CVE Risk Scoring With Context-Aware Asset Prioritization
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Solution Overview
Problem
Existing data security systems struggle to efficiently manage and prioritize common vulnerabilities and exposures (CVEs) across computing assets, lacking comprehensive risk scoring and contextual awareness, which hinders effective resource allocation for mitigation.
Innovation Solution
A data security system generates combined CVE risk scores by integrating CVSS, EPSS, and KEV data, and applies context-specific scoring based on asset sensitivity and access permissions, enabling targeted risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data security systems monitor all computing assets for CVEs, then comprehensive security coverage is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent applies local quality by tailoring security monitoring and risk assessment to specific computing assets based on their contextual characteristics. The system evaluates asset sensitivity, access permissions, and other contextual factors to determine which CVEs pose the highest risk to each specific asset, rather than treating all assets uniformly. This allows comprehensive security coverage while reducing complexity by focusing monitoring resources on high-risk asset-CVE combinations.
Solution Approach 2:
The patent segments the security monitoring function by separating CVE risk assessment into multiple components: CVSS scoring, EPSS prediction, KEV status, and contextual asset evaluation. This segmentation allows the system to process and prioritize vulnerabilities in manageable steps, reducing overall system complexity while maintaining comprehensive coverage through structured analysis of multiple risk factors.
2Measurement precision
If the system evaluates multiple risk factors for each CVE, then risk assessment accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing CVSS scores, EPSS predictions, and KEV status for known CVEs before they are needed for risk assessment. This allows the system to retrieve pre-computed risk data quickly rather than calculating everything in real-time, maintaining high risk assessment accuracy while reducing processing time for each individual CVE evaluation.
Solution Approach 2:
The patent implements dynamics by adjusting the level of risk factor evaluation based on priority thresholds and asset criticality. The system can dynamically adapt its analysis depth - performing comprehensive multi-factor assessment for high-priority CVEs affecting critical assets, while using simplified assessment methods for lower-priority CVEs, thus balancing accuracy with processing time requirements.
3Productivity
If the system prioritizes CVEs based on severity alone, then high-severity vulnerabilities are addressed first, but contextual risk is overlooked
Solution Approach 1:
The patent merges multiple risk assessment dimensions - CVSS severity scoring, EPSS exploitation probability, KEV status, and contextual asset evaluation - into a unified priority determination process. This combination ensures that both raw vulnerability severity and contextual risk factors are considered together when prioritizing CVEs, preventing loss of contextual information while maintaining efficient mitigation through clear priority rankings.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously updates risk assessments based on new contextual information about computing assets and CVEs. This feedback loop ensures that priority rankings remain accurate and reflect current risk conditions, preventing loss of contextual information while enabling adaptive mitigation strategies that respond to changing asset states and vulnerability characteristics.
Data Source
AI summary
Methods, systems, and devices for collecting risk information associated with common vulnerabilities and exposures (CVEs) from multiple CVE data sources and generating a combined CVE risk score are described. A data security system may monitor for and manage data security risks associated with one or more computing or assets. The data security system may collect CVE risk information from multiple CVE data sources. The data security system may detect the presence of a computing objects associated with a CVE on a monitored computing asset. The data security system may generate a combined risk score for the presence of the computing objects associated with the CVE on the computing asset based on the risk information collected from the multiple CVE data sources and based on contextual information associated with the computing asset.