Multi-Source CVE Model Enrichment for Vulnerability Reporting
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Solution Overview
Problem
Existing systems lack an efficient and comprehensive method to monitor and analyze network activities within cloud environments for detecting anomalies and vulnerabilities, particularly in large-scale cloud environments with multiple compute assets, which are critical for security, compliance, and asset management.
Innovation Solution
A data platform is deployed to monitor and analyze network activities using agents installed on compute assets, collecting and processing data through data ingestion, processing, and user interface resources to identify anomalies and vulnerabilities, utilizing data stores like Snowflake and performing real-time or near-real-time analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive monitoring and analysis of network activities is implemented in large-scale cloud environments, then security and vulnerability detection capabilities are improved, but system complexity and resource requirements increase
Solution Approach 1:
The system segments the cloud environment into multiple compute assets, each equipped with local agents that independently collect and process network activity data. This segmentation allows comprehensive monitoring across large-scale environments while distributing complexity across multiple manageable units rather than requiring a single monolithic system.
Solution Approach 2:
Data ingestion resources serve as intermediaries between the compute assets and the analytics processing layer. These intermediaries collect, normalize, and buffer network activity data from multiple sources before it is processed by analytics resources, simplifying the overall system architecture by introducing a dedicated data handling layer.
2Speed
If real-time network activity analysis is performed to detect anomalies, then vulnerability detection speed is improved, but computational resources and processing time requirements increase
Solution Approach 1:
Agents installed on compute assets perform preliminary actions by locally collecting, filtering, and preprocessing network activity data before transmitting it to central ingestion resources. This preliminary processing reduces the volume of data requiring analysis while maintaining real-time detection capability, thereby reducing overall computational resource requirements.
Solution Approach 2:
The system implements partial analysis by focusing computational resources on analyzing only the most critical network activities and anomalies rather than processing all network traffic uniformly. Analytics resources prioritize processing based on data importance, enabling real-time detection of significant vulnerabilities while conserving computational resources.
3Measurement precision
If detailed collection and processing of network activity data is implemented, then monitoring precision and anomaly detection accuracy are improved, but data volume and storage requirements increase
Solution Approach 1:
The system extracts and separates critical network activity data from the broader network traffic stream. Agents and data ingestion resources identify and isolate only the most relevant data elements (such as connection establishment, data transfer patterns, and error conditions) for detailed analysis, while discarding or aggregating less important data, thereby reducing overall data volume while maintaining detection accuracy.
Solution Approach 2:
The system merges and aggregates network activity data from multiple compute assets into unified datasets that can be analyzed collectively. By combining data across assets and time periods, the system achieves higher monitoring precision through pattern recognition while reducing the total data volume required for analysis compared to processing each asset's data independently.
Data Source
AI summary
Gathering and presenting information related to Common Vulnerabilities and Exposures, including: gathering, from a plurality of data sources, information describing a Common Vulnerability and Exposure (CVE); generating, based on the information, an enriched model object for the CVE comprising portions of the information from different data sources of the plurality of data sources; and presenting a report for the CVE based on the enriched model object.