Network Security Data Aggregation System for Private Network Privacy
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Solution Overview
Problem
Existing network security systems face challenges in maintaining privacy while providing security services, as they often require accessing sensitive data within private networks, which can lead to data exfiltration risks when shared with external service providers.
Innovation Solution
Implementing a system that conducts searches and aggregates data within the private network, omitting sensitive information before reporting to external service providers, using a network interface, memory, and processor to receive messages, invoke searches, aggregate data, and transmit reports, thereby preventing sensitive data exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network security data is accessed and shared with external service providers, then security analysis capability is improved, but data exfiltration risk increases
Solution Approach 1:
A data aggregation system acts as an intermediary between internal network data sources and external security service providers. The system receives security event data from multiple internal sources, aggregates and anonymizes it, then exports only the aggregated results externally. This mediator architecture allows security analysis capabilities to be enhanced through external services while preventing raw sensitive data from directly leaving the organization's control environment.
Solution Approach 2:
The system extracts only the essential security-relevant features from raw network data while leaving behind identifying and sensitive information. By taking out only the necessary security indicators (such as threat patterns, anomaly types, and aggregated counts) and removing PII and sensitive operational details, the organization gains external security analysis capabilities without exposing harmful amounts of sensitive data.
2Measurement precision
If detailed network security data is exported externally, then security incident detection accuracy is improved, but privacy protection deteriorates
Solution Approach 1:
The system applies different data treatment qualities to different portions of the data. Sensitive fields such as user identifiers, IP addresses, and proprietary information are heavily anonymized or removed, while security-relevant fields such as threat indicators, anomaly patterns, and behavioral metrics are preserved with high fidelity. This local quality differentiation maintains detection accuracy for security purposes while protecting privacy for sensitive information.
3Object-affected harmful factors
If all network security data is retained internally, then data security is improved, but security analysis effectiveness deteriorates
Solution Approach 1:
The data export process is segmented into multiple stages: raw data collection, aggregation by security-relevant dimensions, anonymization of sensitive fields, and selective export of processed results. This segmentation allows the organization to maintain full raw data internally for security reasons while exporting processed, less-sensitive aggregated data externally for advanced analysis, thus achieving both data security and analysis effectiveness.
Data Source
AI summary
Systems and methods are disclosed for computing network operations. For example, methods may include receiving, at a computing device located within a private network, a message sent from a server located outside of the private network, the message including an observable; invoking, within the private network, a search of data associated with the private network to obtain a search result that includes data matching the observable; aggregating, within the private network, data from the search result that matches the observable to obtain a report that includes an indication of the observable, a count of occurrences of the observable, and identification of one or more components associated with the observable; and transmitting the report to the server.