SigmAIQ 0.3.0

Wrapper and tools for pySigma and Sigma rules

SigmAIQ: pySigma Wrapper & Utils

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 will contain pySigma related tools and script updates coming in the near future, including easy Sigma rule searching, LLM support, an automatic rule creation from IOCs.

This library is currently maintained by:

• Stephen Lincoln via AttackIQ

Disclaimer

This library is currently in pre-release status; therefore, it is a constant work-in-progress and bugs may be encountered. Please report any issues here. Feature requests are also always welcome! pySigma tools/utils are currently not in the pre-release version, and will be added in future releases.

LLM Support

For LLM usage, see the LLM README

Installation & Usage

Installation

SigmAIQ can be installed with your favorite package manager:

pip install sigmaiq
pipenv install sigmaiq
poetry add sigmaiq

Usage Quickstart

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.

Usage Examples

Backends

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"']

Specifying Output Formats

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) ']

Pipelines

Specifying Pipelines

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()

Combining Multiple Pipelines

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.

Custom Fieldmappings

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

All-In-One Conversion

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"']}},
 'elasticsearch': {'ecs_windows': {'default': ['process.command_line:mimikatz.exe'],
 ...

Supported Options

Backends

Available Backends

Backend Option	Description	Associated Pipelines	Default Pipeline
carbonblack	Carbon Black EDR	carbonblack carbonblack_enterprise	carbonblack
cortexxdr	Palo Alto Cortex XDR	cortexxdr	cortexxdr
crowdstrike_splunk	Crowdstrike Splunk Query	crowdstrike	crowdstrike
elasticsearch	Elastic Elasticsearch SIEM	ecs_windows ecs_windows_old ecs_zeek_beats ecs_zeek_corelight zeek_raw	ecs_windows
insightidr	Rapid7 InsightIDR SIEM	insightidr	insightidr
loki	Grafana Loki LogQL SIEM	loki_grafana_logfmt loki_promtail_sysmon loki_okta_system_log	loki_grafana_logfmt
microsoft365defender	Microsoft 365 Defender Advanced Hunting Query (KQL)	microsoft365defender	microsoft365defender
opensearch	OpenSearch Lucene	ecs_windows ecs_windows_old ecs_zeek_beats ecs_zeek_corelight zeek_raw	ecs_windows
qradar	IBM QRadar	qradar_fields qradar_payload	qradar_fields
sentinelone	SentinelOne EDR	sentinelone	sentinelone
splunk	Splunk SIEM	splunk_windows splunk_wineventlog splunk_windows_sysmon_acc splunk_cim_dm	splunk_windows
sigma	Original YAML/JSON Sigma Rule Output	sigma_default	sigma_default
stix	STIX 2.0 & STIX Shifter Queries	stix_2_0 stix_shifter	stix_2_0

Backend Output Formats

Backend Option	Output Format Option	Description
carbonblack	default	Plain CarbonBlack queries
	json	CarbonBlack JSON query
cortexxdr	default	Plain CortexXDR XQL queries
	json	JSON Formatted CortexXDR XQL query
crowdstrike_splunk	default	Plain SPL queries
elasticsearch	default	Plain Elasticsearch Lucene queries
	kibana_ndjson	Kibana NDJSON import file with Lucene queries
	dsl_lucene	Elasticsearch query DSL with embedded Lucene queries
	siem_rule	Elasticsearch query DSL as SIEM Rules in JSON Format
	siem_rule_ndjson	Elasticsearch query DSL as SIEM Rules in NDJSON Format
insightidr	default	Simple log search query mode
	leql_advanced_search	Advanced Log Entry Query Language (LEQL) queries
	leql_detection_definition	LEQL format roughly matching the 'Rule Logic' tab in ABA detection rule definition
loki	default	Plain Loki queries
	ruler	Loki 'ruler' output format for generating alerts
microsoft365defender	default	KQL for Microsoft 365 Defender Advanced Hunting queries
opensearch	default	Plain OpenSearch Lucene queries
	dashboards_ndjson	OpenSearch Dashboards NDJSON import file with Lucene queries
	monitor_rule	OpenSearch monitor rule with embedded Lucene query
	dsl_lucene	OpenSearch query DSL with embedded Lucene queries
qradar	default	Plain QRadar queries
sentinelone	default	Plaintext
	json	JSON format
splunk	default	Plain SPL queries
	savedsearches	Plain SPL in a savedsearches.conf file
	data_model	Data model queries with tstats
	stanza	Enterprise Security savedsearches.conf stanza
sigma	default	Default output format
	yaml	Default Sigma Rule output format
	json	JSON style Sigma Rule Output
stix	default	Plain stix queries

Pipelines

Available Named Pipelines

Pipeline Option	Description
splunk_wineventlog	SigmAIQ Custom, combined windows_audit and splunk_windows pipelines to convert Sysmon fields to Windows Event Log fields for Splunk searches
carbonblack	Uses Carbon Black EDR field mappings
carbonblack_enterprise	Uses Carbon Black Enterprise EDR field mappings
cortexxdr	Cortex XDR field mappings
crowdstrike	Crowdstrike FDR Splunk Mappings
ecs_windows	ECS mapping for Windows event logs ingested with Winlogbeat
ecs_windows_old	ECS mapping for Windows event logs ingested with Winlogbeat <= 6.x
ecs_zeek_beats	Zeek ECS mapping from Elastic
ecs_zeek_corelight	Zeek ECS mapping from Corelight
zeek_raw	Zeek raw JSON log field naming
insightidr	InsightIDR Log Entry Query Language (LEQL) Transformations
loki_grafana_logfmt	Converts field names to logfmt labels used by Grafana
loki_promtail_sysmon	Parse and adjust field names for Windows sysmon data produced by promtail
loki_okta_system_log	Parse the Okta System Log event json, adjusting field-names appropriately
microsoft365defender	Mappings for Sysmon -> Advanced Hunting Query Table Schema
qradar_fields	Supports only the Sigma fields in the Field Mapping
qradar_payload	Uses UTF8(payload) instead of fields unsupported by the Field Mapping.
sigma_default	Empty ProcessingPipeline placeholder
sentinelone	Mappings for SentinelOne Deep Visibility Queries
splunk_windows	Splunk Query, Windows Mappings
splunk_windows_sysmon_acc	Splunk Query, Sysmon Mappings
splunk_cim_dm	Splunk Datamodel Field Mappings
stix_2_0	STIX 2.0 Mappings
stix_shifter	STIX Shifter Mappings
windows_sysmon	Sysmon for Windows
windows_audit	Windows Event Logs
windows_logsource	Windows Logs, General

TODO

• readthedocs docs
• Add tooling for LLM, search, and IOC Rule Creation
• Clean up codebase