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Getting Started

This guide provides step-by-step instructions on how to set up Sigma and convert its rules into different SIEM formats. It also includes some basic configuration tips to help you make the most of Sigma's features. The guide is suitable for both experienced users and beginners, and it aims to provide clear and concise information to help you successfully work with Sigma and its ecosystem.


The Sigma ecosystem offers several tools for your use. This documentation will mainly focus on the primary Sigma converter, called sigma-cli, which converts all Sigma rules into usable SIEM queries for your security environment(s).


To use sigma-cli (the Sigma Rule Converter) & the underlying library, you must have Python >= 3.8 installed.

Using pip

The main conversion tool for Sigma is sigma-cli. The easiest way to install sigma-cli is through the Python 3 package manager pip.

pip3 install sigma-cli


Be sure that you’ve added Python's binary location (usually ~/.local/bin) to your PATH, so you can run sigma-cli straight from your CLI. Visit Python's website for Windows and more info.

From Source

If you don't want to use pip, or if you instead want to download and install sigma-cli from source, first install Poetry on your system, then clone and install the required dependencies using Poetry.

git clone
cd sigma-cli
poetry install && poetry shell
sigma --version

Install your SIEM plugin


Once you've installed sigma-cli, you will need to install your backend plugin through sigma-cli.

Newly introduced, the sigma-cli tool now offers a selection of installable backends – through a plugin system, each designed to target a specific SIEM commonly used by users. To see which backend plugins are available, run the sigma plugin list command from the command line.

sigma plugin list
| Identifier           | Type     | State   | Description                                                  | Compatible? |
| splunk               | backend  | stable  | Splunk backend for conversion into SPL ...                   | yes         |

Throughout this guide, Splunk will be used as the SIEM conversion example.

Converting Sigma Rules

Basic Example

Once you have installed the sigma-cli tool and the associated SIEM backend plugin, create a directory that will contain your Sigma rules and function as your "detection-as-code" repository. Consolidating your rules in one place is considered best practice and simplifies the deployment process.

Next, to create a Sigma rule that will detect when someone tries to disable Windows Defender's Threat Protection, create a YAML file called windows_defender_threat_detection_disabled.yml and fill it with the example Sigma detection shown below.

# Create a test repository to store your detections-as-code
mkdir sigma_test_repo && cd sigma_test_repo
mkdir ./rules

# Create a new Sigma rule
vim ./rules/windows_defender_threat_detection_disabled.yml
title: Windows Defender Threat Detection Disabled
    # Windows Defender
    product: windows
    service: windefend
        # The detection itself
            - 5001
            - 5010
            - 5012
            - 5101
    condition: selection

Quickly running through this rule, we can describe it as detecting when the field EventID matches the following cases:

  • A user has disabled Windows Defender's Real-time protection or
    EventID: 5001
  • A user has disabled Windows Defender's Anti-Spyware protection or
    EventID: 5010
  • A user has disabled Windows Defender's Anti-Virus protection or
    EventID: 5012
  • Windows Defender's Antimalware platform has expired
    EventID: 5101

After saving the file and closing vim (or your preferred editor), you can use the sigma convert command to convert this Sigma rule into a Splunk query. It's important to set the correct parameters to ensure a valid query is produced for the SIEM being used in your environment.

sigma convert \
    --target splunk \
    --pipeline splunk_windows \
source="WinEventLog:Microsoft-Windows-Windows Defender/Operational" \
EventCode IN (5001, 5010, 5012, 5101)

Once events are populated under the WinEventLog:Microsoft-Windows-Windows Defender/Operational source within your SIEM – this example being Splunk, you can easily use this query to set up a detection and alert when someone disables Windows Defender within your monitored environment.

Congratulations, you're now ready to start detecting security threats using Sigma! 🎉

Next Steps

One of the best features of the Sigma format is taking advantage of the 1000's of existing detections for many popular enterprise OS's, Software and Systems, including Microsoft Windows, Microsoft 365, Okta, and many more.

Visit the SigmaHQ/sigma rule repository

Adding Contextual Information

While the previous example demonstrated a simple detection, in practice, Sigma rules contain additional metadata to provide context for the detection. This metadata may include severity (called "level" within Sigma), references, false-positives, tags (such as MITRE Attack mapping), and a rationale for the detection.

To better illustrate this point, let's take a look at a more complex Sigma rule, taken from the SigmaHQ/sigma repository written by Austin Songer.

# ./rules/cloud/okta/okta_user_account_locked_out.yml
title: Okta User Account Locked Out
id: 14701da0-4b0f-4ee6-9c95-2ffb4e73bb9a
status: test
description: Detects when an user account is locked out.
author: Austin Songer @TheAustinSonger
date: 2021/09/12
modified: 2022/10/09
    - attack.impact
    product: okta
    service: okta
        displaymessage: Max sign in attempts exceeded
    condition: selection
    - Unknown
level: medium

It's worth noting that Sigma rules often contain fantastic metadata about a detection, which is highly useful when investigating a security incident.

Learn more about Rules

To learn more about the fields above and how they are used in Sigma,
visit the Rules section of the documentation.

Converting this rule using the Splunk backend, and using the Splunk pipelines outputs the following query.

sigma convert \
    --target splunk \
    --pipeline splunk_windows \
displaymessage="Max sign in attempts exceeded"

Outputting to Different Formats

Notice that adding the metadata to this Sigma rule hasn't changed the output of this query – using the default output format. Sigma supports outputting to multiple formats, unique to each backend.

We can see this metadata being used if we change the output format to savedsearches.

# Convert to Splunk savedsearches format
sigma convert \
    --format savedsearches \
    --target splunk \
    --pipeline splunk_windows \
dispatch.earliest_time = -30d
dispatch.latest_time = now

[Okta User Account Locked Out]
description = Detects when an user account is locked out.
search = displaymessage="Max sign in attempts exceeded"

Custom Field & Source Mapping

Although many logsources in enterprise IT environments are similar, it is essential to recognize that each SIEM is uniquely configured, particularly in its usage of field names, data types, and log formats.

To address these variances, sigma-cli provides end-users with the ability to perform field-mapping when converting rules. This function ensures any fields found in Sigma rules correctly map to users' fields found within their SIEM.

To best illustrate the adaptability of the Sigma format, we will onboard a custom logsource (in this example, an internal production service called puppy_app_production) and its corresponding detection rule, into our detections-as-code repository.

# Inside of sigma_test_repo, create a pipelines directory
mkdir ./pipelines

# Create a new Sigma pipeline file
vim ./pipelines/puppy_app_production_config.yml
# ./pipelines/puppy_app_production_config.yml
name: Puppy Application – Splunk Log Source Configuration
priority: 100
  - id: prefix_source_and_index_for_puppy_logs
    type: add_condition
      index: 'puppy_prod'
      source: 'PuppyApp/App'
      - type: logsource 
        product: puppy
        service: app
  - id: map_fields_for_puppies
    type: field_name_mapping
      status: 'puppy.status' 
      dog_name: ''
      dog_breed: 'puppy.breed'
      - type: logsource
        product: puppy
        service: app
# ./rules/sad_puppy.yml
title: Sad Puppy in Dog Supply Line
id: 469b8469-508d-42d0-98a1-0c7e937ca7a3
status: experimental
description: >
    Detects whenever a sad puppy is logged to the Dog Supply Line (DSL) log stream output.
    See wiki on how to triage with treats and/or walks.
author: Toto <>
date: 2023/04/06
    product: puppy
    service: app
        status: 'sad'
    condition: selection
    - dog_name
    - dog_breed
    - status
    - sometimes sad dogs are reported as guilting owners for walks, treats etc.
level: high


In this configuration example, any rules that have a match on the following:
product: puppy and service: app

Sigma will apply index='puppy_prod' source='PuppyApp/App' to the resultant SIEM query output, and map each field across, for example status to puppy.status, dog_name to etc.

We can combine this configuration example with the custom sad_puppy.yml detection rule, that will detect whenever our log source detects a sad puppy in our SIEM.

sigma convert \ 
    -t splunk \
    -p ./pipelines/puppy_app_production_config.yml \
index="puppy_prod" source="PuppyApp/App" Status="sad" 
| table dog_name,dog_breed,Status

Learn more about Logsources & Field Mapping

Each supported SIEM should have its own configuration already pre-defined, with most fields and logsources mapped for you.

This is completed via a feature in pySigma called processing pipelines. If you're an end-user however, you'll find the documentation on Logsources more relevant to mapping your Sigma rules to your logsources.

You can read more in here in Log Sources.

What's Next?

From here, you've understood the basics of Sigma. It's time to dive deeper into learning more about Logsources, Rules and Backends.