Learn KQL for Microsoft Sentinel



A course designed to refresh your KQL learning and help you to boost your application for Sentinel

What you will learn

Learn KQL basics for Microsoft Sentinel

Know the most used operators

Learn to build your first query

Learn to evaluate your KQL results

Description


Welcome to KQL for Microsoft Sentinel.

KQL is a simple query language used across multiple products like

Azure Log Analytics

Microsoft Sentinel

Azure Resource Graph

to read & write structured & unstructured data.

Course Structure

In this course we will focus on leveraging KQL for Microsoft Sentinel.

This will walk you though a basic understanding of KQL

  • Quick Start
  • Go for a quick result
  • Filter for better results
  • Leverage the joins
  • Summarize for perspective
  • Save & Reuse
  • Apply the visual
  • Build the use case

Each section has subsections for easy understanding of the topics.

A quick start happens with  searching a particular phrase -> projecting  the necessary columns -> extending the additional columns needed.


Now, to get a quick result we do distinct to find unique values -> use count -> get the top for display a limited set of result.

To Filter better result Apply where condition -> Apply TimeGeneated filter

Leverage the joins by learning about different kinds of joins

Summarize for perspective by Summarize -> make_list -> make_set

Once done save & reuse by saving as query or function.

Apply the visual for better visibility.

Start building you use case now with an example.

Outcome at completion

After you successfully complete this course you will be able to build your own KQL query from scratch to end.

Whom is this course for

Either you are new to Microsoft Sentinel , Log Analytics or KQL or you are already working in SOC on a regular basis, this course is for you.

English
language

Content

Introduction

Introduction

Quick Start

Start your KQL with Search
Project the required columns
Extend your query to build columns on the fly

Go for a quick result

Use distinct to find unique values
Use count for a quick output

Filter for better results

Apply where condition to filter better
Filter your data set with TimeGeneated

Leverage the joins

Corelate multiple datasets with joins

Summarize for perspective

Summarize your data to get an overview
Apply make_list to summarize all values
Apply make_set to summarize unique values

Save & Reuse

Save your query
Save your query as function

Apply the visual

Apply visual on a Dataset

Build the use case

Brute force in SignInLogs

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