• Table of Contents
• Introduction
• Project Status
• LLM Support
• Installation & Usage
  • Requirements
  • Installation
  • Usage Quickstart
  • Usage Examples
• Supported Options
  • Backends
  • Pipelines
• Contributing
• License
• Maintainers

SigmAIQ is a wrapper for pySigma and pySigma backends & pipelines. It allows detection engineers to easily convert Sigma rules and rule collections to SIEM/product queries without having to worry about the overhead of ensuring the correct pipelines and output formats are used by each pySigma supported backend. SigmAIQ also contains custom pipelines and output formats for various backends that are not found in the original backend source code. If you don't see a backend that's currently supported, please consider contributing to the Sigma/pySigma community by making it with this pySigma Cookiecutter Template

In addition, SigmAIQ contains pySigma related tools and scripts, including easy Sigma rule searching, LLM support, an automatic rule creation from IOCs.

This library is currently maintained by:

• Stephen Lincoln via AttackIQ

SigmAIQ is currently in pre-release status. It is a constant work-in-progress and bugs may be encountered. Please report any issues here.

Feature requests are always welcome! pySigma tools/utils are currently not in the pre-release version, and will be added in future releases.

For LLM usage, see the LLM README

• Python 3.9+
• pip, pipenv, or poetry

SigmAIQ can be installed with your favorite package manager:

pip install sigmaiq
pipenv install sigmaiq
poetry add sigmaiq

To install the LLM dependencies, use the llm extra:

pip install sigmaiq[llm]
pipenv install sigmaiq[llm]
poetry add sigmaiq[llm]

Create a backend from the list of available backends, then give a valid Sigma rule to convert to a query. You can find the list of available backends in this README, or SigmAIQBackend.display_available_backends().

from sigmaiq import SigmAIQBackend

sigma_rule = """
    title: Test Rule
    logsource:
        category: process_creation
        product: windows
    detection:
        sel:
            CommandLine: mimikatz.exe
        condition: sel
"""

# Create backend
backend = SigmAIQBackend(backend="microsoft365defender").create_backend()

# Convert Rule or Collection
output = backend.translate(sigma_rule)
print(output)

Output:

['DeviceProcessEvents
| where ProcessCommandLine =~ "mimikatz.exe"']

Although you can pass a SigmaRule or SigmaCollection object to translate() like you would to convert() or convert_rule() for a typical pySigma backend, there is no need with SigmAIQ. As long as a valid Sigma rule is given as a YAML str or dictionary (or list of), SigmAIQ will take care of it for you.

Typical usage will be using the SigmAIQBackend class from sigmaiq to create a customized pySigma backend, then use translate() to convert a SigmaRule or SigmaCollection to a query:

from sigmaiq import SigmAIQBackend
from sigma.rule import SigmaRule

sigma_rule = SigmaRule.from_yaml(
    """
    title: Test Rule
    logsource:
        category: process_creation
        product: windows
    detection:
        sel:
            CommandLine: mimikatz.exe
        condition: sel
    """
)

backend = SigmAIQBackend(backend="splunk").create_backend()
print(backend.translate(sigma_rule))

Output: ['CommandLine="mimikatz.exe"']

Passing the output_format arg will use an original output specified by the original backend, or a custom format implemented by SigmAIQ. You can find information about output formats specific to each backend via SigmAIQBackend.display_backends_and_outputs()The necessary processing pipelines are automatically applied, even if the original pySigma backend does not automatically apply it:

from sigmaiq import SigmAIQBackend
from sigma.rule import SigmaRule
from sigma.backends.splunk import SplunkBackend

sigma_rule = SigmaRule.from_yaml(
    """
    title: Test Rule
    logsource:
        category: process_creation
        product: windows
    detection:
        sel:
            CommandLine: mimikatz.exe
        condition: sel
    """
)
# Raises sigma.exceptions.SigmaFeatureNotSupportedByBackendError
orig_backend = SplunkBackend()
print("Original Backend:")
try:
    print(orig_backend.convert_rule(sigma_rule, output_format="data_model"))
except Exception as exc:
    print(exc)
print("\n")

# Necessary pipeline for output_format automatically applied
print("SigmAIQ Backend:")
sigmaiq_backend = SigmAIQBackend(backend="splunk", output_format="data_model").create_backend()
print(sigmaiq_backend.translate(sigma_rule))

Output:

Original Backend:
No data model specified by processing pipeline

SigmAIQ Backend:
['| tstats summariesonly=false allow_old_summaries=true fillnull_value="null" count min(_time) as firstTime max(_time) 
as lastTime from datamodel=Endpoint.Processes where Processes.process="mimikatz.exe" by Processes.process 
Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level 
Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id 
Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` 
| convert timeformat="%Y-%m-%dT%H:%M:%S" ctime(firstTime) | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime(lastTime) ']

You can specify a specific pipeline to be applied to the SigmaRule by passing it to the backend factory. Generally, you want to only apply pipelines to a backend meant for that specific backend. You can use a name of a pipeline as defined in SigmAIQPipeline.display_available_pipelines(), or pass any pySigma ProcessingPipeline object. The pipeline can be passed directory to SigmAIQPipeline, or created with SigmAIQPipeline.

from sigmaiq import SigmAIQBackend, SigmAIQPipeline

# Directly to backend
backend = SigmAIQBackend(backend="elasticsearch",
                         processing_pipeline="ecs_zeek_beats").create_backend()

# Create pipeline first, then pass to backend
pipeline = SigmAIQPipeline(processing_pipeline="ecs_zeek_beats").create_pipeline()
backend = SigmAIQBackend(backend="elasticsearch",
                         processing_pipeline=pipeline).create_backend()

The SigmAIQPipelineResolver class automates combining multiple pipelines together via pySigma's ProcessingPipelineResolver class. This results in a single ProcessingPipeline object that are applied in order of priority of each ProcessingPipeline's priority. You can pass any named available pipeline, ProcessingPipeline object, or callable that returns any valid combination of these two types:

from sigmaiq import SigmAIQPipelineResolver
from sigma.pipelines.sysmon import sysmon_pipeline
from sigma.pipelines.sentinelone import sentinelone_pipeline

# ProcessingPipeline Object
proc_pipeline_obj = sysmon_pipeline()

# Available Pipeline Name
pipeline_named = "splunk_windows"

my_pipelines = [sysmon_pipeline(),  # ProcessingPipeline type
                "splunk_windows",  # Available pipeline name
                sentinelone_pipeline  # Callable that returns a ProcessingPipeline type
                ]

my_pipeline = SigmAIQPipelineResolver(processing_pipelines=my_pipelines).process_pipelines(
    name="My New Optional Pipeline Name")

print(f"Created single new pipeline from {len(my_pipelines)} pipelines.")
print(f"New pipeline '{my_pipeline.name}' contains {len(my_pipeline.items)} ProcessingItems.")

Output:

Created single new pipeline from 3 pipelines.
New pipeline 'My New Optional Pipeline Name' contains 103 ProcessingItems.

A dictionary can be used to create a custom fieldmappings pipeline on the fly. Each key should be the original fieldname, with each value being a new fieldname or list of new fieldnames:

from sigmaiq import SigmAIQPipeline
from sigma.rule import SigmaRule

sigma_rule = SigmaRule.from_yaml(
    """
    title: Test Rule
    logsource:
        category: process_creation
        product: windows
    detection:
        sel:
            CommandLine: mimikatz.exe
        condition: sel
    """
)

custom_fieldmap = {'CommandLine': 'NewCommandLineField'}
custom_pipeline = SigmAIQPipeline.from_fieldmap(custom_fieldmap).create_pipeline()
print(f"Original Fieldname: {list(sigma_rule.detection.detections.values())[0].detection_items[0].field}")
custom_pipeline.apply(sigma_rule)
print(f"New Fieldname: {list(sigma_rule.detection.detections.values())[0].detection_items[0].field}")

Output:

Original Fieldname: CommandLine
New Fieldname: NewCommandLineField

The create_all_and_translate() method for the backend factory will automatically create backends for all possible available backends, and create queries for all possible pipelines & output formats for each backend. If show_errors=False (default), any invalid queries due to pipeline errors, such as unsupported fields, will be left out of the results dictionary:

from sigmaiq import SigmAIQBackend
from sigma.rule import SigmaRule
from pprint import pprint

sigma_rule = SigmaRule.from_yaml(
    """
    title: Test Rule
    logsource:
        category: process_creation
        product: windows
    detection:
        sel:
            CommandLine: mimikatz.exe
        condition: sel
    """
)

output = SigmAIQBackend.create_all_and_translate(sigma_rule)
pprint(output)

Output:

{backend: {pipeline: {output_format: query} } }

{'carbonblack': {'carbonblack': {'default': ['os_type:windows '
                                             'cmdline:mimikatz.exe'],
                                 'json': [{'description': None,
                                           'id': None,
                                           'query': 'os_type:windows '
                                                    'cmdline:mimikatz.exe',
                                           'title': 'Test Rule'}]},
                 'carbonblack_enterprise': {'default': ['device_os:WINDOWS '
                                                        'process_cmdline:mimikatz.exe'],
                                            'json': [{'description': None,
                                                      'id': None,
                                                      'query': 'device_os:WINDOWS '
                                                               'process_cmdline:mimikatz.exe',
                                                      'title': 'Test Rule'}]}},
 'crowdstrike_splunk': {'crowdstrike': {'default': ['event_simpleName="ProcessRollup2" '
                                                    'CommandLine="mimikatz.exe"']}},
 'crowdstrike_logscale': {'crowdstrike': {'default': ['event_simpleName="ProcessRollup2" '
                                                    'CommandLine="mimikatz.exe"']}},
 'elasticsearch': {'ecs_windows': {'default': ['process.command_line:mimikatz.exe'],
 ...

We welcome contributions to SigmAIQ! Here's how you can contribute:

1. Fork the repository
2. Create a new branch for your feature or bug fix
3. Make your changes and commit them with a clear commit message
4. Push your changes to your fork
5. Submit a pull request to the main repository

Please ensure your code adheres to the project's coding standards and includes appropriate tests.

This project is licensed under the LGPL License.

This library is currently maintained by:

• Stephen Lincoln via AttackIQ