Third-Party Data Explorer for Cybersecurity Monitoring
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Solution Overview
Problem
Current cybersecurity monitoring architectures and software are limited in their ability to effectively collect, store, and analyze third-party entity activity data, leading to inadequate identification of cybersecurity vulnerabilities and increased risk of cyber threats.
Innovation Solution
A third-party data management system that processes and correlates third-party activity data to an entity profile, utilizing a cybersecurity correlation and analytics computing system to monitor and track third-party activity in real-time, enhancing the detection and prevention of cyber threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing cybersecurity monitoring architectures are used, then monitoring of network, infrastructure, and application data is performed, but insights into third-party entity security vulnerabilities are limited
Solution Approach 1:
The system extends monitoring capabilities to multiple data planes (network, infrastructure, application, and third-party entity activity) by implementing a universal monitoring architecture that can collect and analyze diverse data types from various sources including social media, news feeds, and threat intelligence feeds, enabling comprehensive security vulnerability detection across all planes
Solution Approach 2:
The monitoring architecture is segmented into distinct data plane collectors and a unified correlation engine, allowing independent collection of third-party entity activity data while maintaining centralized analysis and correlation with entity profiles for comprehensive vulnerability assessment
2Reliability
If third-party entity activity data is not efficiently collected and stored, then system complexity is reduced, but ability to identify third-party risks is prevented
Solution Approach 1:
The system introduces an intermediary entity profile database that mediates between third-party entity activity data collection and risk analysis, storing structured entity profiles that aggregate information from multiple sources and enable efficient risk identification without requiring complex real-time processing of all raw data
Solution Approach 2:
The system performs preliminary actions by pre-collecting and structuring third-party entity activity data into organized entity profiles before risk analysis is needed, including pre-processing of social media, news, and threat intelligence data into standardized formats that facilitate rapid risk identification when required
3Measurement precision
If real-time monitoring of third-party activity is implemented, then cybersecurity detection capability is improved, but resource consumption increases
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on entity profiles identified as high-risk or showing anomalous activity patterns, rather than uniformly processing all third-party entity data in real-time, thus achieving effective threat detection while reducing overall resource consumption
Solution Approach 2:
The system implements feedback mechanisms where risk analysis results and threat detection outcomes feed back into the monitoring process, dynamically adjusting which entity profiles receive intensified real-time monitoring based on their risk levels, thereby optimizing resource allocation between detection precision and computational cost
Data Source
AI summary
Systems and methods for managing third party data are provided. A third party data management system includes a processing circuit. The processing circuit is configured to receive first third party activity data from a source computing system and via a cybersecurity correlation and analytics computing system, determine a computing entity external to the third party data management system associated with the third party activity data based on at least one item extracted from the first third party activity data, periodically monitor third party activity associated with the computing entity, comprising operations to collect second third party activity data, and correlate the monitored second third party activity data to an entity profile.