Data Models
Data Models provide a way to configure a set of unified fields across all log types
Data Models provide a way to configure a set of unified fields across all log types.
Suppose you want to check for a particular source ip address in all events that log network traffic. These LogTypes might not only span different categories (DNS, Zeek, Apache, etc.), but also different vendors. Without a common logging standard, each of these LogTypes may represent the source ip by a different name, such as
ipAddress
, srcIP
, or ipaddr
. The more LogTypes you want to monitor, the more complex and cumbersome this simple check becomes:(event.get('ipAddress') == '127.0.0.1' or
event.get('srcIP') == '127.0.0.1' or
event.get('ipaddr') == '127.0.0.1')
If instead we define a Data Model for each of these LogTypes, we can translate the unified data model field name to the LogType field name and our logic simplifies to:
event.udm('source_ip') == '127.0.0.1'
By default, Panther comes with built-in data models for several log types, such as
AWS.S3ServerAccess
, AWS.VPCFlow
, and Okta.SystemLog
. All currently supported data models can be found here.New Data Models are added in the Panther Console or via the Panther Analysis Tool. Each log type can only have one enabled data model specified. If you want to change or update an existing data model,
disable
the existing one, and create a new, enabled one.Panther Console
Panther Analysis Tool
To create a new Data Model in the Panther Console:
- 1.Log in to your Panther Console and navigate to Build > Data Models.
- 2.In the upper right corner, click Create New.
- 3.Fill in the fields under Settings and Data Model Mappings.
- 4.In the upper right corner, click Save.
You can now access this Data Model in your rule logic with the
event.udm()
method.All files related to your custom Data Models must be stored in a folder with a name containing
data_models
(this could be a top-level data_models
directory, or sub-directories with names matching *data_models*
).
- 1.Create your Data Model specification file (e.g.
data_models/aws_cloudtrail_datamodel.yml
):AnalysisType: datamodelLogTypes:- AWS.CloudTrailDataModelID: AWS.CloudTrailFilename: aws_cloudtrail_data_model.pyEnabled: trueMappings:- Name: actor_userPath: $.userIdentity.userName- Name: event_typeMethod: get_event_type- Name: source_ipPath: sourceIPAddress- Name: user_agentPath: userAgent - 2.If any
Method
s are defined, create an associated Python file (data_models/aws_cloudtrail_datamodel.py
), as shown below. Note: The Filename specification field is required if a Method is defined in a mapping. If Method is not used in any Mappings, no Python file is required.from panther_base_helpers import deep_getdef get_event_type(event):if event.get('eventName') == 'ConsoleLogin' and deep_get(event, 'userIdentity', 'type') == 'IAMUser':if event.get('responseElements', {}).get('ConsoleLogin') == 'Failure':return "failed_login"if event.get('responseElements', {}).get('ConsoleLogin') == 'Success':return "successful_login"return None - 3.Use this Data Model in a rule:
- 1.Add the LogType under the Rule specification
LogType
field. - 2.Add the LogType to all the Rule's
Test
cases, in thep_log_type
field. - 3.Leverage the
event.udm()
method in the Rule's python logic:
AnalysisType: rule
DedupPeriodMinutes: 60
DisplayName: DataModel Example Rule
Enabled: true
Filename: my_new_rule.py
RuleID: DataModel.Example.Rule
Severity: High
LogTypes:
# Add LogTypes where this rule is applicable
# and a Data Model exists for that LogType
- AWS.CloudTrail
Tags:
- Tags
Description: >
This rule exists to validate the CLI workflows of the Panther CLI
Runbook: >
First, find out who wrote this the spec format, then notify them with feedback.
Tests:
- Name: test rule
ExpectedResult: true
# Add the LogType to the test specification in the 'p_log_type' field
Log: {
"p_log_type": "AWS.CloudTrail"
}
def rule(event):
# filter events on unified data model field
return event.udm('event_type') == 'failed_login'
def title(event):
# use unified data model field in title
return '{}: User [{}] from IP [{}] has exceeded the failed logins threshold'.format(
event.get('p_log_type'), event.udm('actor_user'),
event.udm('source_ip'))
Use your Data Model in a rule via any of the following methods:
- Add the LogType under the Rule specification
LogType
field - Add the LogType to all the Rule's
Test
cases, in thep_log_type
field - Leverage the
event.udm()
method in the Rule's python logic:def rule(event):# filter events on unified data model fieldreturn event.udm('event_type') == 'failed_login'def title(event):# use unified data model field in titlereturn '{}: User [{}] from IP [{}] has exceeded the failed logins threshold'.format(event.get('p_log_type'), event.udm('actor_user'),event.udm('source_ip'))
Rules can be updated to use unified data model field names by leveraging the
event.udm()
method. For example:def rule(event):
return event.udm('source_ip') in DMZ_NETWORK
def title(event):
return 'Suspicious request originating from ip: ' + event.udm('source_ip')
Update the rule specification to include the pertinent LogTypes:
AnalysisType: rule
Filename: example_rule.py
Description: A rule that uses datamodels
Severity: High
RuleID: Example.Rule
Enabled: true
LogTypes:
- Logtype.With.DataModel
- Another.Logtype.With.DataModel
Panther provides a built-in method on the event object called
event.udm_path
. It returns the original path that was used for the Data Model.Using
event.udm_path('destination_ip')
will return 'dstAddr'
, since this is the path defined in the Data Model for that log type.
The following example uses event.udm_path
:from panther_base_helpers import deep_get
def rule(event):
return True
def title(event):
return event.udm_path('destination_ip')
def alert_context(event):
enriched_data = deep_get(event, 'p_enrichment', 'lookup_table_name', event.udm_path('destination_ip'))
return {'enriched_data':enriched_data}
This test case was used:
{
"p_log_type": "AWS.VPCFlow",
"dstAddr": "1.1.1.1",
"p_enrichment": {
"lookup_table_name": {
"dstAddr": {
"datakey": "datavalue"
}
}
}
}
The test case returns an alert that includes Alert Context with the
datakey
and datavalue
:
A complete list of DataModel specification fields:
Field Name | Required | Description | Expected Value |
AnalysisType | Yes | Indicates whether this specification is defining a rule, policy, data model, or global | datamodel |
DataModelID | Yes | The unique identifier of the data model | String |
DisplayName | No | What name to display in the UI and alerts. The DataModelID will be displayed if this field is not set. | String |
Enabled | Yes | Whether this data model is enabled | Boolean |
FileName | No | The path (with file extension) to the python DataModel body | String |
LogTypes | Yes | What log type this policy will apply to | Singleton List of strings
Note: Although LogTypes accepts a list of strings, you can only specify 1 log type per Data Model. |
Mappings | Yes | Mapping from source field name or method to unified data model field name | List of Maps |
Mappings translate LogType fields to unified data model fields. Each mapping entry must define a unified data model field name (
Name
) and either a Path (Path
) or a method (Method
). The Path
can be a simple field name or a JSON Path. The method must be implemented in the file listed in the data model specification Filename
field.Mappings:
- Name: source_ip
Path: srcIp
- Name: user
Path: $.events[*].parameters[?(@.name == 'USER_EMAIL')].value
- Name: event_type
Method: get_event_type
The initial set of supported unified data model fields are described below.
Unified Data Model Field Name | Description |
actor_user | ID or username of the user whose action triggered the event. |
assigned_admin_role | Admin role ID or name assigned to a user in the event. |
destination_ip | Destination IP for the traffic |
destination_port | Destination port for the traffic |
event_type | Custom description for the type of event. Out of the box support for event types can be found in the global, panther_event_type_helpers.py . |
http_status | Numeric http status code for the traffic |
source_ip | Source IP for the traffic |
source_port | Source port for the traffic |
user_agent | User agent associated with the client in the event. |
user | ID or username of the user that was acted upon to trigger the event. |
Last modified 1mo ago