PyPanther Detections

Configure detections fully in Python

Overview

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PyPanther Detections are in closed beta starting with Panther version 1.108. Please share any bug reports and feature requests with your Panther support team.

PyPanther Detections are Panther’s evolved approach to detections-as-code. In this framework, detections are defined fully in Python, enabling component reusability and simple rule overrides. The foundation of PyPanther Detections is the Panther-managed pypanther Python library.

Key features

  • An entirely Python-native experience for importing, writing, modifying, testing, and uploading rules—eliminating the need to manage a fork or clone of panther-analysis.

    # Import the Panther-managed BoxNewLogin rule
    from pypanther.rules.box import BoxNewLogin
  • The ability to apply custom configurations to Panther-managed rules through overrides, filters, and inheritance.

    from pypanther import Severity
    from pypanther.wrap import exclude
    
    # Set multiple attribute overrides
    BoxNewLogin.override(
        default_severity=Severity.MEDIUM,
        tags=['Initial Access'],
        default_runbook="Ask user in Slack if this login was actually from them.",
    )
    
    # Add a simple filter to exclude all logins from Alice
    exclude(lambda e: e.deep_get('created_by', 'name') != 'Alice')(BoxNewLogin)
  • The ability to selectively choose the set of rules you want to include in your Panther deployment package.

    from pypanther import register
    
    # Register a single rule to test and upload
    register(BoxNewLogin)

Benefits

PyPanther Detections have the following benefits:

  • No upstream merge conflicts: In the v1 model, merge conflicts can arise when syncing your customized fork of panther-analysis with the upstream repository. In this PyPanther model, Panther-managed rules exist separately from your rule configurations, eliminating the possibility of merge conflicts.

  • Full flexibility and composability: This feature offers complete flexibility in rule creation, enabling full modularity, composability, the ability to override any rule attribute, and full Python inheritance—all providing a customizable and user-centric experience.

  • First-class developer experience: Backed by a portable, open-source Python library called pypanther, this framework provides a superior local development experience by hooking into native applications and developer workflows. This library can also be loaded into any Python environment, such as Jupyter Notebooks.

PyPanther Detections vs. v1 detections

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This documentation uses the term "v1 detections," which refers to rules created in the format described in Writing Python Detections. PyPanther Detections are sometimes referred to as "v2 detections."

Panther has developed a tool that translates detections from v1 to PyPanther format—see the convert command on Using the pypanther Command Line Tool.

PyPanther Detections differ from v1 detections in the following areas:

  • File structure: A rule in the v1 framework requires two files: a Python file to define rule logic and dynamic alerting functions and a YAML file to set metadata. A PyPanther rule is written entirely in Python.

    • This singular rule definition in a Python class—which contains all functions, properties, and helpers—enables overrides and composability.

  • Process for retrieving Panther-managed detections: In v1 detections, you must periodically sync your copy of panther-analysis with upstream changes. With PyPanther Detections, however, no Git syncing is required—the latest Panther-managed content is always available in the pypanther Python library.

  • Packs: Panther-managed v1 detections are bundled in Detection Packsarrow-up-right. With PyPanther Detections, you can choose which detections you want to include in your Panther instance using get_panther_rules() (or direct imports) with register().

The same detection, Box.New.Login is defined below in both versions:

v1 Box New Login rule
PyPanther Box New Login rule

Limitations of PyPanther Detections

PyPanther Detections are currently designed for real-time rules developed in the CLI workflow.

While PyPanther Detections are evolving rapidly, they currently have the following limitations:

Getting started using PyPanther Detections

Panther has provided this pypanther-starter-kit repositoryarrow-up-right, containing PyPanther Detection examples, which you can clone to quickly get up and running.

Get started by following the setup instructions in the repository's READMEarrow-up-right.

Creating PyPanther Detections

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Before writing PyPanther Detections, you’ll need to set up your environment. See Getting started using PyPanther Detections, above.

When working with PyPanther Detections:

  • A main.py file controls your entire detection configuration, outlining which rules to register, override configurations, and custom rule definitions. You can either:

    • (Recommended) Define your detections in various other files/folders, then import them into main.py

    • Define your detections in main.py

  • A PyPanther rule is defined in a single Python file. Within it, you can import Panther-managed (or your own custom) PyPanther rules and specify overrides. A single Python file can define multiple detections.

  • All PyPanther rules subclass the pypanther Rule class or a parent class of type Rule.

  • Rules must be registered to be tested and uploaded to your Panther instance.

  • All event object functions currently available in v1 detections are available in PyPanther Detections. These include: get()arrow-up-right, deep_get()arrow-up-right, deep_walk()arrow-up-right, and udm()arrow-up-right.

  • All alert functions available in Python (v1) detectionsarrow-up-right are available in PyPanther Detections, such as title() and severity(). See Rule auxiliary/alerting function reference.

  • Use the pypanther type-ahead hints in your IDE, like searching for available rules or viewing properties of classes. Under a header reading "from pypanther.rules." is a menu with various values, including {}aws_cloudtrail_rules.

Writing a custom PyPanther Detection

A "custom" PyPanther rule is one that you write completely from scratch—i.e., one that isn't built from a Panther-managed rule. Custom PyPanther rules are defined in a Python class that subclasses the pypanther Rule class. In this class, you must:

  • Define a rule() function

  • Define certain attributes, such as log_types

    • (Optional) Define additional attributes, such as threshold or dedup_period_minutes

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The id attribute is only required for a rule if you plan to register it.

See the Rule property reference section for a full list of required and optional fields.

Importing Panther-managed rules

Panther-managed rules can be imported directly or using the get_panther_rules() function.

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Panther-managed rules currently all have a -prototype suffix (e.g., AWS.Root.Activity-prototype). This is temporary, and will be removed in the future.

You may want to import Panther-managed rules (into main.py or another file) to either register them individually as-is, set overrides on them, or subclass them. You can import Panther-managed rules directly (from the pypanther.rules module) or by using the get_panther_rules() function.

To import a Panther-managed rule directly using the rules module, you would use a statement like:

The get_panther_rules() function can filter on any Rule class attribute, such as default_severity, log_types, or tag. When filtering, keys use AND logic, and values use OR logic.

Get all Panther-managed rules using get_panther_rules():

Get Panther-managed rules with certain severities using get_panther_rules():

Get Panther-managed rules for certain log types using get_panther_rules():

Get Panther-managed rules that meet multiple criteria using get_panther_rules():

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See Using list comprehension for an example of how to use get_panther_rules() with advanced Python.

Once you’ve imported a Panther-managed rule, you can modify it using inheritance or overrides.

Creating include or exclude filters

PyPanther Detection filters let you exclude certain events from being evaluated by a rule. Filters are designed to be applied on top of existing rule logic (likely for Panther-managed PyPanther Detections you are importing).

Each filter defines logic that is run before the rule() function, and the outcome of the filter determines whether or not the event should go on to be evaluated by the rule.

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Common use cases for filters include:

  • To target only certain environments, like prod

  • To exclude events that are known false positives, due to a misconfiguration or other non-malicious scenario

There are two types of filters:

  • include filters: If the filter returns True for an event, the event is evaluated by rule()

  • exclude filters: If the filter returns True for an event, the event is dismissed (i.e., not evaluated by rule())

include and exclude, which can be imported from the pypanther.wrap module, can run as standalone functions or as rule class decorators.

Examples as standalone functions:

Example as rule class decorators:

Filters can also be reused with a for loop to decorate multiple rules:

Applying overrides on existing rules

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If your objective is to modify a rule's logic, it's recommended to use include/exclude filters instead of overriding the rule() function itself. Learn more in Use filters instead of overriding rule().

When making overrides on Panther-managed detections, it's recommended to:

  • Outside of main.py, store all of your overrides in apply_overrides() functions.

  • In main.py, call pypanther's apply_overrides() to apply each of your apply_overrides() functions.

Learn more about apply_overrides() in PyPanther Detections Style Guide.

Overriding single attributes

You can override a rule’s attributes with a one-line statement:

Overriding multiple attributes with the override function

It’s also possible to configure multi-line overrides with the override() function:

Applying overrides on multiple PyPanther rules

To apply overrides on multiple rules at once, iterate over the collection using a for loop.

This could be useful, for example, when updating a certain attribute for all rules associated to a certain LogType.

Ensuring necessary fields are set on configuration-required rules

Panther-managed rules that require some customer configuration before they are uploaded into a Panther environment may include a validate_config() function, which defines one or more conditions that must be met for the rule to pass the test command (and function properly).

Most commonly, validate_config() verifies that some class variable, such as an allowlist or denylist, has been assigned a value. If the requirements included in validate_config() are not met, an exception will be raised when the pypanther test command is run (if the rule is registered).

Example:

In this example, if allowed_domains is not assigned a non-empty list, an assertion error will be thrown during pypanther test.

To set this value, you can use a statement like:

Creating PyPanther rules with inheritance

You can use inheritance to create rules that are subclasses of other rules (that are Panther-managed or custom).

It’s recommended to use inheritance when you’re creating a collection of rules that all share a number of characteristics—for example, rule() function logic, property values, class variables, or helper functions. In this case, it’s useful to create a base rule that all related rules inherit from.

For example, it may be useful to create a base rule for each LogType, from which all rules for that LogType are extended.

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Inheritance is commonly used with filters—i.e., subclassed rules can define additional criteria an event must meet in order to be processed by the rule.

If you don’t plan to register a base rule, it’s not required to provide it an id property.

Example:

Using advanced Python

Because PyPanther rules are fully defined in Python, you can use its full expressiveness when customizing your detections.

Calling super()

For more advanced use cases, you can supplement the logic in functions defined by the parent rule.

Using list comprehension

You can use Python’s list comprehension functionality to create a new list based on an existing list with condensed syntax. This may be particularly useful when you want to filter a list of detections fetched using get_panther_rules().

Registering PyPanther Detections

Registering a PyPanther rule means including it in your Panther deployment package.

To register a rule, pass it in to register() in your main.py file. When you run the pypanther upload or test commands, only rules passed in to register() will be uploaded to your Panther instance or tested.

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Registering a single rule more than once (perhaps because it’s included in multiple collections, which are all passed into register()) will not result in an error.

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It’s not required to register rules used as base rules with inheritance so long as you do not want the base rules themselves uploaded into your Panther instance.

Viewing registered rules

  • To see all rules that are registered given your currently configured repository, run the pypanther list rules CLI command.

Importing instances of Rule

You can use the get_rules() function to easily fetch rules you might want to register in main.py. get_rules() takes in an imported package (folder) or module (file) name, and returns all rules from that package or module that inherit the pypanther Rule class.

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Each folder containing rules imported with get_rules() must contain an __init__.py file. Learn more about recommended repository structure in the PyPanther Detections Style Guide.

Example using get_rules():

Registering vs. enabling a rule

All rules have an enabled property, which is different from being registered. See the table below for all possible outcomes:

enabled = True
enabled = False

Registered

Uploaded to Panther and enabled. Unit tests are run during testing.

Uploaded to Panther and disabled. Unit tests are run during testing.

Not registered

Not uploaded to Panther. If uploaded previously, deleted. Unit tests are not run during testing.

Not uploaded to Panther. If uploaded previously, deleted. Unit tests are not run during testing.

Testing PyPanther Detections

Defining tests

Tests for PyPanther Detections are defined by creating instances of the pypanther RuleTest class.

Each instance of RuleTest must set a name, expected_result, and log. Each test can also optionally define:

  • Additional fields (prepended with expected_) that verify the output of alert functions. For example, expected_severity and expected_title.

  • A mocks field, which takes a list of RuleMocks.

See a full list of available fields in the RuleTest property reference.

Tests are associated with rules by assigning them to a rule’s tests field. Tests can be defined directly within a rule, or separately set to a variable (that is either local or imported).

Example

Note that this rule's tests (defined above the rule class) use mocks, expected_title, expected_dedup, and expected_alert_context.

chevron-rightExample: Rule with testshashtag

Running tests

To run all tests defined for your PyPanther Detections, run:

To run only a subset of tests, filter the detections for which tests are run by using a filter flag with test, such as --id or --log-types. See a full list of filter flags by running pypanther test --help.

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The test command:

  • Only tests those detections passed into register()

  • Skips tests for Panther-managed rules

If any rules fail a test, the error will print next to the rule name that was tested:

Testing custom helper functions and data fixtures

The pypanther test command tests all PyPanther Detection classes that have a tests attribute set. In addition to testing rule() and alert function output in this way, if you have created custom helper functions for use in rules, you may want to write targeted tests for these helper functions. To do this, it's recommended to use a common Python testing framework, such as pytestarrow-up-right or unittestarrow-up-right.

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Example: using pytest to test a custom function

Say you'd like to write a reusable function that reads a list of AWS account IDs from a file and filters it to include only the production ones. (This function could then be used in an include or exclude filter.) This function might look like the following:

Given this function, there are a few ways in which you may want to test both the data fixture (AWS_ACCOUNTS) itself, as well as the function is_prod_aws_account(). For instance:

  • To verify that the AWS_ACCOUNTS data fixture:

    • Has a certain number of rows

    • Has a certain required field

  • To verify that certain account IDs are included in the prod list (and others are not)

To test these characteristics using pytest, you would add the following functions (all with the test_ prefix):

After you define these tests, you can invoke them by running pytest from your command line.

Uploading PyPanther Detections to Panther

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To upload all PyPanther Detections that are registered, run:

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You must authenticate when using upload—see Authenticating CLI commands.

You can see all upload options by running pypanther upload -h, but may find the following options particularly useful:

  • --verbose: Generates verbose output, which includes a list of tests by detection (and their pass/fail statuses), a list of registered detections, and a list of included files

  • --dry-run: Do not upload, but show a summary of the changes that will be applied in the next upload

  • --output {text, json}: Prints output in the provided format

    • text is the default value, but json can be useful if you plan to port the output into another workflow

chevron-rightSample output for pypanther upload --verbose --output jsonhashtag

If a previously uploaded rule is not passed in to register() on a subsequent invocation of upload, it will be deleted in your Panther instance.

Uploading a PyPanther rule with the same id as an existing v1 rule's RuleId will overwrite it. The same is true in the other direction—i.e., uploading a PyPanther rule with the same RuleId as an existing PyPanther rule's id will overwrite it.

upload limitations

The following limitations currently apply to pypanther upload:

  • A maximum of 500 custom rules can be uploaded at once.

    • This limit does not include Panther-managed rules (i.e., those returned by the get_panther_rules() function)

  • The total size of the zip file pypanther produces for upload cannot exceed 4 MB.

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