Rules and Scheduled Rules

Rules and scheduled rules detect suspicious activity in logs, then generate alerts

Overview

Rules and scheduled rules are segments of logic through which log data is run to detect suspicious activity and generate signals (and optionally alerts). Rules analyze real-time events, while scheduled rules analyze events queried from your data lake. These detection types differ from policies, which apply to cloud resource configurations.

Rules can be created in the Console using the Simple Detection builder or by writing Python; in the CLI workflow, rules can be written as Simple Detections (in YAML) or in Python. Scheduled rules can be written in Python (in the Console or CLI workflow).

Rules can use inheritance, which allows you to created one or more Derived Detections from a single Base Detection.

If you'd like to create rules or scheduled rules for actions that aren't worthy—at least on their own—of generating an alert, you can configure them to not generate alerts (only generating signals). You may then want to include those rules in a correlation rule.

Panther provides a number of Panther-managed rules and scheduled rules in Python, which are already written and continuously updated. Rules can use derivation, which allows you to create one or more Derived Detections from a single Base Detection. You can also convert Sigma rules into Panther rules.

Common examples of rules include analyzing logs for:

• Authentication from unknown or unexpected locations

• Sensitive API calls, such as administrative changes to SaaS services

• Network traffic access to sensitive data sources, such as databases or virtual machines

• New, suspicious entries added into a system's scheduled tasks, like cron

• Alerts generated from NIDS, HIDS, or other security systems

Rules vs. scheduled rules

Both rules and scheduled rules define logic through which log events are run—but rules analyze real-time events, while scheduled rules analyze events queried from your data lake.

• Rules

  • Rules are the default mechanism of analyzing data sent to Panther. Rules work by accepting a defined set of log types such as one or more of the Supported Logs sources, or your own custom data. Rules have the benefit of low-latency detection and alerting.

  • Use cases: High-signal logs that do not require joins with other data.

  • Rules may also be known as streaming rules, real-time rules, and low-latency rules.

• Scheduled rules

  • Scheduled rules work by accepting individual rows output from an associated Scheduled Search.

  • Use cases: Searching windows of time further in the past, running statistical analysis over data, or joining separate data streams.

Using Python vs. Simple Detections YAML

If you are writing rules locally, in the CLI workflow, you can create them as Python or Simple Detections. (Scheduled rules can only be written in Python.) There are advantages to both methods of rule creation, outlined below.

• Write rules in Python when:

  • The complexity of the logic is very high, or the detection requires any of the features currently listed as limitations of Simple Detections YAML.

• Write rules as Simple Detections when:

  • You'd like to promote collaboration between team members with differing levels of technical skill. When Simple Detections written in the CLI workflow are uploaded to Panther, they're represented in the Console in the Simple Detection builder. You can then edit (or create new) detections in the Console using the Simple Detection builder.

For examples of the same detection logic written in both Python and YAML, see Rule examples, below.

How rules and scheduled rules work

Rules and scheduled rules each analyze one event at a time. They use event thresholds and de-duplication to create event grouping within windows of time.

At a minimum, each rule and scheduled rule must define rule logic. For Python detections, this means they each must contain a rule() function; for Simple Detections written in the CLI workflow, each must contain a Detection key with at least one match expression.

Python rules can also define dynamic alert functions, and Simple Detections can use dynamic alert keys. Both can use Inline Filters, which run before detection logic.

Rules cannot run longer than 15 seconds. If this happens, a rule error is generated and processing is terminated. (This constraint does not apply to scheduled rules.)

Matches on rules and scheduled rules generate signals. When rules and scheduled rules have alerting enabled, rule matches are generated, which can create alerts, according to event threshold and deduplication configurations. Learn more about the difference between signals, rule matches, and alerts here.

Title of associated alerts

When a detection has alerting enabled, the title of a resulting alert is determined by that detection's configuration. When a separate deduplication string is not explicitly set, the alert title is also used to deduplicate alerts.

How the alert title is set

The order of precedence for setting the alert title is as follows:

Python

Alert title precedence in Python:

1. The output of the dynamic title() function is used.

2. If title() is not defined, the value of the detection's display name is used. This is defined:

   • In the Console: in the Name field

   • In the CLI workflow: in the DisplayName field in the YAML file

3. If there is no display name defined, the detection's ID is used. This is defined:

   • In the Console: in the ID field

   • In the CLI workflow: in theRuleID or PolicyID field in the YAML file

Simple Detections

Alert title precedence in Simple Detections YAML:

1. The dynamic alert title is used. This is defined:

   • In the Console: in the Optional Fields > Default title is > Change to field

   • In the developer workflow: in the AlertTitle field

2. If a dynamic alert title is not defined, the value of the detection's display name is used. This is defined:

   • In the Console: in the Name field

   • In the developer workflow: in the DisplayName field

3. If there is no display name defined, the detection's ID is used. This is defined:

   • In the Console: in the ID field

   • In the developer workflow: in theRuleID or PolicyID field

Deduplication of alerts

Events triggering the same detection within its deduplication period that also share a deduplication string will be grouped together in a single alert.

Each rule and scheduled rule has a default event threshold of 1 and deduplication period of 1h. This means all events returning True from the rule function (with the same deduplication string) will be grouped into a single alert within the hour after first being generated.

A rule or scheduled rule with an event threshold of 5 and deduplication period of 15m would not trigger an alert until five or more events (with the same deduplication string) passed into the rule function returned True within a 15-minute time period.

If your deduplication threshold is greater than one (meaning multiple events must match before an alert is generated), the first matching event will be used as the event parameter to alert functions in Python detections and alert keys in Simple Detections.

Deduplication period

The detection editor in the Panther Console supports a maximum deduplication period of 24 hours. If you upload your detections via the bulk uploader or the Panther Analysis Tool (PAT), there is no limit on the value of DedupPeriodMinutes.

The deduplication period is not affected by changing the status of an alert. This means, for example, events will continue to be grouped into the same alert for the length of the deduplication period even if an alert's status is changed to Resolved.

How the deduplication string is set

The order of precedence for setting the deduplication string is as follows:

Python

Deduplication string precedence in Python:

1. The output of the dynamic dedup() function is used.

2. Ifdedup() is not defined, the alert title is used. The alert title is defined by the order of precedence defined here.

Simple Detections

Deduplication string precedence in Simple Detections YAML:

1. The value of the GroupBy key is used.

2. If GroupBy is not present, the alert title is used. The alert title is defined by the order of precedence defined here.

Which method you use to set the deduplication string depends on how granularly you want to group alerts:

• If you only want alerts generated by a certain detection to be grouped separately from alerts from other detections, using the detection's display name (given that the display name is unique) or ID as the deduplication string will suffice.

• If you want alerts generated by a certain detection to be grouped separately not only from alerts generated by other detections, but also from alerts triggered by the same detection where one or more event fields have different values, you can use title() or AlertTitle (for Python and Simple Detections, respectively) to dynamically set the alert title and therefore, the deduplication string.

• If you want to group alerts generated by a certain detection distinctly from alerts generated by the same detection where one or more event field values differ and you want to group the alerts based on one or more event fields that weren't used in title() or AlertTitle (or if you didn't set title() or AlertTitle at all), you can use dedup() or GroupBy (for Python and Simple Detections, respectively) to set the deduplication string.

How to write rules and scheduled rules

You can write rules in the Panther Console (using the Simple Detection builder or Python) or locally (in Simple Detections YAML or Python); scheduled rules can be written in Python (in the Panther Console or locally).

Before you start writing a new rule, check to see if there's an existing Panther-managed rule that meets your needs. If you (or Panther) has already created a rule similar to the one you want to build, consider creating a Derived Detection.

Writing detections locally means creating Python and/or YAML files that define a Panther detection on your own machine. After writing detections locally, you upload the files to your Panther instance, typically via PAT.

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.

How to write rules

These instructions outline how to set up real-time rules. To configure a scheduled rule, see How to write scheduled rules.

Follow the below instructions to learn how to write rules:

• In Python (in either the Console, or the CLI workflow)

• In the Simple Detection builder in the Console

• As a Simple Detection in the CLI workflow

How to write scheduled rules

Scheduled rules are associated with one or more Scheduled Searches. If you have not yet created a scheduled search, follow the How to create a Saved and Scheduled Search instructions first, then return to these instructions to create the scheduled rule.

If the Scheduled Search returns multiple rows, each row is processed by the rule logic as a separate event. The number of alerts triggered depends on the deduplication settings you've configured on the scheduled rule.

We recommend doing as much data processing as is possible in SQL (i.e., in the Scheduled Search) in order to take advantage of database optimizations and improve rule performance.

Follow the below instructions to learn how to write scheduled rules:

• In Python (in either the Console, or the CLI workflow)

Rule errors and scheduled rule errors

Rule errors and scheduled rule errors are types of detection errors generated when:

• A detection's code raises an exception.

• Python processing of a rule or Scheduled Rule runs longer than 15 seconds.

If no alert destination is configured to receive rule errors (or scheduled rule errors), the alert for a rule error (or scheduled rule error) will be routed to the same destination as the one that would be used for that rule or scheduled rule's alert. See Routing order precedence on Alert Destinations for more information.

In the event of a Scheduled Search timeout, a System Error is generated.

Rule examples

See templates for rules and scheduled rules in the panther-analysis GitHub repository.

For in-depth information on how to write detections, see Writing Python Detections and Writing Simple Detections.

Auth0 multi-factor authentication policy disabled

See how one detection is represented in both Python and as a Simple Detection:

Python

Auth0 multi-factor authentication policy disabled detection in Python

View this detection in full in the panther-analysis repository in GitHub.

from global_filter_auth0 import filter_include_event
from panther_auth0_helpers import auth0_alert_context, is_auth0_config_event
from panther_base_helpers import deep_get


def rule(event):
    if not filter_include_event(event):
        return False
    data_description = deep_get(event, "data", "description", default="<NO_DATA_DESCRIPTION_FOUND>")
    request_path = deep_get(
        event, "data", "details", "request", "path", default="<NO_REQUEST_PATH_FOUND>"
    )
    request_body = deep_get(event, "data", "details", "request", "body", default=[-1])
    return all(
        [
            data_description == "Set the Multi-factor Authentication policies",
            request_path == "/api/v2/guardian/policies",
            request_body == [],
            is_auth0_config_event(event),
        ]
    )

Simple Detection

Auth0 multi-factor authentication policy disabled detection in Simple Detection YAML

Detection:
  - DeepKey:
      - data
      - description
    Condition: Equals
    Value: Set the Multi-factor Authentication policies
  - DeepKey:
      - data
      - details
      - request
      - path
    Condition: Equals
    Value: /api/v2/guardian/policies
  - DeepKey:
      - data
      - details
      - request
      - body
    Condition: IsNullOrEmpty
  - DeepKey:
      - data
      - details
      - request
      - channel
    Condition: Equals
    Value: 'https://manage.auth0.com/'

Detect and alert when an admin panel is accessed on a web server

As an example, let's write a rule to send an alert when an admin panel is accessed on a web server.

Take the following NGINX log:

{
  "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"
}

We want to create a detection that:

• Looks for 200 (OK) web requests to any URL containing "admin-panel"

• Generates an an alert title that says there has been successful admin panel logins from a specific IP address

• Deduplicates events based on IP address

Python

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')

Simple Detection

Detection:
  - KeyPath: status
    Condition: Equals
    Value: 200
  - KeyPath: request
    Condition: Contains
    Value: 'admin-panel'

AlertTitle: "Successful admin panel login detected from {remoteAddr}"

GroupBy:
  - KeyPath: remoteAddr

Then, the following would occur:

1. An alert would be generated and sent to the set of associated destinations, which by default are based on the rule severity.

2. The alert's title would say, "Successful admin panel login detected from 180.76.15.143".

3. Similar events with the same deduplication string of 180.76.15.143 would be appended to the same alert.

   • A unique alert will be generated for each unique deduplication string, which in this case, is the IP of the requestor.

4. The recipient of the alert could then log into Panther to view all alert metadata and a summary of the events, as well as execute searches across all events to perform additional analysis.

Reference

Alert severity

We recommend following these guidelines to define alert severity levels:

Severity	Exploitability	Description	Examples
Info	None	No risk, simply informational	Gaining operational awareness.
Low	Difficult	Little to no risk if exploited	Non-sensitive information leaking such as system time and OS versions.
Medium	Difficult	Moderate risk if exploited	Expired credentials, missing protection against accidental data loss, encryption settings, best practice settings for audit tools.
High	Moderate	Very damaging if exploited	Large gaps in visibility, directly vulnerable infrastructure, misconfigurations directly related to data exposure.
Critical	Easy	Causes extreme damage if exploited	Public data/systems available, leaked access keys.