Writing and Editing Detections
You can write your own Python detections in the Panther Console or locally, following the CI/CD workflow. This page contains detection writing examples and best practices, available auxiliary functions, and guidance on how to configure a detection dynamically.
For instructions on how to write detections, see the following pages:
Before you write a new 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 Rule 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.
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.
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 |
See the Detection Functions section below to view all available functions within a Panther Detection.
The only required function is
def rule(event), but other functions make your Alerts more dynamic. See the section Configuring Detection functions dynamically for examples and for more information about the different functions.def rule(event):
if event.get("Something"):
return True
return False
return True triggers an alert, while return False does not trigger an alert.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.
Lookups for event fields are not case sensitive.
event.get("Event_Type") or event.get("event_type") will return the same result.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.
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 example 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'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. For example, see the
deep_get() function referenced in the next section.If the field is nested deep within the event, use a Panther-supplied function called
deep_get() to safely access the fields value. deep_get() must be imported by the panther_base_helpers library.deep_get() takes two or more arguments:- The event object itself (required)
- The top-level field name (required)
- Any nested fields, in order (as many nested fields as needed)
Example:
AWS CloudTrail logs nest the type of user accessing the console underneath
userIdentity.JSON CloudTrail root activity:
{
"eventVersion": "1.05",
"userIdentity": {
"type": "Root",
"principalId": "1111",
"arn": "arn:aws:iam::123456789012:root",
"accountId": "123456789012",
"userName": "root"
},
...
}
Here is how you could check that value safely with
deep_get:from panther_base_helpers import deep_get
def rule(event):
return deep_get(event, "userIdentity", "type") == "Root"
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.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:
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:
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
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)
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:
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 listed takes a single argument of
event (Rules) or resource (Policies). Advanced users may define functions, variables, or classes outside of the functions defined below.The only required function is
def rule(event), but other functions make your Alerts more dynamic.Detection Alerting Function Name | Description | Return Value | Default Return Value |
|---|---|---|---|
title | The generated alert title | String | If not defined, the Display Name, RuleID, or PolicyID is used |
dedup | The string to group related events with, limited to 1000 characters | String | If not defined, the titlefunction output is used. |
alert_context | Additional context to pass to the alert destination(s) | Dict[String: Any] | An empty Dict |
severity | The level of urgency of the alert | INFO, LOW, MEDIUM, HIGH, or CRITICAL | The severity as defined in the detection metadata |
description | An explanation about why the rule exists | String | The description as defined in the detection metadata |
reference | A reference URL to an internal document or online resource about the rule | String | The reference as defined in the detection metadata |
runbook | A list of instructions to follow once the alert is generated | String | The runbook as defined in the detection metadata |
destinations | The label or ID of the destinations to specifically send alerts to. An empty list will suppress all alerts. | List[Destination Name/ID] | The destinations as defined in the detection metadata |
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.
If the dedup function is not present, the title is used to group related events for deduplication purposes.
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
Deduplication is the process of grouping suspicious events together into a single alert to prevent receiving duplicate alerts for the same behavior that may have multiple indicators. Any event that triggers a detection is grouped together with other events that triggered the same detection and subsequent deduplication string within the designated deduplication period. It returns a string used to group related events. It is limited to 1000 characters. If this function is not present, the title string will be used to group events.
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")
In some scenarios, you may need to upgrade or downgrade the severity level of an alert. In the example below, a HIGH severity alert is returned if an API token is created - otherwise we create an INFO level alert. The severity levels of an Alert can be mapped to INFO, LOW, MEDIUM, HIGH, or CRITICAL.
Example:
def severity(event):
if event.get('eventType') == 'system.api_token.create':
return "HIGH"
return "INFO"
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"]
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")
}
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>"
Visit the Panther Knowledge Base to view articles about detections that answer frequently asked questions and help you resolve common errors and issues.
Last modified 17d ago