Understanding XQL Data Sources and Structures in Cortex XDR

Heads up!

This summary and transcript were automatically generated using AI with the Free YouTube Transcript Summary Tool by LunaNotes.

Generate a summary for free
Buy us a coffee

If you found this summary useful, consider buying us a coffee. It would help us a lot!

Introduction

In the realm of cybersecurity, having a robust data querying capability is essential. Cortex XDR (Extended Detection and Response) offers a powerful querying language known as XQL (Extended Query Language). This article dives into the foundational elements of XQL, focusing on data sources, structure, and syntax. By the end of this guide, you will understand how to utilize Cortex XDR for effective data analysis and incident response.

Understanding XQL Data Sources

Every XQL query operates against specific data sources. In Cortex XDR, data sources are primarily categorized into two types: data sets and presets. Each category offers unique functionalities that enhance query efficiency and accuracy.

What are Data Sets?

Data sets are collections of data stored within the Cortex XDR system. They contain raw events reported by the XDR agent as well as logs from a variety of sources. There are several types of data sets, including:

  • System Data Sets: Built-in data sets that come pre-configured with the product. For instance, the XDR data set is designed to store endpoint-related data.
  • User Data Sets: Custom data sets created by users, often by utilizing the target stage to save the results of specific queries.
  • Lookup Data Sets: Data sets created by importing CSV, TSV, or JSON files. These are typically used for referencing and querying additional information.
  • Raw Data Sets: Collected data from third-party sources, including network logs from NGFWs (Next-Generation Firewalls) and other external sources.
  • Correlation Data Sets: Generated from configured correlation rules within Cortex XDR.

What are Presets?

Presets, on the other hand, are subsets of data sets. They consist of extracted fields and provide an efficient means of querying by encapsulating only the necessary information. The benefits include:

  • Efficiency: By using presets, users can query against a smaller, relevant set of fields, improving the speed and relevance of results.
  • Types of Presets:
    • Regular Presets: Typically consist of event logs categorized by specific operations like process execution or file operations.
    • Story Presets: These combine logs from multiple sources into a unified schema, beneficial for comprehensive analytics. Examples include network story and authentication story.

XQL Structure

The XQL structure is integral to understanding how to write efficient queries. When crafting queries within Cortex XDR, the following components are crucial:

Query Development Environment

The XQL coding occurs within a designated development area, often referred to as the code editor. Here you can define your queries, set parameters, and view results.

XQL Syntax

The syntax of XQL is fairly straightforward. You will primarily deal with:

  • Fields: These are the specific data points you seek to analyze.
  • Filters: Conditions that refine your search to yield more precise results.
  • Stages: Different phases where you can shape your query. For instance, defining your data source as a data set or preset.

Incorporating Data into Your Queries

To effectively utilize data sources in XQL, consider the following:

  • Always specify the data set or preset from which you are querying, unless you are relying on the default data set.
  • Utilize the schema viewer in the code editor to reference the fields available in your chosen data set or preset.

Demos and Practical Examples

Having a theoretical foundation is important, but practical implementation drives the learning process. Here are some demo examples:

Example 1: Querying a Data Set

  1. Open the XQL code editor.
  2. Type a query referencing the specific data set (e.g., data set = "XDR Data").
  3. Execute the query to view results.

Example 2: Saving Query Results to a User Data Set

  1. Define your query to select specific data.
  2. Add a Target type = data set directive.
  3. Execute the query; results will be saved to the user-defined data set.

Example 3: Utilizing a Preset

  1. Begin with a question: “What processes were executed during a specific timeframe?”
  2. Start your query with a relevant preset (e.g., using the file_preset).
  3. Fetch results rapidly due to the focused field selection.

Conclusion

Understanding XQL's data sources, structure, and syntax is pivotal for effective data analysis in Cortex XDR. By leveraging both data sets and presets, analysts can optimize their queries, improving efficiency and obtaining relevant insights quickly. As you continue to practice with the code editor and experiment with various queries, you'll enhance your skill in navigating the complexities of cybersecurity data analysis. Stay updated with the latest documentation to keep your knowledge current. Happy querying!


Elevate Your Educational Experience!

Transform how you teach, learn, and collaborate by turning every YouTube video into a powerful learning tool.

Download LunaNotes for free!