PyPanther Detections (Beta)

Configure detections fully in Python

Overview

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 open-source pypanther Python library.

Learn about PyPanther key features, benefits, and differences from the v1 detection format on this page—then, start creating PyPanther Detections in the CLI workflow or the Panther Console.

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
  
  # 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
  BoxNewLogin.extend(
      exclude_filters=[lambda e: e.deep_get('created_by', 'name') == 'Alice'],
  )

• 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: Working in the CLI workflow in the v1 model, merge conflicts can arise when syncing your customized fork of panther-analysis with the upstream repository. In PyPanther, Panther-managed rules exist separately from your rule configurations, eliminating the possibility of merge conflicts.

• Full flexibility and composability: PyPanther 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, 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

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."

PyPanther Detections differ from v1 detections in the following areas:

• File structure: Working in the CLI workflow, 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 a single Python class containing all functions, properties, and helpers.
• Process for retrieving Panther-managed detections: In v1 detections, you must periodically sync your copy of panther-analysis with upstream changes. With PyPanther Detections, 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 Packs. 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

# box_new_login.py from panther_base_helpers import deep_get def rule(event): return event.get("event_type") == "ADD_LOGIN_ACTIVITY_DEVICE" def title(event): return ( f"User [{deep_get(event, 'created_by', 'name', default='<UNKNOWN_USER>')}] " f"logged in from a new device." ) # box_new_login.yml AnalysisType: rule Filename: box_new_login.py RuleID: "Box.New.Login" DisplayName: "Box New Login" Enabled: true LogTypes: - Box.Event Tags: - Box - Initial Access:Valid Accounts Reports: MITRE ATT&CK: - TA0001:T1078 Severity: Info Description: A user logged in from a new device. Reference: <https://support.box.com/hc/en-us/articles/360043691914-Controlling-Devices-Used-to-Access-Box> Runbook: Investigate whether this is a valid user login. SummaryAttributes: - ip_address Tests: - ...

# box_new_login.py pypanther rule from pypanther import Rule, Severity, LogType class BoxNewLogin(Rule): id = "Box.New.Login" display_name = "Box New Login" log_types = [LogType.BOX_EVENT] tags = ["Box", "Initial Access:Valid Accounts"] reports = {"MITRE ATT&CK": ["TA0001:T1078"]} default_severity = Severity.INFO default_description = "A user logged in from a new device.\\n" default_reference = "<https://support.box.com/hc/en-us/articles/360043691914-Controlling-Devices-Used-to-Access-Box>" default_runbook = "Investigate whether this is a valid user login.\\n" summary_attributes = ["ip_address"] tests = box_new_login_tests # Not shown in the example def rule(self, event): return event.get("event_type") == "ADD_LOGIN_ACTIVITY_DEVICE" def title(self, event): return f"User [{event.deep_get('created_by', 'name', default='<UNKNOWN_USER>')}] logged in from a new device."

Limitations of PyPanther Detections

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

• (Planned) It’s not possible to define new custom Lookup Tables.

  • It is possible to reference data from existing custom Lookup Tables, as well as Panther-managed Enrichment Providers.

• (Planned) No support for Policies, Scheduled Rules, Simple Rules, Correlation Rules.

  • Only rules (or “real-time rules”) are supported in the PyPanther framework.

• (Planned) It’s not possible to use Data Replay in the CLI or Console workflows.

• (Planned) It’s not possible to define your own data models.

  • It is possible to reference Panther-managed data models in the pypanther library, using event.udm().
• Library version pinning and declaring custom dependencies from the repository are not supported.

• The CLI tool used with PyPanther Detections, pypanther, does not have all of the commands panther_analysis_tool does.

  • See a list of available pypanther commands on Using the pypanther CLI Tool.

• There are additional limitations on the functionality of the upload command. See the upload limitations section.
• There are additional limitations when managing PyPanther Detections in the Panther Console. See the Console limitations section.

Getting started using PyPanther Detections

See the pypanther-starter-kit repository, 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 README.

Learn more about how to modify Panther-managed PyPanther Detections and create your own in the CLI workflow on Creating PyPanther Detections or in the Panther Console on Managing PyPanther Detections in the Panther Console.

Contribute to pypanther

The pypanther library is open source, and welcomes contributions. Please see CONTRIBUTING.md for contribution guidelines.