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Processing Pipelines ​

Processing pipelines provide granular control over how Sigma rules are converted into SIEM-specific formats. They enable field mapping, log source transformations, and other customizations needed for accurate rule conversion.

Overview ​

A processing pipeline defines an ordered sequence of transformations that are applied to a Sigma rule during conversion. Common use cases include:

  • Mapping Sigma field names to SIEM-specific field names
  • Transforming generic log sources into platform-specific ones
  • Adding conditions or modifying rule structure for specific environments

Pipelines can be defined in two ways:

  • YAML files - Used by end-users for configuration
  • Python code - Used by backend developers

This documentation focuses on YAML-based pipeline configuration.

Creating Pipelines ​

Pipelines are defined in YAML files, typically stored in a config/ or pipelines/ directory alongside your rules.

Here's an example pipeline that standardizes Windows Event ID field names:

bash
# Create the pipelines folder
mkdir pipelines

# Create the pipeline for "Windows EventID / Event Code mapping"
vim pipelines/fix_windows_event_id_mapping.yml
yaml
name: Fixing the field naming mess
priority: 30
transformations:
  - id: image_fail_path
    type: detection_item_failure
    message: Image must only contain file name without any further path components.
    field_name_conditions:
      - type: include_fields
        fields:
          - Image
    detection_item_conditions:
      - type: match_string
        cond: any
        pattern: "^\\*\\\\?[^\\\\]+$"
        negate: true
  - id: image_file_only
    type: replace_string
    regex: "^\\*\\\\([^\\\\]+)$"
    replacement: "\\1"
    field_name_conditions:
      - type: include_fields
        fields:
          - Image
  - id: field_mapping
    type: field_name_mapping
    mapping:
      EventID:
        - event_id
        - evtid
  - id: windows_field_prefix
    type: field_name_prefix
    prefix: "win."
    field_name_cond_not: true
    field_name_conditions:
      - type: processing_item_applied
        processing_item_id: field_mapping
  - id: index_condition
    type: add_condition
    conditions:
      index: windows
    rule_conditions:
      - type: logsource
        product: windows

Pipelines can be chained in the order defined by their priority. A usual processing chain is:

  1. Generic log sources are translated into specific log sources, (e.g. process creation Sigma rules into Sysmon EventID: 1).
  2. Transformation of the log signatures into the taxonomy used by a backend.
  3. Environment-specific transformations.

Pipeline Usage ​

Invoking a pipeline is done by passing the --pipeline / -p parameter to the sigma convert command, and can either be a reference to a pre-defined pipeline, or a path to the specific .yml configuration file itself.

Example: Predefined Pipelines ​

bash
sigma convert -t splunk -p sysmon rules/windows/process_creation/proc_creation_win_sysinternals_procdump.yml
splunk
EventID=1 Image IN ("*\\procdump.exe", "*\\procdump64.exe")

Priorities ​

During conversion, after all the backend and user-supplied pipelines have been gathered, priorities are used to sort the pipelines into their order-of-execution.

Some standard conventions used for these priorities are listed below.

PriorityDescription
10Log source pipelines like for Sysmon
20Pipelines provided by backend packages that should be run before the backend pipeline.
50Backend pipelines that are integrated in the backend and applied automatically.
60Backend output format pipelines that are integrated in the backend and applied automatically for the associated output format.

Tip:

Pipelines are executed from the lowest priority to the highest.

Note:

  • Pipelines with the same priority are applied in the order they were provided.
  • Pipelines without a priority are assumed to have the Priority 0.

Pipeline Variables and State ​

Pipelines can define variables that are used during processing via the vars section:

Placeholders ​

Additionally, pipelines maintain state that can be accessed and modified during processing through the set_state transformation.

Replace placeholders with predefined values:

yaml
vars:
  admin_users:
    - "Administrator"
    - "Admin"
transformations:
  - type: value_placeholders
    include:
      - "admin_users"

Query Expression Placeholders

Insert query expressions with placeholder replacement:

yaml
transformations:
  - type: query_expression_placeholders
    include:
      - "admin_users"
    expression: "[| inputlookup {id} | rename user as {field}]"

Wildcard Placeholders

Replace undefined placeholders with wildcards:

yaml
transformations:
  - type: wildcard_placeholders

Conditions ​

Conditions can be attached transformations, so that a transformation may only trigger when a given logsource is present, or only if another transformation was applied previously during the conversion process. Conditions also support a conditional logic system implemented in PySigma version v0.11.19 that perform similar to how the detection logic in a Sigma rule itself works allowing you to assign an identifier to a transformation and perform logical operations against those identifiers.

yaml
name: m365
priority: 20
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: logsource
        product: m365
        service: threat_detection
  - id: defender_index
    type: add_condition
    conditions:
      index: "microsoft_defender"
    rule_conditions:
      logsource_cond:
        type: logsource
        product: m365
      logsource_cond2:
        type: processing_item_applied
        processing_item_id: atp_index
    rule_cond_expr: logsource_cond and not logsource_cond2

Rule-based Conditions ​

Identifier
logsource
contains_detection_item
processing_item_applied
processing_state
is_sigma_rule
is_sigma_correlation_rule
rule_attribute
tag

logsource ​

Matches log source on rule. Not specified log source fields are ignored. For Correlation rules, the condition returns true if any of the associated rules have the required log source fields.

Parameters:

  • 'category': the category field in the sigma rule logsource section
  • 'product': the product field in the sigma rule logsource section
  • 'service': the service field in the sigma rule logsource section
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: logsource
        product: m365
        service: threat_detection

contains_detection_item ​

Returns True if rule contains a detection item that matches the given field name and value.

Parameters:

  • 'field': The field you'd like to match on.
  • 'value': The value you'd like to match on.
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: contains_detection_item
        field: Severity
        value: Informational

Rule-Based processing_item_applied ​

Checks if processing item was applied to rule.

Parameters:

  • 'processing_item_id': The identifier of the processing item you'd like to match on
yaml
name: transformation_demo
priority: 100
transformations:
  - id: test_id
    type: add_condition
    conditions:
      hello: world
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: processing_item_applied
        processing_item_id: test_id

Rule-Based processing_state ​

Matches on processing pipeline state.

Parameters:

  • 'key': The key for the processing state.
  • 'val': The value for the processing state key.
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: processing_state
        key: key-test
        val: val-test

is_sigma_rule ​

Checks if rule is a SigmaRule.

Parameters:

  • N/A
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: is_sigma_rule

is_sigma_correlation_rule ​

Checks if rule is a SigmaRule.

Parameters:

  • N/A
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: is_sigma_correlation_rule

rule_attribute ​

Generic match on rule attributes with supported types:

  • strings (exact matches)
  • UUIDs (exact matches)
  • numbers (relations: eq, ne, gte, ge, lte, le)
  • dates (relations: eq, ne, gte, ge, lte, le)
  • Rule severity levels (relations: eq, ne, gte, ge, lte, le)
  • Rule statuses (relations: eq, ne, gte, ge, lte, le)
  • Fields that contain lists of values, maps or other complex data structures are not supported and raise a SigmaConfigurationError. If the type of the value doesn’t allows a particular relation, the condition also raises a SigmaConfigurationError on match.

Parameters:

  • 'attribute': The attribute to match on.
  • 'value': The value to match on.
  • 'op': The relational comparison type to match on (eq (equals), ne (not equals), gte (greater than or equals), gt (greater than), lte (less than or equals), lt (less than)) with the default value of eq
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: rule_attribute
        attribute: date
        value: "2025-01-01"
        op: gte

tag ​

Matches if rule is tagged with a specific tag.

Parameters:

  • 'tag': The tag to match on.
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    rule_conditions:
      - type: tag
        tag: attack.discovery

Detection-based Conditions ​

Identifier
match_string
is_null
processing_item_applied
processing_state

match_string ​

Match string values with a regular expression ‘pattern’. The parameter ‘cond’ determines for detection items with multiple values if any or all strings must match. Generally, values which aren’t strings are skipped in any mode or result in a false result in all match mode.

Parameters:

  • 'cond': 'any' or 'all'
  • 'pattern': The pattern to match on
  • 'negate': Default to false, but can be changed to True to make a negated condition

:::code-block

yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    detection_item_conditions:
      - type: match_string
        cond: any
        pattern: informational
        negate: False

is_null ​

Match null values. The parameter ‘cond’ determines for detection items with multiple values if any or all strings must match. Generally, values which aren’t strings are skipped in any mode or result in a false result in all match mode.

Parameters:

  • 'cond': 'any' or 'all'

:::code-block

yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    detection_item_conditions:
      - type: is_null
        cond: any

Detection-Based processing_item_applied ​

Checks if processing item was applied to detection item.

Parameters:

  • 'processing_item_id': The identifier of the processing item you'd like to match on

:::code-block

yaml
name: transformation_demo
priority: 100
transformations:
  - id: test_id
    type: add_condition
    conditions:
      hello: world
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    detection_item_conditions:
      - type: processing_item_applied
        processing_item_id: test_id

Detection-Based processing_state ​

Matches on processing pipeline state.

Parameters:

  • 'key': The key for the processing state.
  • 'val': The value for the processing state key.
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    detection_item_conditions:
      - type: processing_state
        key: key-test
        val: val-test

Field-based Conditions ​

Identifier
include_fields
exclude_fields
processing_item_applied
processing_state

include_fields ​

Matches on field name if it is contained in fields list. The parameter ‘type’ determines if field names are matched as plain string (“plain”) or regular expressions (“re”).

Parameters:

  • 'fields': The fields to match on
  • 'type': Plain match or regex match using 'plain' or 're'.

:::code-block

yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: drop_detection_item
    field_name_conditions:
      - type: include_fields
        fields:
          - name
          - type

exclude_fields ​

Parameters:

  • 'fields': The fields to match on
  • 'type': Plain match or regex match using 'plain' or 're'.

:::code-block

yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: drop_detection_item
    field_name_conditions:
      - type: exclude_fields
        fields:
          - name
          - value

Field-based processing_item_applied ​

Checks if processing item was applied to detection item.

Parameters:

  • 'processing_item_id': The identifier of the processing item you'd like to match on

:::code-block

yaml
name: transformation_demo
priority: 100
transformations:
  - id: test_id
    type: add_condition
    conditions:
      hello: world
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    field_name_conditions:
      - type: processing_item_applied
        processing_item_id: test_id

Field-based processing_state ​

Matches on processing pipeline state.

Parameters:

  • 'key': The key for the processing state.
  • 'val': The value for the processing state key.
yaml
name: transformation_demo
priority: 100
transformations:
  - id: atp_index
    type: add_condition
    conditions:
      index: microsoft_atp
    field_name_conditions:
      - type: processing_state
        key: key-test
        val: val-test

Transformations ​

WIP

TODO

Field Name Mapping ​

Map a field name in the sigma rule to a field name used in your logs.

Parameters:

  • 'mapping': the fields that will be mapped (required)
yaml
name: transformation_demo
priority: 100
transformations:
  - id: useragent_mapping
    type: field_name_mapping
    mapping:
      c-useragent: useragent
      cs-host: hostname
      c-ip: ip
    rule_conditions:
      - type: logsource
        category: proxy

Field Name Prefix Mapping ​

Map a field name prefix to replace it with another prefix.

Parameters:

  • 'mapping': the fields that will be mapped (required)
yaml
name: transformation_demo
priority: 100
transformations:
  - id: integritylevel_prefix_mapping
    type: field_name_prefix_mapping
    mapping:
      win.: proc.
    rule_conditions:
      - type: logsource
        product: windows

Drop Detection Item ​

Deletes detection items. Some sort of condition is recommended but not required.

Parameters:

  • none
yaml
name: transformation_demo
priority: 100
transformations:
  # Drops the Hashes field which is specific to Sysmon logs
  - id: hashes_drop_sysmon-specific-field
    type: drop_detection_item
    field_name_conditions:
      - type: include_fields
        fields:
          - Hashes
    rule_conditions:
      - type: logsource
        product: windows
        category: process_creation

Field Name Suffix ​

Add a field name suffix. field_name_conditions are not required, but are recommended.

Parameters:

  • 'suffix': the suffix to be added (required)
yaml
name: transformation_demo
priority: 100
transformations:
  - id: windows_field_suffix
    type: field_name_suffix
    suffix: ".win"
    field_name_conditions:
      - type: include_fields
        fields:
          - Hashes

Field Name Prefix ​

Add a field name prefix.

Parameters:

  • 'prefix': the prefix to be added (required)
yaml
name: transformation_demo
priority: 100
transformations:
  - id: windows_field_prefix
    type: field_name_prefix
    prefix: "win."

Wildcard Placeholders ​

Replaces placeholders with wildcards. This transformation is useful if remaining placeholders should be replaced with something meaningful to make conversion of rules possible without defining the placeholders content.

Parameters:

  • none
yaml
name: transformation_demo
priority: 100
transformations:
  - id: wildcard_placeholders_transformation
    type: wildcard_placeholders

Value Placeholders ​

Replaces placeholders with values contained in variables defined in the configuration.

Parameters:

  • include: identify the specific placeholders you'd like to transform
yaml
name: value_placeholder_pipeline
vars:
  administrator_name:
    - "Administrator"
    - "Admin"
    - "SysAdmin"
transformations:
  - type: value_placeholders
    include:
      - "administrator_name"
yaml
title: Administrator Usage
logsource:
  product: windows
detection:
  selection:
    user|expand: "%administrator_name%"
  condition: selection
splunk
user IN ("Administrator", "Admin", "SysAdmin")

Query Expression Placeholders ​

Replaces a placeholder with a plain query containing the placeholder or an identifier mapped from the placeholder name. The main purpose is the generation of arbitrary list lookup expressions which are passed to the resulting query.

Parameters:

  • expression: string that contains query expression with {field} and {id} placeholder where placeholder identifier or a mapped identifier is inserted.
  • include:identify the specific placeholders you'd like to transform
  • mapping: Mapping between placeholders and identifiers that should be used in the expression. If no mapping is provided the placeholder name is used.
yaml
name: transformation_demo
priority: 100
transformations:
  - id: Admins_Workstations_query_expression_placeholder
    type: query_expression_placeholders
    include:
      - Admins_Workstations
    expression: "[| inputlookup {id} | rename user as {field}]"

For the rule: User with Privileges Logon

splunk
EventID IN (4672, 4964) NOT (SubjectUserSid="S-1-5-18" OR [| inputlookup Admins_Workstations | rename user as SubjectUserName])

Add Condition ​

Add and condition expression to rule conditions.

If template is set to True the condition values are interpreted as string templates and the following placeholders are replaced:

  • category, product and service: with the corresponding values of the Sigma rule log source.

Parameters:

  • 'conditions': the string to be added to replace the logsource values
yaml
name: transformation_demo
priority: 100
transformations:
  - id: index_condition
    type: add_condition
    conditions:
      index: winevent
    rule_conditions:
      - type: logsource
        product: windows

Change Logsource ​

Replace log source as defined in transformation parameters.

Parameters:

  • 'category', 'product', 'service': the log source to be changed (requires at least one)
yaml
name: transformation_demo
priority: 100
transformations:
  - id: change_logsource
    type: change_logsource
    category: security
    rule_conditions:
      - type: logsource
        category: process_creation

Replace String ​

Replace string part matched by regular expression with replacement string that can reference capture groups. It operates on the plain string representation of the SigmaString value.

This is basically an interface to re.sub() and can use all features available there.

Parameters:

  • regex: The regular expression to find the desired string
  • replacement: The replacement string to be added
yaml
name: transformation_demo
priority: 100
transformations:
  - id: image_file_only
    type: replace_string
    regex: "^\\*\\\\([^\\\\]+)$"
    replacement: "\\1"
    field_name_conditions:
      - type: include_fields
        fields:
          - Image

Set State ​

A variable that is set within the processing pipeline and can serve for different purposes:

  • as variable in templates in post processing via the pipeline object exposed to the template engine.
  • by backend like Splunk where data_model_set is used for the generated tstats queries as data model.
  • a processing condition using this state is planned.
  • the pipeline state is also used to initiate the conversion state for each rule conversion.

Parameters:

  • key: The key to modified
  • val: The values to assign to the key
yaml
name: transformation_demo
priority: 100
transformations:
  - id: set_datamodel
    type: set_state
    key: "data_model_set"
    val: "Endpoint.Processes"
  - id: custom_process_dm
    type: set_state
    key: fields
    val:
      CommandLine:
      Image:
      OriginalFilename:
splunk
| tstats summariesonly=false allow_old_summaries=true fillnull_value="null" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where IntegrityLevel="System" User IN ("*AUTHORI*", "*AUTORI*") Image IN ("*\\calc.exe", "*\\wscript.exe", "*\\cscript.exe", "*\\hh.exe", "*\\mshta.exe", "*\\forfiles.exe", "*\\ping.exe") OR CommandLine IN ("* -NoP *", "* -W Hidden *", "* -decode *", "* /decode *", "* /urlcache *", "* -urlcache *", "* -e* JAB*", "* -e* SUVYI*", "* -e* SQBFAFgA*", "* -e* aWV4I*", "* -e* IAB*", "* -e* PAA*", "* -e* aQBlAHgA*", "*vssadmin delete shadows*", "*reg SAVE HKLM*", "* -ma *", "*Microsoft\\Windows\\CurrentVersion\\Run*", "*.downloadstring(*", "*.downloadfile(*", "* /ticket:*", "*dpapi::*", "*event::clear*", "*event::drop*", "*id::modify*", "*kerberos::*", "*lsadump::*", "*misc::*", "*privilege::*", "*rpc::*", "*sekurlsa::*", "*sid::*", "*token::*", "*vault::cred*", "*vault::list*", "* p::d *", "*;iex(*", "*MiniDump*", "*net user *") by CommandLine Image OriginalFilename | `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)

In this example above, we are demonstrating how the Splunk backend will apply a data model when used with the data_model output format with a Sigma CLI command like sigma convert -p pipeline.yml -t splunk -f data_model rule.yml. This will convert a query to use datamodels and form a tstats query such at this:

Rule Failure ​

Raise a SigmaTransformationError with the provided message. This enables transformation pipelines to signalize that a certain situation can't be handled, e.g. only a subset of values is allowed because the target data model doesn't offers all possibilities. A transformation condition is not required, but is recommended.

Parameters:

  • message: the message to present when the transformation failure state is met
yaml
name: transformation_demo
priority: 100
transformations:
  - id: cs_drop_unsupported_logsource_sysmon_status
    type: rule_failure
    message: CrowdStrike logs do not support sysmon_status logs at this time.
    rule_conditions:
      - type: logsource
        product: windows
        category: sysmon_status

Detection Item Failure ​

Raise a SigmaTransformationError with the provided message. This enables transformation pipelines to signalize that a certain situation can't be handled, e.g. only a subset of values is allowed because the target data model doesn't offers all possibilities. A transformation condition is not required, but is recommended.

Parameters:

  • message: the message to present when the transformation failure state is met
yaml
name: transformation_demo
priority: 100
transformations:
  - id: cs_drop_eventid
    type: detection_item_failure
    message: CrowdStrike logs do not support the field EventID at this time.
    field_name_conditions:
      - type: include_fields
        fields:
          - EventID
    rule_conditions:
      - type: logsource
        product: windows

Detection Item Condition ​

A rule condition that matches if a detection item within the rule contains the specified field name and value.

yaml
name: transformation_demo
priority: 100
transformations:
  - id: service_control_manager_drop
    type: drop_detection_item
    field_name_conditions:
      - type: include_fields
        fields:
          - EventID
          - Provider_Name
    rule_conditions:
      - type: logsource
        service: system
        product: windows
      - type: contains_detection_item
        field: "Provider_Name"
        value: "Service Control Manager"

Finalizers ​

Finalizers process the final query output after all transformations are complete. Common finalizers include:

Concatenate Queries Join multiple queries with a separator:

yaml
finalizers:
  - type: concat
    separator: " OR "
    prefix: "("
    suffix: ")"

JSON Output Convert queries to JSON:

yaml
finalizers:
  - type: json
    indent: 2

Template Output Apply a Jinja2 template:

yaml
finalizers:
  - type: template
    template: |
      {
        "query": {{ query | tojson }},
        "rule": {{ rule.title | tojson }}
      }

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