Writing Python Detections

Construct Python detections in the Console or CLI workflow

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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 . When writing Python detections, try to follow , and remember that . Rules written in Python can be used in .

You can alternatively use the in the Console to create rules, or . If you aren't sure whether to write detections locally as Simple Detections or Python detections, see the section.

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

It is highly discouraged to make external API requests from within your detections in Panther. In general, detections are processed at a very high scale, and making API requests can overload receiving systems and cause your rules to exceed the .

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

In the left-hand navigation bar of your Panther Console, click Detections.
Click Create New.
In the Select Detection Type modal, choose Rule.
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 Rule Function text editor, write a Python rule function to define your detection.
    For detection templates and examples, see the .
- In the Create Alert section, set the Create Alert ON/OFF toggle. This indicates whether an should be created when there are matches, or only a . If you set this toggle to ON:
  - Severity: Select a 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.
    To see examples of runbooks for built-in rules, see .
    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 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 .
    Summary Attributes: Enter the attributes you want to showcase in the alerts that are triggered by this detection.
    To use a nested field as a summary attribute, use the Snowflake dot notation in the Summary Attribute field to traverse a path in a JSON object:
    <column>:<level1_element>.<level2_element>.<level3_element>
    The alert summary will then be generated for the referenced object in the alert.
    For more information on Alert Summaries, see .
    Custom Tags: Enter custom tags to help you understand the rule at a glance (e.g., HIPAA.)
    In the Framework Mapping section:
    Click Add New to enter a report.
    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 for the rule you defined in the previous step.
In the upper-right corner, click Save.

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

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 or a from Panther's .

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 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.

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')
```

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 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

In the left-hand navigation bar of your Panther Console, click Detections.
Click Create New.
In the Select Detection Type modal, choose Scheduled Rule.
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:
- In the Detect section:
  - In the Rule Function text editor, write a Python rule function to define your detection.
    def rule(event): return True
- - 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.
    Summary Attributes: Enter the attributes you want to showcase in the alerts that are triggered by this detection.
    To use a nested field as a summary attribute, use the Snowflake dot notation in the Summary Attribute field to traverse a path in a JSON object:
    <column>:<level1_element>.<level2_element>.<level3_element>
    Custom Tags: Enter custom tags to help you understand the rule at a glance (e.g., HIPAA.)
    In the Framework Mapping section:
    Click Add New to enter a report.
    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 upper-right corner, click Save.

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.

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).

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.

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'
Schedule:
  # Note: CronExpression and RateMinutes are mutually exclusive, only
  # configure one or the other
  CronExpression: '0 * * * *'
  RateMinutes: 1
  TimeoutMinutes: 1

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

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"
      }

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
Alert functions (dynamic)

Filter key
Metadata keys
Alert keys (static)

Basic Python rule structure

rule.py

rule.yml

`InlineFilters`

Alert functions in Python detections

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.

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

Alert function

Description

Default value

Return value

The level of urgency of the alert

In YAML: Severity key

In Console: Severity field

INFO, LOW, MEDIUM, HIGH,CRITICAL, or DEFAULT

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)

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.

The severity string is case insensitive, meaning you can return, for example, Critical or default, depending on your style preferences.

In the example below, if an API token has been created, a HIGH severity alert is generated—otherwise an INFO level alert is generated:

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

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 an alert.

In the example below, the log type, username, and a static string are sent to the alert destination. The function checks to see if the event is related the AWS.CloudTrail log type and, if so, returns the AWS Account Name.

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

`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.

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")

`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"]

`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).

Values included in the alert context dictionary must be JSON-compliant. Examples of non-compliant values include Python's nan, inf, and -inf.

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

`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.

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.

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.

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

`lookup()`

Function signature

def lookup(self, lookup_table_name: str, lookup_key: str) -> Any:

lookup() takes two arguments:

The name of the Lookup Table
- The Lookup Table name passed to lookup() must be as it appears on the Enrichment Providers or Lookup Tables pages in the Panther Console. This name may differ syntactically from how it appears in a search query; for example, My-Custom-LUT instead of my_custom_lut.
A Lookup Table primary key

If a match is found in the Lookup Table for the provided key, the full Lookup Table row is returned as a Python dictionary. If no match is found, None is returned.

Example using lookup():

# Imagine you have a Lookup Table named user_roles with the following entries:
# row 1: {"id": "your@email.com", "role": "admin"}
# row 2: {"id": "vistor@email.com", "role": "guest"}

# In this rule, we want to return True if the user has a non-admin role
# We want to fetch role from the Lookup Table, but the event doesn't 
# contain the Lookup Table's primary key (the email address)
def rule(event):
    lookup_table_name = "user_roles"
    # On the event, we *do* have access to the username, from which we can
    # generate the email address
    user_name = event.get("username", "").lower()
    lookup_key = f"{user_name}@email.com" # Dynamically compose the lookup key, or "Selector"
    
    lookup_data = event.lookup(lookup_table_name, lookup_key)
    # If a match occurs, `lookup_data` will contain the full row of data
    # Otherwise it will return None
    
    if (lookup_data and lookup_data.get("role") != "admin") or lookup_data is None:
        return True
        
    return False

Unit testing detections that use `lookup()`

_mocked_lookup_data_ should be structured like the following example:

Mocking event.lookup()

{
  "_mocked_lookup_data_": {
     "user_roles": { # This key is the name of your Lookup Table
         # The keys in this object should be the Lookup Table key
         # The values in this object should be the Lookup Table data object
         "your@email.com": {"id": "your@email.com", "role": "admin"},
         "vistor@email.com": {"id": "vistor@email.com", "role": "guest"}
      }
   }
}

If you do not specify a _mocked_lookup_data_ field in your unit test, attempts to call lookup() will return None/null.

`udm()`

Function signature

def udm(self, *key: str, default: Any = None) -> Any:

Here is how the udm() function works:

Sample udm usage

# Example usage when operating on data models 
def rule(event):
  return event.udm('field_on_data_model')
  
# Example usage when operating on data models with default
def rule(event):
  # The default parameter is only respected when using path-based mappings on
  # your data model. If your data model maps to a function, whatever value your
  # function returns will be respected
  return event.udm('field_on_data_model', default='')
  
# Example usage when operating on p_udm fields
def rule(event):
  # Note: When operating on p_udm fields, the udm function operates like our
  # deep_get function, allowing you to reference nested fields
  #
  # If the deep_get syntax is used and the top most field belongs to a mapped
  # data model field, udm will look at the p_udm field instead
  return event.udm('field_on_udm', 'nested_field', default='')

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

Available Python libraries

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

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

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

Accessing nested fields safely

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():

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

from panther_base_helpers import deep_get

def rule(event):
    return deep_get(event, "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

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 event values against a list (containing, for example, IP addresses or users) is quick in Python. It's a common pattern to set your rule logic to not match when an event value also exists in the list. This can help reduce false positives for known behavior in your environment.

When checking whether event values are in some collection, it's recommended to use a Python set—sets are more performant (i.e., memory efficient) than lists and tuples in Python. Lists and tuples, unlike sets, require iterating through each item in the collection to check for inclusion.

If the set against which you're performing the comparison is static, it's recommended to define it at the global level, rather than inside the rule() function. Global variables are initialized only once per Lambda invocation. Because a single Lambda invocation can process multiple events, a global variable is usually more efficient than initializing it each time rule() is invoked.

Example:

# Set - Recommended over tuples and 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)

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: ruleScheduled 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

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

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Last updated 9 days ago

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Overview

How to create detections in Python

How to create a rule in Python

Folder setup

File setup

How to create a scheduled rule in Python

Folder setup

File setup

How to create a policy in Python

Python detection syntax

Basic Python rule structure

InlineFilters

Alert functions in Python detections

severity

title

dedup

destinations

alert_context

runbook, reference, and description

Event object functions

get()

deep_get()

deep_walk()

lookup()

Unit testing detections that use lookup()

udm()

Python best practices

Available Python libraries

Python detection writing best practices

Writing tests for your detections

Casing for event fields

Understanding top level fields and nested fields

Accessing top-level fields safely

Using Global Helper functions

Accessing nested fields safely

Checking fields for specific values

Checking fields for Integer values

Using the Universal Data Model

Using multiple conditions

Searching values in lists

Matching events with regex

Python rule specification reference

Python Policy Specification Reference

Troubleshooting Detections

Overview

How to create detections in Python

How to create a rule in Python

Folder setup

File setup

How to create a scheduled rule in Python

Folder setup

File setup

How to create a policy in Python

Python detection syntax

Basic Python rule structure

InlineFilters

Alert functions in Python detections

severity

title

dedup

destinations

alert_context

runbook, reference, and description

Event object functions

get()

deep_get()

deep_walk()

lookup()

Unit testing detections that use lookup()

udm()

Example using udm() to access a value:

Python best practices

Available Python libraries

Python detection writing best practices

Writing tests for your detections

Casing for event fields

Understanding top level fields and nested fields

Accessing top-level fields safely

Using Global Helper functions

Accessing nested fields safely

`InlineFilters`

`severity`

`title`

`dedup`

`destinations`

`alert_context`

`runbook`, `reference`, and `description`

`get()`

`deep_get()`

`deep_walk()`

`lookup()`

Unit testing detections that use `lookup()`

`udm()`

`InlineFilters`

`severity`

`title`

`dedup`

`destinations`

`alert_context`

`runbook`, `reference`, and `description`

`get()`

`deep_get()`

`deep_walk()`

`lookup()`

Unit testing detections that use `lookup()`

`udm()`

Example using `udm()` to access a value:

Example using `udm()` to access a value: