Testing

Use unit tests to ensure your detections are working as expected
Overview
Testing your detections ensures that once deployed, detections will behave as expected, generating alerts appropriately. Testing also promotes reliability and protects against regressions as code evolves over time.
Testing works by defining a test log event for a certain detection, and indicating whether or not you'd expect an alert to be generated when the test event is processed by that detection. Panther doesn't enforce a required minimum or maximum number of tests for each detection, but it's recommended to configure at least two—one false positive and one true positive.
You can create, edit, and delete tests for custom detections (i.e., those that aren't Panther-managed). Tests for Panther-managed detections are maintained by Panther and are read-only.
Panther's Data Replay, which allows you to run historical log data through a rule to preview the outcome, is also useful while testing.
Using tests
How to create a test
You can create tests for detections that are not Panther-managed in the Panther Console, or programmatically with the Panther Analysis Tool.
Console (detection page)
Console (alert event)
Developer Workflows
1.Log in to the Panther Console.
2.In the left sidebar, click Build > Detections.
3.Click into a Detection or create a new Detection.
4.Click the Functions & Tests tab.
5.Click Edit in the upper right corner of the page.
6.Scroll down, below the Rule Function text editor, to the Unit Test text editor.
7.In the upper right corner of the testing text editor, click Add New. 
If the detection is Panther-managed, Add New will be disabled, as you cannot create new tests.
8.The newly created test is given a placeholder name. To its right, click the three dots icon and select Rename. 
Provide a meaningful name for the test, and press enter, or return, to save it.
9.Toggle whether the test event should trigger an alert.
10.Compose a sample case in the text editor. When you are finished, click Run Test.
In the Panther Console, you can create a new unit test directly from an alert, using an actual event associated with the alert. Follow the instructions in Add filters from an alert event, taking note of steps 9 and 10. 
Note that this functionality is only available for rules.
Rules
In your spec file, add the Tests key with sample cases:
Tests:
  -
    Name: Name to describe our first test
    LogType: LogType.GoesHere
    ExpectedResult: true or false
    Log:
      {
        "hostName": "test-01.prod.acme.io",
        "user": "martin_smith",
        "eventTime": "June 22 5:50:52 PM"
      }
We recommend running as many test cases as possible, including both true and false positives.
​
Policies
In the spec file, add the following Tests key:
Tests:
  -
    Name: Name to describe our first test.
    ResourceType: AWS.S3.Bucket
    ExpectedResult: true
    Resource:
      {
        "PublicAccessBlockConfiguration": null,
        "Region": "us-east-1",
        "Policy": null,
        "AccountId": "123456789012",
        "LoggingPolicy": {
          "TargetBucket": "access-logs-us-east-1-100",
          "TargetGrants": null,
          "TargetPrefix": "acmecorp-fiancial-data/"
        },
        "EncryptionRules": [
          {
            "ApplyServerSideEncryptionByDefault": {
              "SSEAlgorithm": "AES256",
              "KMSMasterKeyID": null
            }
          }
        ],
        "Arn": "arn:aws:s3:::acmecorp-fiancial-data",
        "Name": "acmecorp-fiancial-data",
        "LifecycleRules": null,
        "ResourceType": "AWS.S3.Bucket",
        "Grants": [
          {
            "Permission": "FULL_CONTROL",
            "Grantee": {
              "URI": null,
              "EmailAddress": null,
              "DisplayName": "admins",
              "Type": "CanonicalUser",
              "ID": "013ae1034i130431431"
            }
          }
        ],
        "Versioning": "Enabled",
        "ResourceId": "arn:aws:s3:::acmecorp-fiancial-data",
        "Tags": {
          "aws:cloudformation:logical-id": "FinancialDataBucket"
        },
        "Owner": {
          "ID": "013ae1034i130431431",
          "DisplayName": "admins"
        },
        "TimeCreated": "2020-06-13T17:16:36.000Z",
        "ObjectLockConfiguration": null,
        "MFADelete": null
      }
The value of Resource can be a JSON object copied directly from the Policies > Resources explorer.
How to rename or delete a test in the Panther Console
You can rename or delete tests for detections that are not Panther-managed.
1.Log in to the Panther Console.
2.In the left sidebar, click Build > Detections.
3.Click into a detection.
4.Click the Functions & Tests tab.
5.Click Edit in the upper right corner of the page.
6.Scroll down, below the Rule Function text editor, to the Unit Test text editor.
7.To the right of the test's name, click the three dot icon.

If the detection is Panther-managed, its tests cannot be renamed or deleted. Instead of a three dot icon next to the name of each test, a Panther icon will appear.
8.Click Rename or Delete.
If renaming, enter the new name and press enter, or return, to save.
If deleting, a Delete Test confirmation modal will pop up. 
Select Confirm.
Test example
Click Edit in the upper right corner of the page.
Scroll down, below the Rule Function text editor, to the Unit Test text editor.
Keeping with the previous example (in Rules), let's write two tests for this detection:
def rule(event):
  return event.get('status') == 200 and 'admin-panel' in event.get('request')
​
    
def title(event):
  return f"Successful admin panel login detected from {event.get('remoteAddr')}"
​
​
def dedup(event):
  return event.get('remoteAddr')
Name: Successful admin-panel Logins
Test event should trigger an alert: Yes
{
  "httpReferer": "https://domain1.com/?p=1",
  "httpUserAgent": "Chrome/80.0.3987.132 Safari/537.36",
  "remoteAddr": "180.76.15.143",
  "request": "GET /admin-panel/ HTTP/1.1",
  "status": 200,
  "time": "2019-02-06 00:00:38 +0000 UTC"
}
Name: Errored requests to the access-policy page
Test event should trigger an alert: No
{
  "httpReferer": "https://domain1.com/?p=1",
  "httpUserAgent": "Chrome/80.0.3987.132 Safari/537.36",
  "remoteAddr": "180.76.15.143",
  "request": "GET /access-policy/ HTTP/1.1",
  "status": 500,
  "time": "2019-02-06 00:00:38 +0000 UTC"
}
Use as many combinations as you would like to ensure the highest reliability with your detections.
Mocks
Panther's testing framework also allows for basic Python call mocking. Both policy and rule tests support unit test mocking.
When writing a detection that requires an external API call, mocks can be utilized to mimic the server response in the unit tests without having to actually execute an API call. 
Mocks are defined by a given Mock Name and Return Value which respectively denote the name of the object to patch and the string to be returned when the patched object is invoked.
Mocks are defined on the unit test level, allowing you to define different mocks for each unit test. 
How to use mocks
Panther Console
Panther Developer Workflows
To add a mock to a unit test in the Console:
1.Within the Unit Test section, locate the Mock Testing section, below the test event editor.
2.Add values for Mock Name and Return Value.
3.To test that your mock is behaving as expected, click Run Test.
To configure this using a CI/CD workflow, add the Mocks key to your test case. The Mocks key is used to define a list of functions you want to mock, and the value that should be returned when that function is called. Multiple functions can be mocked in a single test. 
For example, if we have a rule test and want to mock the function get_counter to always return a 1 and the function geoinfo_from_ip to always return a specific set of geo IP info, we could write our unit test like this:
Tests:
  -
    Name: Test With Mock
    LogType: LogType.Custom
    ExpectedResult: true
    Mocks:
      - objectName: get_counter
        returnValue: 1
      - objectName: geoinfo_from_ip
        returnValue: >-
                          {
                          "region": "UnitTestRegion",
                          "city": "UnitTestCityNew",
                          "country": "UnitTestCountry"
                          }
    Log:
      {
        "hostName": "test-01.prod.acme.io",
        "user": "martin_smith",
        "eventTime": "June 22 5:50:52 PM"
      }
Mocking allows us to emulate network calls without requiring API keys or network access in our CI/CD pipeline, and without muddying the state of external tracking systems (such as the panther KV store).
See the related documentation for more information on using Panther Analysis Tool (PAT) and CI/CD workflows.
Mocks are allowed on the global and built-in python namespaces, this includes:
1.Imported Modules and Functions
2.Global Variables
3.Built-in Python Functions
4.User-Defined Functions
Python provides two distinct forms of importing, which can be mocked as such:
import package 
Mock Name: package
from package import module
Mock Name: module
Example mock
This example is based on the AWS Config Global Resources detection.
The detection utilizes a global helper function resource_lookup from panther_oss_helpers which queries the resources-api and returns the resource attributes. However, the unit test should be able to be performed without any external API calls.
This test fails as there is no corresponding resource mapping to the generic example data.
Diving into the detection
# --- Snipped ---
from panther_oss_helpers import resource_lookup
# --- Snipped ---
def policy(resource):
    # --- Snipped ---
    for recorder_name in resource.get("Recorders", []):
        recorder = resource_lookup(recorder_name)
        resource_records_global_resources = bool(
            deep_get(recorder, "RecordingGroup", "IncludeGlobalResourceTypes")
            and deep_get(recorder, "Status", "Recording")
        )
        if resource_records_global_resources:
            return True
    return False
    # --- Snipped ---
The detection uses the from panther_oss_helpers import resource_lookup convention which means the mock should be defined for the resource_lookup function.
Mocks provide a way to leverage real world data to test the detection logic.
The return value used:
 { "AccountId": "012345678910", "Name": "Default", "RecordingGroup": { "AllSupported": true, "IncludeGlobalResourceTypes": true, "ResourceTypes": null }, "Region": "us-east-1", "ResourceId": "012345678910:us-east-1:AWS.Config.Recorder", "ResourceType": "AWS.Config.Recorder", "RoleARN": "arn:aws:iam::012345678910:role/PantherAWSConfig", "Status": { "LastErrorCode": null, "LastErrorMessage": null, "LastStartTime": "2018-10-05T22:45:01.838Z", "LastStatus": "SUCCESS", "LastStatusChangeTime": "2021-05-28T17:45:14.916Z", "LastStopTime": null, "Name": "Default", "Recording": true }, "Tags": null, "TimeCreated": null }
While this resource should be compliant, the unit test fails. 
Detections that do not expect a string to be returned requires a small tweak for mocks.
In order to get this unit test working as expected, the following modifications need to be made:
# --- Snipped ---
import json
# Another option is to use: from ast import literal_eval
# --- Snipped ---
def policy(resource):
    # --- Snipped ---
        recorder = resource_lookup(recorder_name)
        if isinstance(recorder, str):
            recorder = json.loads(recorder)
    # --- Snipped ---
Once this modification is added, you can now test the detection logic with real data!
Mocks from the CLI
Unit test mocking is also supported with CLI based workflows for writing detections. For details on adding unit test mocks to a CLI based detection, see the unit test mocking section of the Panther Analysis Tool docs.
Enrich test data
If you have Lookup Table(s) configured and created a detection to leverage them, click Enrich Test Data in the upper right side of the JSON event editor to ensure that p_enrichment is populated correctly.
Policies
Data Replay (Beta)
Last modified 1mo ago