Network Security GUI With Late-Binding Schema Data Intake
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data generated from diverse sources, such as system logs, network packets, and sensors, is challenging due to the vast amount of data and varying formats, leading to inefficiencies in data retrieval and analysis.
Innovation Solution
The implementation of an event-based data intake and query system that uses a late-binding schema to process and store machine data, allowing for flexible schema definition and extraction rules application at search time, enabling field-searchability and efficient data retrieval across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed and only specified data items are extracted and stored, then data retrieval and analysis efficiency is improved, but the flexibility to analyze all generated data is lost
Solution Approach 1:
The system performs preliminary indexing of all raw data fields without discarding any data, creating a searchable index structure that enables both efficient retrieval and flexible analysis. The index is built in advance but maintains all original data characteristics, allowing analysts to query any field without pre-specification.
Solution Approach 2:
The system changes the parameter of data storage from storing only extracted/processed data to storing all raw data with associated metadata. This parameter change enables both efficient retrieval through indexing and flexible analysis of all data types, resolving the contradiction between retrieval speed and analytical versatility.
2Adaptability or versatility
If massive quantities of raw data are stored for later retrieval, then data analysis flexibility is improved, but data retrieval and search performance deteriorates
Solution Approach 1:
The system segments the massive raw data into indexed fields and metadata components, organizing data by type, source, and other characteristics. This segmentation allows the system to retrieve only relevant portions of data based on query parameters, maintaining high retrieval performance while storing all raw data for flexible analysis.
Solution Approach 2:
The system introduces an indexing layer as an intermediary between raw data storage and data retrieval operations. This index structure acts as a mediator that enables fast search and retrieval across massive datasets without requiring the analysis system to scan all raw data, thus maintaining both flexibility and performance.
3Loss of information
If all generated data is analyzed instead of only pre-specified subsets, then insight derivation opportunities are improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary organization and indexing of all data during the data ingestion phase, before analysis occurs. This preliminary action structures the data in a way that enables comprehensive analysis without requiring complex processing during query execution, reducing the complexity burden to the initial setup phase.
Solution Approach 2:
The indexing system automatically organizes and tags all raw data fields without requiring manual pre-specification of analysis parameters. This self-service approach to data organization enables comprehensive analysis of all generated data while minimizing the complexity of data processing, as the system autonomously prepares data for any potential analysis scenario.
Data Source
AI summary
A graphical user interface (GUI) for presentation of network security risk and threat information is disclosed. A listing is generated of incidents identified by use of event data obtained from a networked computing environment. A particular incident is determined to be associated with a risk object, wherein a risk object is a component of the networked computing environment. The listing is populated with a name associated with the risk object. Risk events associated with the incident are determined, wherein each risk event contributes to a risk score for the incident. The risk score indicates a potential security issue associated with the risk object. The listing is populated with the risk score and a summary of the events. An action is associated with the listing, for triggering display of additional information associated with the risk object. The listing can be displayed in a first display screen of the GUI.