Event Stream Processor Context Enrichment for Threat Detection
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Solution Overview
Problem
Existing log data analysis systems are inefficient in identifying and correlating events across different systems, lacking effective methods to enrich log data with user and system context, which hinders effective threat detection and security monitoring.
Innovation Solution
The system transfers master data between a backend computing system and an event stream processor, enriching log data by associating user and system contexts, creating or updating contexts as needed, and transforming log data into a normalized format for threat detection, enabling correlation and semantic analysis of log entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If log data is enriched with user and system context, then threat detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by transferring master data (user data, system data) to the event stream processor before log data analysis. This pre-population of context information enables the ESP to enrich log entries with relevant user and system contexts during threat detection, improving accuracy without adding complexity to the core analysis logic
Solution Approach 2:
The event stream processor acts as an intermediary component between the backend computing system and the log data analysis process. It receives master data from the backend, processes log data, and enriches it with appropriate user and system contexts. This intermediary architecture isolates the complexity of context management from the core threat detection logic
2Reliability
If log data from multiple systems is correlated, then threat detection capability is improved, but data processing complexity increases
Solution Approach 1:
The event stream processor is designed as a universal component that can process log data from multiple different computing systems and correlate them with user and system contexts. It handles diverse log formats and sources through a unified processing framework, enabling multi-system correlation without proportionally increasing processing complexity
Solution Approach 2:
The system segments the complex task of multi-system log correlation into distinct functional components: master data transfer, log data processing, context enrichment, and threat detection. The event stream processor handles specific segments of this pipeline, managing complexity through functional decomposition
Data Source
AI summary
A transfer of master data is executed in a backend computing system. The master data includes user data and system data. The transfer of master data includes receiving user data associated with a particular user identifier in the backend computing system, transferring the received user data to an event stream processor, receiving system data associated with a particular log providing computing system in the backend computing system, transferring the received user data to the event stream processor, and executing a transfer of log data associated with logs of computing systems connected to the backend computing system.