Data Models

Data Models provide a way to configure a set of unified fields across all log types

Overview

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'

Data Models for detections are different from the Panther Unified Data Model fields (also known as Core Fields). To learn more, see Core Fields vs. Data Models in Python detections.

Built-in Data Models

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.

How to add Data Models

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

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.

Panther Analysis Tool

Using Data Models

Using Data Models in rules

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 the p_log_type field

• Leverage the event.udm() method in the Rule's python logic:

  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'))

See examples of Data Models in Panther's Github repository.

Leveraging existing Data Models

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

Using Data Models with Enrichment

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.

AWS.VPCFlow logs example

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:

DataModel Specification Reference

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

DataModel Mappings

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

For more information about jsonpath-ng, see pypi.org's documentation here.

Unified Data Model Field Reference

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.