Writing Python Detections

Construct Python detections in the Console or CLI workflow

Overview

You can write your own Python detections in the Panther Console or locally, following the CLI workflow. When writing Python detections, try to follow these best practices, and remember that certain alert fields can be set dynamically. Rules written in Python can be used in detection derivation.

You can alternatively use the no-code detection builder in the Console to create rules, or write them locally as Simple Detections. If you aren't sure whether to write detections locally as Simple Detections or Python detections, see the Using Python vs. Simple Detections YAML section.

Before you write a new Python detection, see if there's a Panther-managed detection that meets your needs (or almost meets your needs—Panther-managed rules can be tuned with Inline Filters). Leveraging a Panther-managed detection not only saves you from the effort of writing one yourself, but also provides the ongoing benefit of continuous updates to core detection logic, as Panther releases new versions.

How to create detections in Python

How to create a rule in Python

You can write a Python rule in both the Panther Console and CLI workflow.

Creating a rule in Python in the Console
  1. In the left-hand navigation bar of your Panther Console, click Build > Detections.

  2. Click Create New.

  3. In the Select Detection Type modal, choose Rule.

  4. On the create page, configure your rule:

    • Name: Enter a descriptive name for the rule.

    • ID (optional): Click the pen icon and enter a unique ID for your rule.

    • In the upper-right corner, the Enabled toggle will be set to ON by default. If you'd like to disable the rule, flip the toggle to OFF.

    • In the For the Following Source section:

      • Log Types: Select the log types this rule should apply to.

    • In the Detect section:

    • In the Create Alert section, set the Create Alert ON/OFF toggle. This indicates whether an alert should be created when there are matches, or only a Signal. If you set this toggle to ON:

      • Severity: Select a severity level for the alerts triggered by this detection.

      • In the Optional Fields section, optionally provide values for the following fields:

        • Description: Enter additional context about the rule.

        • Runbook: Enter the procedures and operations relating to this rule.

        • Reference: Enter an external link to more information relating to this rule.

        • Destination Overrides: Choose destinations to receive alerts for this detection, regardless of severity. Note that destinations can also be set dynamically, in the rule function. See Routing Order Precedence to learn more about routing precedence.

        • Deduplication Period and Events Threshold: Enter the deduplication period and threshold for rule matches. To learn how deduplication works, see Deduplication.

        • Summary Attributes: Enter the attributes you want to showcase in the alerts that are triggered by this detection.

        • Custom Tags: Enter custom tags to help you understand the rule at a glance (e.g., HIPAA.)

        • In the Framework Mapping section:

          1. Click Add New to enter a report.

          2. Provide values for the following fields:

            • Report Key: Enter a key relevant to your report.

            • Report Values: Enter values for that report.

    • In the Test section:

      • In the Unit Test section, click Add New to create a test for the rule you defined in the previous step.

  5. In the upper-right corner, click Save.

After you have created a rule, you can modify it using Inline Filters.

Creating a rule in Python in the CLI workflow

If you're writing detections locally (instead of in the Panther Console), we recommend managing your local detection files in a version control system like GitHub or GitLab.

We advise that you start your custom detection content by creating either a public fork or a private cloned repo from Panther's open-source panther-analysis repository.

Folder setup

If you group your rules into folders, each folder name must contain rules in order for them to be found during upload (using either PAT or the bulk uploader in the Console).

We recommend grouping rules into folders based on log/resource type, e.g., suricata_rules or aws_s3_policies. You can use the panther-analysis repo as a reference.

File setup

Each rule and scheduled rule consists of:

  • A Python file (a file with a .py extension) containing your detection logic.

  • A YAML specification file (a file with a .yml extension) containing metadata attributes of the detection.

    • By convention, we give this file the same name as the Python file.

Rules are Python functions to detect suspicious behaviors. Returning a value of True indicates suspicious activity, which triggers an alert.

  1. Write your rule and save it (in your folder of choice) as my_new_rule.py:

    def rule(event):  
      return 'prod' in event.get('hostName')
  2. Create a metadata file using the template below:

    AnalysisType: rule
    DedupPeriodMinutes: 60 # 1 hour
    DisplayName: Example Rule to Check the Format of the Spec
    Enabled: true
    Filename: my_new_rule.py
    RuleID: Type.Behavior.MoreContext
    Severity: High
    LogTypes:
      - LogType.GoesHere
    Reports:
      ReportName (like CIS, MITRE ATT&CK):
        - The specific report section relevant to this rule
    Tags:
      - Tags
      - Go
      - Here
    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.
    Reference: https://www.a-clickable-link-to-more-info.com

When this rule is uploaded, each of the fields you would normally populate in the Panther Console will be auto-filled. See Rule specification reference for a complete list of required and optional fields.

How to create a scheduled rule in Python

You can write a Python scheduled rule in both the Panther Console and CLI workflow.

Creating a scheduled rule in Python in the Console
  1. In the left-hand navigation bar of your Panther Console, click Build > Detections.

  2. Click Create New.

  3. In the Select Detection Type modal, choose Scheduled Rule.

  4. On the create page, configure your scheduled rule:

    • Name: Enter a descriptive name for the scheduled rule.

    • ID (optional): Click the pen icon and enter a unique ID for your scheduled rule.

    • In the upper-right corner, the Enabled toggle will be set to ON by default. If you'd like to disable the scheduled rule, flip the toggle to OFF.

    • In the For the Following Scheduled Queries section:

      • Scheduled Queries: Select one or more Scheduled Searches this scheduled rule should apply to.

    • In the Detect section:

      • In the Rule Function text editor, write a Python rule function to define your detection.

        • If all your filtering logic is already taken care of in the SQL of the associated scheduled query, you can configure the rule function to simply return true for each row:

          def rule(event):  
              return True
        • For detection templates and examples, see the panther_analysis GitHub repository

    • In the Create Alert section, set the Create Alert ON/OFF toggle. This indicates whether an alert should be created when there are matches, or only a Signal. If you set this toggle to ON:

      • Severity: Select a severity level for the alerts triggered by this detection.

      • In the Optional Fields section, optionally provide values for the following fields:

        • Description: Enter additional context about the rule.

        • Runbook: Enter the procedures and operations relating to this rule.

        • Reference: Enter an external link to more information relating to this rule.

        • Destination Overrides: Choose destinations to receive alerts for this detection, regardless of severity. Note that destinations can also be set dynamically, in the rule function. See Routing Order Precedence to learn more about routing precedence.

        • Deduplication Period and Events Threshold: Enter the deduplication period and threshold for rule matches. To learn how deduplication works, see Deduplication.

        • Summary Attributes: Enter the attributes you want to showcase in the alerts that are triggered by this detection.

        • Custom Tags: Enter custom tags to help you understand the rule at a glance (e.g., HIPAA.)

        • In the Framework Mapping section:

          1. Click Add New to enter a report.

          2. Provide values for the following fields:

            • Report Key: Enter a key relevant to your report.

            • Report Values: Enter values for that report.

    • In the Test section:

      • In the Unit Test section, click Add New to create a test for the rule you defined in the previous step.

  5. In the upper-right corner, click Save.

    • Once you've clicked Save, the scheduled rule will become active. The SQL returned from the associated scheduled query (at the interval defined in the query) will be run through the scheduled rule (if, that is, any rows are returned).

After you have created a rule, you can modify it using Inline Filters.

Creating a scheduled rule in Python in the CLI workflow

If you're writing detections locally (instead of in the Panther Console), we recommend managing your local detection files in a version control system like GitHub or GitLab.

We advise that you start your custom detection content by creating either a public fork or a private cloned repo from Panther's open-source panther-analysis repository.

Folder setup

If you group your rules into folders, each folder name must contain the string rules in order for them to be found during upload (using either PAT or the bulk uploader in the Console).

We recommend grouping rules into folders based on log/resource type, e.g., suricata_rules or aws_s3_policies. You can use the panther-analysis repo as a reference.

File setup

Each scheduled rule consists of:

  • A Python file (a file with a .py extension) containing your detection logic.

  • A YAML specification file (a file with a .yml extension) containing metadata attributes of the detection.

    • By convention, we give this file the same name as the Python file.

Scheduled rules allow you to analyze the output of a scheduled search with Python. Returning a value of True indicates suspicious activity, which triggers an alert.

  1. Write your query and save it as my_new_scheduled_query.yml:

    AnalysisType: scheduled_query
    QueryName: My New Scheduled Query Name
    Enabled: true
    Tags:
      - Optional
      - Tags
    Description: >
      An optional Description
    Query: 'SELECT * FROM panther_logs.aws_cloudtrail LIMIT 10'
    SnowflakeQuery: 'SELECT * FROM panther_logs.public.aws_cloudtrail LIMIT 10'
    AthenaQuery: 'SELECT * FROM panther_logs.aws_cloudtrail LIMIT 10'
    Schedule:
      # Note: CronExpression and RateMinutes are mutually exclusive, only
      # configure one or the other
      CronExpression: '0 * * * *'
      RateMinutes: 1
      TimeoutMinutes: 1
  2. Write your rule and save it as my_new_rule.py:

    # Note: See an example rule for more options
    # https://github.com/panther-labs/panther-analysis/blob/master/templates/example_rule.py
    
    def rule(_):
        # Note: You may add additional logic here
        return True
  3. Create a metadata file and save it as my_new_schedule_rule.yml:

    AnalysisType: scheduled_rule
    Filename: my_new_rule.py 
    RuleID: My.New.Rule
    DisplayName: A More Friendly Name
    Enabled: true
    ScheduledQueries:
      - My New Scheduled Query Name
    Tags:
      - Tag
    Severity: Medium
    Description: >
      An optional Description
    Runbook: >
      An optional Runbook 
    Reference: An optional reference.link 
    Tests:
      -
        Name: Name 
        ExpectedResult: true
        Log:
          {
            "JSON": "string"
          }

When this scheduled rule is uploaded, each of the files will connect a scheduled query with a rule, and fill in the fields you would normally populate in the Panther Console will be auto-filled. See Rule specification reference below for a complete list of required and optional fields.

How to create a policy in Python

Python detection syntax

A local Python detection is made up of two files: a Python file and a YAML file. When a Python detection is created in the Panther Console, there is only a Python text editor (not a YAML one). The keys listed in the YAML column, below, are set in fields in the user interface.

The Python file can contain:The YAML file can contain:
  • Detection logic

    def rule(event): # or def policy(resource):
  • Alert functions (dynamic)

    def severity(event):
    def title(event):
    def dedup(event):
    def destinations(event):
    def runbook(event):
    def reference(event):
    def description(event):
    def alert_context(event):

  • Filter key

    InlineFilters: 
  • Metadata keys

    AnalysisType: # rule, scheduled_rule, or policy
    Enabled: 
    FileName: 
    RuleID: # or PolicyId:
    LogTypes: 
    Reports: 
    Tags: 
    Tests: 
    ScheduledQueries: # only applicable to scheduled rules
    Suppressions: # only applicable to policies
    CreateAlert: # not applicable to policies
  • Alert keys (static)

    Severity:
    Description:
    DedupPeriodMinutes:
    Threshold: 
    DisplayName:
    OutputIds:
    Reference:
    Runbook:
    SummaryAttributes: 

Basic Python rule structure

Only a rule() function and the YAML keys shown below are required for a Python rule. Additional Python alert functions, however, can make your alerts more dynamic. Additional YAML keys are available, too—see Python rule specification reference.

rule.pyrule.yml

def rule(event): 
    if event.get("Something"): 
        return True 
    return False

AnalysisType: rule
Enabled: true
Filename: rule.py
RuleID: my.rule
LogTypes: 
    - Some.Schema
Severity: INFO

For more templates, see the panther-analysis repo on GitHub.

InlineFilters

Learn more about using InlineFilters in Python rules on Modifying Detections with Inline Filters.

Alert functions in Python detections

Panther's detection auxiliary functions are Python functions that control analysis logic, generated alert title, event grouping, routing of alerts, and metadata overrides. Rules are customizable and can import from standard Python libraries or global helpers.

Applicable to both rules and policies, each function below takes a single argument of event (rules) or resource (policies). Advanced users may define functions, variables, or classes outside of the functions defined below.

If you are using alert deduplication, the first event to match the detection is used as a parameter for these alert functions.

Each of the below alert functions are optional, but can add dynamic context to your alerts.

Detection alert function nameDescriptionOverridesReturn Value

The level of urgency of the alert

In YAML: Severity key


In Console: Severity field

INFO, LOW, MEDIUM, HIGH, or CRITICAL

The generated alert title

In YAML: DisplayName > RuleID or PolicyID


In Console: Name field > ID field

String

The string to group related events with, limited to 1000 characters

In Python/YAML: title() > DisplayName > RuleID or PolicyID


In Console: title() > Name field > ID field

String

Additional context to pass to the alert destination(s)

Does not override a YAML nor Console field

Dict[String: Any]

An explanation about why the rule exists

In YAML: Description key


In Console: Description field

String

A reference URL to an internal document or online resource about the rule

In YAML: Reference key


In Console: Reference field

String

A list of instructions to follow once the alert is generated

In YAML: Runbook key


In Console: Runbook field

String

The label or ID of the destinations to specifically send alerts to. An empty list will suppress all alerts.

In YAML: OutputIds key


In Console: Destination Overrides field

List[Destination Name/ID]

severity

In some scenarios, you may need to upgrade or downgrade the severity level of an alert. The severity levels of an alert can be mapped to INFO, LOW, MEDIUM, HIGH, CRITICAL, or DEFAULT. Return DEFAULT to fall back to the statically defined rule severity.

In all cases, the severity string returned is case insensitive, meaning you can return, for example, Critical or default, depending on your style preferences.

Example where a HIGH severity alert is returned if an API token is created - otherwise we create an INFO level alert:

def severity(event):
    if event.get('eventType') == 'system.api_token.create':
        return "HIGH"
    return "INFO"

Reference: Template Rule

Example using DEFAULT:

def severity(event):
    if event.get('eventType') == 'system.api_token.create':
        return "HIGH"
    return "DEFAULT"

title

The title function is optional, but it is recommended to include it to provide additional context. In the example below, the log type, relevant username, and a static string are returned to the destination. The function checks to see if the event is related the AWS.CloudTrail log type and return the AWS Account Name if that is true. Learn more about how an alert title is set on Rules and Scheduled Rules.

Example:

def title(event):
    # use unified data model field in title
    log_type = event.get("p_log_type")
    title_str = (
        f"{log_type}: User [{event.udm('actor_user')}] has exceeded the failed logins threshold"
    )
    if log_type == "AWS.CloudTrail":
        title_str += f" in [{lookup_aws_account_name(event.get('recipientAccountId'))}]"
    return title_str

Reference: Template Rule

dedup

Deduplication is the process of grouping related events into a single alert to prevent receiving duplicate alerts. Events triggering the same detection that also share a deduplication string, within the deduplication period, are grouped together in a single alert. The dedup function is one way to define a deduplication string. It is limited to 1000 characters. Learn more about deduplication on Rules and Scheduled Rules.

Example:

def dedup(event):
	user_identity = event.get("userIdentity", {})

	if user_identity.get("type") == "AssumedRole":
		return helper_strip_role_session_id(user_identity.get("arn", ""))

	return user_identity.get("arn")

Reference: AWS S3 Bucket Deleted Rule

destinations

By default, Alerts are sent to specific destinations based on severity level or log type event. Each Detection has the ability to override their default destination and send the Alert to one or more specific destination(s). In some scenarios, a destination override is required, providing more advance criteria based on the logic of the Rule.

Example:

A rule used for multiple log types utilizes the destinations function to reroute the Alert to another destination if the log type is "AWS.CloudTrail". The Alert is suppressed to this destination using return ["SKIP"] if the log type is not CloudTrail.

def destinations(event):
    if event.get("p_log_type") == "AWS.CloudTrail":
        return ["slack-security-alerts"] ### Name or UUID of destination
    # Do not send alert to an external destination
    return ["SKIP"]

Reference: Template Rule

alert_context

This function allows the detection to pass any event details as additional context, such as usernames, IP addresses, or success/failure, to the Alert destination(s).

Example:

The code below returns all event data in the alert context.

def rule(event):
    return (
        event.get("actionName") == "UPDATE_SAML_SETTINGS"
        and event.get("actionResult") == "SUCCEEDED"
    )

def alert_context(event):
    return {
        "user": event.udm("actor_user"),
        "ip": event.udm("source_ip")
    }

runbook, reference, and description

These functions can provide additional context around why an alert was triggered and how to resolve the related issue. Depending on what conditions are met, a string can be overridden and returned to the specified field in the alert.

The example below dynamically provides a link within the runbook field in an alert.

def runbook(event):
	log_type = event.get("p_log_type")
	if log_type == "OnePassword.SignInAttempt":
		return: f"<https://link/to/resource>"
	elif log_type == "Okta.SystemLog":
		return: f"<https://link/to/resource/2>"
	else: 
		return: f"<https://default/link>"

Event object functions

In a Python detection, the rule() function and all dynamic alert functions take in a single argument: the event object. This event object has built-in functions to enable simple extraction of event values.

get()

Function signature
def get(self, key, default=None) -> Any:

Use get() to access a top-level event field. You can provide a default value that will be returned if the key is not found.

It is also possible to access a top-level field using deep_get() and deep_walk(). Learn more about accessing top-level fields safely below.

Example:

Example event
{
  "key": "value"
}
Using get()
def rule(event):
    return event.get("key") == "value"

# The above would return true

deep_get()

Function signature
def deep_get(self, *keys: str, default: Any = None) -> Any:

Use deep_get() to return keys that are nested within Python dictionaries.

If the value you need to retrieve lives within a list, use deep_walk() instead.

This function is also represented as a global helper, but for convenience it is recommended to use this event object function.

Example:

Given an event with the following structure

Example event
{
  "object": {
    "nested": {
       "key": "here"
      }
   }
 }
Using deep_get()
def rule(event):
    return event.deep_get("object", "nested", "key") == "here"
    
# The above would return true

deep_walk()

Function signature
def deep_walk(
        self, *keys: str, default: Optional[str] = None, return_val: str = "all"
    ) -> Union[Optional[Any], Optional[List[Any]]]:

Use deep_walk() to return values associated with keys that are deeply nested in Python dictionaries, which may contain any number of dictionaries or lists. If it matches multiple event fields, an array of matches will be returned; if only one match is made, the value of that match will be returned.

This function is also represented as a global helper, but for convenience it is recommended to use this event object function.

Example:

Example event
{
  "object": {
    "nested": {
       "list": [
          {
             "key": "first"
          },
          {
             "key": "second"
          }
         ]
      }
   }
 }
Using deep_walk()
def rule(event):
    return "first" in event.deep_walk("object", "nested", "list", "key", default=[])

# The above would return true

udm()

Function signature
def udm(self, key: str) -> Any:

The udm() function is primarily intended to allow you to access Data Model and Core Field (also known as p_udm field) values, but can also be used to access event fields.

Here is how the udm() function works:

  1. The function first checks to see if there is a Data Model key mapping defined for the value passed in to udm(). If so, the value of the Data Model is returned.

    • If a Data Model key is defined for the value passed in to udm(), the function returns its value and does not move on to steps 2 or 3, below. This is true even if the event being evaluated does not contain the key path defined in the Data Model mapping—in this case, null is returned.

  2. If there is no Data Model key defined for the value passed into udm(), the function then checks to see if there is Core Field value in the event's p_udm struct with that name. If so, the Core Field value is returned.

    • In order for udm() to not move on to Step 3, below, the event being evaluated must contain the value passed in to udm() as a key within its p_udm struct. If a Core Field has simply been defined with the passed-in value as a key, but the event does not contain the mapping's associated field path, the event's p_udm struct will not contain the Core Field, and udm() will move on to Step 3, below.

  3. If there is no Data Model defined or Core Field present for the value passed into udm(), the function then checks whether there is an event field with that name. If so, its value is returned.

    • In this case, udm() checks all event fields, even nested ones. Its behavior is analogous to deep_get().

The behavior outlined above means it is only possible to use udm() to access a Core Field value if there is not also a Data Model mapping defined with the same key. Further, it is only possible to use udm() to access an event field if there is not also a Data Model mapping defined nor a Core Field present with the same key.

Example using udm() to access a Data Model value:

Data Model example
Mappings:
  - Name: source_ip
    Path: nested.srcIp
Example event
{
  "nested": {
    "srcIp": "127.0.0.1"
  }
}
Using udm()
def rule(event):
    return event.udm("source_ip") == "127.0.0.1"

# The above would return true

Python best practices

Python Enhancement Proposals publishes resources on how to cleanly and effectively write and style your Python code. For example, you can use autopep8 to automatically ensure that your written detections all follow a consistent style.

Available Python libraries

The following Python libraries are available to be used in Panther in addition to boto3, provided by AWS Lambda:

Package

Version

Description

License

jsonpath-ng

1.5.2

JSONPath Implementation

Apache v2

policyuniverse

1.3.3.20210223

Parse AWS ARNs and Policies

Apache v2

requests

2.23.0

Easy HTTP Requests

Apache v2

Python detection writing best practices

Writing tests for your detections

Before enabling new detections, it is recommended to write tests that define scenarios where alerts should or should not be generated. Best practice dictates at least one positive and one negative to ensure the most reliability.

Casing for event fields

Lookups for event fields are not case sensitive. event.get("Event_Type") or event.get("event_type") will return the same result.

Understanding top level fields and nested fields

Top-level fields represent the parent fields in a nested data structure. For example, a record may contain a field called user under which there are other fields such as ip_address. In this case, user is the top-level field, and ip_address is a nested field underneath it.

Nesting can occur many layers deep, and so it is valuable to understand the schema structure and know how to access a given field for a detection.

Accessing top-level fields safely

Basic Rules match a field’s value in the event, and a best practice to avoid errors is to leverage Python’s built-in get() function.

The example below is a best practice because it leverages a get() function. get() will look for a field, and if the field doesn't exist, it will return None instead of an error, which will result in the detection returning False.

def rule(event):
    return event.get('field') == 'value'

In the example below, if the field exists, the value of the field will be returned. Otherwise, False will be returned:

def rule(event):
    if event.get('field')
        return event.get('field')
    return False

Bad practice example The rule definition below is bad practice because the code is explicit about the field name. If the field doesn't exist, Python will throw a KeyError:

def rule(event):
    return event['field'] == 'value'

Using Global Helper functions

Once many detections are written, a set of patterns and repeated code will begin to emerge. This is a great use case for Global Helper functions, which provide a centralized location for this logic to exist across all detections.

Accessing nested fields safely

If you'd like to access a filed nested deeply within an event, use the deep_get() and deep_walk() functions available on the event object. These functions are also represented as Global Helper functions, but for convenience, it's recommended to use the event object version instead.

Example:

AWS CloudTrail logs nest the type of user accessing the console underneath userIdentity. Here is a snippet of a JSON CloudTrail root activity log:

{ 	
       "eventVersion": "1.05",
       "userIdentity": { 	
               "type": "Root", 	
               "principalId": "1111", 	
               "arn": "arn:aws:iam::123456789012:root", 	
               "accountId": "123456789012", 		
               "userName": "root" 
               }, 	
        ... 
 }

See how to check the value of type safely using both forms of deep_get():

Checking the event value using the event object deep_get() function:

def rule(event):
    return event.deep_get("userIdentity", "type") == "Root"

Checking fields for specific values

You may want to know when a specific event has occurred. If it did occur, then the detection should trigger an alert. Since Panther stores everything as normalized JSON, you can check the value of a field against the criteria you specify.

For example, to detect the action of granting Box technical support access to your Box account, the Python below would be used to match events where the event_type equals ACCESS_GRANTED:

def rule(event):
    return event.get("event_type") == "ACCESS_GRANTED"

If the field is event_type and the value is equal to ACCESS_GRANTED then the rule function will return true and an Alert will be created.

Checking fields for Integer values

You may need to compare the value of a field against integers. This allows you to use any of Python’s built-in comparisons against your events.

For example, you can create an alert based on HTTP response status codes:

# returns True if 'status_code' equals 404
def rule(event):
    if event.get("status_code"):
        return event.get("status_code") == 404
    else:
        return False

# returns True if 'status_code' greater than 400
def rule(event):
    if event.get("status_code"):
        return event.get("status_code") > 404
    else:
        return False

Reference:

Using the Universal Data Model

Data Models provide a way to configure a set of unified fields across all log types. By default, Panther comes with built-in Data Models for several log types. Custom Data Models can be added in the Panther Console or via the Panther Analysis Tool.

event.udm() can only be used with log types that have an existing Data Model in your Panther environment.

Example:

import panther_event_type_helpers as event_type

def rule(event):
    # filter events on unified data model field ‘event_type’
    return event.udm("event_type") == event_type.FAILED_LOGIN

References:

Using multiple conditions

The and keyword is a logical operator and is used to combine conditional statements. It is often required to match multiple fields in an event using the and keyword. When using and, all statements must be true: "string_a" == "this"and"string_b" == "that"

Example:

To track down successful root user access to the AWS console you need to look at several fields:

from panther_base_helpers import deep_get

def rule(event):
    return (event.get("eventName") == "ConsoleLogin" and
            deep_get(event, "userIdentity", "type") == "Root" and
	    deep_get(event, "responseElements", "ConsoleLogin") == "Success")

The or keyword is a logical operator and is used to combine conditional statements. When using or, either of the statements may be true: "string_a" == "this" or "string_b" == "that"

Example:

This example detects if the field contains either Port 80 or Port 22:

# returns True if 'port_number' is 80 or 22
def rule(event):
    return event.get("port_number") == 80 or event.get("port_number") == 22

Searching values in lists

Comparing and matching events against a list of IP addresses, domains, users etc. is very quick and easy in Python. This is often used in conjunction with choosing not to alert on an event if the field being checked also exists in the list. This helps with reducing false positives for known behavior in your environment.

Example: If you have a list of IP addresses that you would like to add to your allow list, but you want to know if an IP address comes through outside of that list, we recommend using a Python set. Sets are similar to Python lists and tuples, but are more memory efficient.

# Set - Recommended over tuples or lists for performance
ALLOW_IP = {'192.0.0.1', '192.0.0.2', '192.0.0.3'}

def rule(event):
    return event.get("ip_address") not in ALLOW_IP

In the example below, we use the Panther helper pattern_match_list:

from panther_base_helpers import pattern_match_list

USER_CREATE_PATTERNS = [
    "chage",  # user password expiry
    "passwd",  # change passwords for users
    "user*",  # create, modify, and delete users
]


def rule(event):
    # Filter the events
    if event.get("event") != "session.command":
        return False
    # Check that the program matches our list above
    return pattern_match_list(event.get("program", ""), USER_CREATE_PATTERNS)

Reference: Teleport Create User Accounts

Matching events with regex

If you want to match against events using regular expressions - to match subdomains, file paths, or a prefix/suffix of a general string - you can use regex. In Python, regex can be used by importing the re library and looking for a matching value.

In the example below, the regex pattern will match Administrator or administrator against the nested value of the privilegeGranted field.

import re
from panther_base_helpers import deep_get

#The regex pattern is stored in a variable
# Note: This is better performance than putting it in the rule function, which is evaluated on each event
ADMIN_PATTERN = re.compile(r"[aA]dministrator")

def rule(event):
    # using the deep_get function we can pull out the nested value under the "privilegeGranted" field
    value_to_search = deep_get(event, "debugContext", "debugData", "privilegeGranted")
    # finally we use the regex object we created earlier to check against our value
    # if there is a match, "True" is returned 
    return (bool(ADMIN_PATTERN.search(value_to_search, default="")))

In the example below, we use the Panther helper pattern_match:

from panther_base_helpers import pattern_match

def rule(event):
    return pattern_match(event.get("operation", ""), "REST.*.OBJECT")

References:

Python rule specification reference

Required fields are in bold.

Field Name

Description

Expected Value

AnalysisType

Indicates whether this analysis is a rule, scheduled_rule, policy, or global

Rules: rule Scheduled Rules: scheduled_rule

Enabled

Whether this rule is enabled

Boolean

FileName

The path (with file extension) to the python rule body

String

RuleID

The unique identifier of the rule

String Cannot include %

LogTypes

The list of logs to apply this rule to

List of strings

Severity

What severity this rule is

One of the following strings: Info, Low, Medium, High, or Critical

ScheduledQueries (field only for Scheduled Rules)

The list of Scheduled Query names to apply this rule to

List of strings

CreateAlert

Whether the rule should generate rule matches/an alert on matches (default true)

Boolean

Description

A brief description of the rule

String

DedupPeriodMinutes

The time period (in minutes) during which similar events of an alert will be grouped together

15,30,60,180 (3 hours),720 (12 hours), or 1440 (24 hours)

DisplayName

A friendly name to show in the UI and alerts. The RuleID will be displayed if this field is not set.

String

OutputIds

Static destination overrides. These will be used to determine how alerts from this rule are routed, taking priority over default routing based on severity.

List of strings

Reference

The reason this rule exists, often a link to documentation

String

Reports

A mapping of framework or report names to values this rule covers for that framework

Map of strings to list of strings

Runbook

The actions to be carried out if this rule returns an alert, often a link to documentation

String

SummaryAttributes

A list of fields that alerts should summarize.

List of strings

Threshold

How many events need to trigger this rule before an alert will be sent.

Integer

Tags

Tags used to categorize this rule

List of strings

Tests

Unit tests for this rule.

List of maps

Python Policy Specification Reference

Required fields are in bold.

A complete list of policy specification fields:

Field Name

Description

Expected Value

AnalysisType

Indicates whether this specification is defining a policy or a rule

policy

Enabled

Whether this policy is enabled

Boolean

FileName

The path (with file extension) to the python policy body

String

PolicyID

The unique identifier of the policy

String Cannot include %

ResourceTypes

What resource types this policy will apply to

List of strings

Severity

What severity this policy is

One of the following strings: Info, Low, Medium, High, or Critical

Description

A brief description of the policy

String

DisplayName

What name to display in the UI and alerts. The PolicyID will be displayed if this field is not set.

String

Reference

The reason this policy exists, often a link to documentation

String

Reports

A mapping of framework or report names to values this policy covers for that framework

Map of strings to list of strings

Runbook

The actions to be carried out if this policy fails, often a link to documentation

String

Suppressions

Patterns to ignore, e.g., aws::s3::*

List of strings

Tags

Tags used to categorize this policy

List of strings

Tests

Unit tests for this policy.

List of maps

Troubleshooting Detections

Visit the Panther Knowledge Base to view articles about detections that answer frequently asked questions and help you resolve common errors and issues.

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