Policies

Overview
Panther enables easy scanning and evaluation of cloud infrastructure configurations.
Policies are Python functions used to identify misconfigured cloud infrastructure and generate alerts for your team.
How to write a policy
Policy Body
The policy body must:
Be valid Python3.
Define a policy() function that accepts one resource argument.
Each policy takes a resource input of a given resource type from the supported resources page. 
Return a bool from the policy function.
def policy(resource):
  return True
The Python body should name the argument to the policy() function resource and also may do the following:
Import standard Python3 libraries
Import from the user defined aws_globals module
Import from the Panther defined panther module
Define additional helper functions as needed
Define variables and classes outside the scope of the rule function
Using the schemas in supported resources provides details on all available fields in resources. Top level keys are always present, although they may contain NoneType values.
Writing policies locally and in the Panther Console
You can write and deploy policies in the Panther Console or you can write them locally and upload them to Panther via Panther Analysis Tool (PAT).
Panther Console
Write policies locally
1.Log in to the Panther Console.
2.In the left sidebar, click Build > Detections.
3.In the upper right, click Create New.
4.At the top of the page, choose Policy as the detection type.
5.Fill out the Basic Info form:
Name: Enter a descriptive name.
Severity: Select a detection severity from the drop-down options.
Resource Types: Select the resource you want to apply this policy to. Leave empty to apply to all resources.
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6.Scroll down to Policy Settings. Optionally add a Description, Runbook, Reference, Ignore Patterns, Custom Tags, and Destination Overrides.
7.Click the Functions & Tests tab. Write a Python function to define your policy in the Policy Function text editor.
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For detection templates and examples, see the panther_analysis repo on Github.
8.Click {} Create Test to run a test against the Policy you defined in the previous step.
9.In the upper right corner of the page, click Save.
File setup
Each detection consists of:
A Python file (a file with a .py extension) containing your detection/audit logic 
A YAML or JSON specification file (a file with a .yml or .json extension) containing metadata attributes of the detection. 
By convention, we give this file the same name as the Python file.
We recommend creating folders based on log/resource type to group your detections, such as suricata_rules or aws_s3_policies. You can use the open source Panther Analysis repo as a reference.
We also recommend managing these files in a version control system (VCS). Most commonly we see GitHub or GitLab used, which are managed git providers.
It's best practice to create a fork of Panther's open-source analysis repository, but you can also create your own repo from scratch.
Writing policies locally
1.​Write your policy and save it (in your folder of choice) as my_new_policy.py:
def polcy(resource):
def policy(resource):  
  return resource['Region'] != 'us-east-1'
2.Create a specification file using the template below:
AnalysisType: policy
Enabled: true
Filename: my_new_policy.py
PolicyID: Category.Type.MoreInfo
ResourceType:
  - Resource.Type.Here
Severity: Info|Low|Medium|High|Critical
DisplayName: Example Policy to Check the Format of the Spec
Tags:
  - Tags
  - Go
  - Here
Runbook: Find out who changed the spec format.
Reference: https://www.link-to-info.io
See Policy Specification Reference below for a complete list of required and optional fields.
Policy Writing Best Practices
Constructing Test Resources
Manually building test cases can be prone to human error. We suggest one of the following methods:
Option 1: In the Panther Console, navigate to Investigate > Cloud Resources. Apply a filter of the resource type you intend to emulate in your test. Select a resource in your environment, and on the Attributes card you can copy the full JSON representation of that resource by selecting copy button next to the word root.
Option 2: Open the Panther Resources documentation, and navigate to the section for the resource you are trying to emulate. Copy the provided example resource. Paste this in to the resource editor if you're working in the web UI, or into the Resource field if you are working locally. Now you can manually modify the fields relevant to your policy and the specific test case you are trying to emulate.
Option 1 is best when it is practical, as this can provide real test data for your policies. Additionally, it is often the case that you are writing/modifying a policy specifically because of an offending resource in your account. Using that exact resource's JSON representation as your test case can guarantee that similar resources will be caught by your policy in the future.
Debugging Exceptions
Debugging exceptions can be difficult, as you do not have direct access to the Python environment running the policies.
When you see a policy that is showing the state Error on a given resource, that means that the policy threw an exception. The best method for troubleshooting these errors is to use option 1 in the Constructing test resources section above and create a test case from the resource causing the exception.
Running this test case either locally or in the Panther Console should provide more context for the issue, and allow you to modify the policy to debug the exception without having to run the policy against all resources in your environment.
Note: Anything printed to stdout or stderr by your Python code will end up in CloudWatch.  For SaaS/CPaaS customers, Panther engineers can see these CloudWatch logs during routine application monitoring.
Policy examples
S3 public read access
In the example below, the policy checks if an S3 bucket allows public read access:
# A list of grantees that represent public access
GRANTEES = {
    'http://acs.amazonaws.com/groups/global/AuthenticatedUsers',
    'http://acs.amazonaws.com/groups/global/AllUsers'
}
PERMISSIONS = {'READ'}
​
​
def policy(resource):
    for grant in resource['Grants']:
        if grant['Grantee']['URI'] in GRANTEES and grant[
                'Permission'] in PERMISSIONS:
            return False
​
    return True
IAM Password Policy
This example policy alerts when the password policy does not enforce a maximum password age:
def policy(resource):
    if resource['MaxPasswordAge'] is None:
        return False
    return resource['MaxPasswordAge'] <= 90
In the policy() body, returning a value of True indicates the resource is compliant and no alert should be sent. Returning a value of False indicates the resource is non-compliant.
The policy is based on an IAM Password Policy resource:
{
    "AccountId": "123456789012",
    "AllowUsersToChangePassword": true,
    "AnyExist": true,
    "ExpirePasswords": true,
    "HardExpiry": null,
    "MaxPasswordAge": 90,
    "MinimumPasswordLength": 14,
    "Name": "AWS.PasswordPolicy",
    "PasswordReusePrevention": 24,
    "Region": "global",
    "RequireLowercaseCharacters": true,
    "RequireNumbers": true,
    "RequireSymbols": true,
    "RequireUppercaseCharacters": true,
    "ResourceId": "123456789012::AWS.PasswordPolicy",
    "ResourceType": "AWS.PasswordPolicy",
    "Tags": null,
    "TimeCreated": null
}
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
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
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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
Tags
Tags used to categorize this policy
List of strings
Tests
Unit tests for this policy.
List of maps
​
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Data Replay (Beta)

Testing

Last modified 5d ago

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
`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
`Tags`	Tags used to categorize this policy	List of strings
`Tests`	Unit tests for this policy.	List of maps

Policies

Overview

How to write a policy

Policy Body

Writing policies locally and in the Panther Console

File setup

Writing policies locally

Policy Writing Best Practices

Constructing Test Resources

Debugging Exceptions

Policy examples

S3 public read access

IAM Password Policy

Policy Specification Reference

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