a subset of assets, including the first device, associated with the first vulnerability based on the combination of attributes represented in the set of attributes; calculating a vulnerability risk score, in a set of vulnerability risk scores, associated with the first vulnerability based on a quantity of devices in the subset of devices and a severity score assigned to the first vulnerability; selecting a subset of vulnerabilities, in the set of vulnerabilities, exhibiting highest vulnerability risk score; and generating a visualization indicating the subset of vulnerabilities, including the first vulnerability, and the quantity of devices associated with the first vulnerability.