Custom Logs
Define, write, and manage custom schemas
Overview
Panther allows you to define your own custom log schemas. You can ingest custom logs into Panther via a Data Transport, and your custom schemas will then normalize and classify the data.
This page explains how to determine how many custom schemas you need, infer, write, and manage custom schemas, as well as how to upload schemas with Panther Analysis Tool (PAT). For information on how to use pantherlog to work with custom schemas, please see pantherlog CLI tool.
Custom schemas are identified by a Custom. prefix in their name and can be used wherever a natively supported log type is used:
Log ingestion
You can onboard custom logs through a Data Transport (e.g., HTTP webhook, S3, SQS, Google Cloud Storage, Azure Blob Storage)
Detections
You can write rules and scheduled rules for custom schemas.
Investigations
You can query the data in Search and in Data Explorer. Panther will create a new table for the custom schema once you onboard a source that uses it.
Determine how many custom schemas you need
There is no definitive rule for determining how many schemas you need to represent data coming from a custom source, as it depends on the intent of your various log events and the degree of field overlap between them.
In general, it's recommended to create the minimum number of schemas required for each log type's shape to be represented by its own schema (with room for some field variance between log types to be represented by the same schema). A rule of thumb is: if two different types of logs (e.g., application audit logs and security alerts) have less than 50% overlap in required fields, they should use different schemas.
In the table below, see example scenarios and their corresponding schema recommendations:
You have one type of log with fields A, B, and C, and a different type of log with fields X, Y, and Z.
Create two different schemas, one for each log type.
While it's technically possible to create one schema with all fields (A, B, C, X, Y, Z) marked as optional (i.e., required: false), it's not recommended, as downstream operations like detection writing and searching will be made more difficult.
You have one type of log that always has fields A, B, and C, and a different type of log that always has fields A, B, and Z.
Create one schema, with fields A and B marked as required and fields C and Z marked as optional.
After you have determined how many schemas you need, you can define them.
How to define a custom schema
There are multiple ways to define a custom schema. You can:
Infer one or more schemas from data: see Automatically infer the schema in Panther.
Create a schema manually: see Create the schema yourself.
Automatically infer the schema in Panther
Instead of writing a schema manually, you can let the Panther Console or the pantherlog CLI tool infer a schema (or multiple schemas) from your data.
When Panther infers a schema, note that if your data sample has:
A field of type
objectwith more than 200 fields, that field will be classified as typejson.A field with mixed data types (i.e., it is an array with multiple data types, or the field itself has varying data types), that field will be classified as type
json.
How to infer a schema
There are multiple ways to infer a schema in Panther:
In the Panther Console:
To infer a schema from sample data you've uploaded, see the Inferring a custom schema from sample logs tab, below.
To infer a schema from S3 data received in Panther, see the Inferring a custom schema from S3 data received in Panther tab, below.
To infer one or more schemas from historical S3 data, see the Inferring custom schemas from historical S3 data tab, below.
To infer a schema from HTTP data received in Panther, see the Inferring a custom schema from HTTP data received in Panther tab, below.
In the CLI workflow:
Use the
pantherlog infercommand.
Inferring a custom schema from sample logs
You can generate a schema by uploading sample logs into the Panther Console. If you'd like to use the command line instead, follow the instructions on using the pantherlog CLI tool here.
To get started, follow these steps:
Log in to your Panther Console.
On the left sidebar, navigate to Configure > Schemas.
At the top right of the page next to the search bar, click Create New.
Enter a Schema ID, Description, and Reference URL.
The Description is meant for content about the table, while the Reference URL can be used to link to internal resources.
Optionally enable Field Discovery by clicking its toggle
ON. Learn more on Field Discovery.In the Schema section, in the Infer a schema from sample events tile, click Start.
In the Infer schema from sample logs modal, click one of the radio buttons:
Upload Sample file: Upload a sample set of logs: Drag a file from your system over the pop-up modal, or click Select file and choose the log file.
Note that Panther does not support CSV without headers for inferring schemas.
Paste sample events(s): Directly paste or type sample events in the editor.
After uploading a file, Panther will display the raw logs in the UI. You can expand the log lines to view the entire raw log. Note that if you add another sample set, it will override the previously-uploaded sample.
Select the appropriate Stream Type (view examples for each type here).
Lines: Events are separated by a new line character.
JSON: Events are in JSON format.
JSON Array: Events are inside an array of JSON objects.
CloudWatch Logs: Events came from CloudWatch Logs.
XML: Events are in XML format.
Auto: Panther will automatically detect the appropriate stream type.
Click Infer Schema.
Panther will begin to infer a schema from the raw sample logs.
Panther will attempt to infer multiple timestamp formats.
Once the schema is generated, it will appear in the schema editor box.
To ensure the schema works properly against the sample logs you uploaded and against any changes you made to the schema, click Run Test.
This test will validate that the syntax of your schema is correct and that the log samples you have uploaded into Panther are successfully matching against the schema.
To see the test results, click View Events.
All successfully matched logs will appear under Matched; each log will display the column, field, and JSON view.
All unsuccessfully matched logs will appear under Unmatched; each log will display the error message and the raw log.
Click Save to publish the schema.
Inferring a custom schema from S3 data received in Panther
You can generate and publish a schema for a custom log source from live data streaming from an S3 bucket into Panther. You will first view your S3 data in Panther, then infer a schema, then test the schema.
View raw S3 data
After onboarding your S3 bucket into Panther, you can view raw data coming into Panther and infer a schema from it:
Follow the instructions to onboard an S3 bucket onto Panther without having a schema in place.
While viewing your log source's Overview tab, scroll down to the Attach a schema to start classifying data section.
Choose from the following options:
I want to add an existing schema: Choose this option if you already created a schema and you know the S3 prefix you want Panther to read logs from. Click Start in the tile.
You will see a S3 Prefixes & Schemas popup modal:
I want to generate a schema from raw events: Select this option to generate a schema from live data in this bucket and define which prefixes you want Panther to read logs from. Click Start in the tile.
Note that you may need to wait up to 15 minutes for data to start streaming into Panther.
On the page you are directed to, you can view the raw data Panther has received at the bottom of the screen:
This data is displayed from
data-archiver, a Panther-managed S3 bucket that retains raw logs for up to 15 days for every S3 log source.Only raw log events that were placed in the S3 bucket after you configured the source in Panther will be visible, even if you've set the timespan to look further back.
If your raw events are JSON-formatted, you can view them as JSON by clicking View JSON in the left-hand column.
Infer a schema from raw data
If you chose to I want to generate a schema from raw events in the previous section, now you can infer a schema.
Once you see data populating in Raw Events, you can filter the events you'd like to infer a schema from by using the string Search, S3 Prefix, Excluded Prefix, and/or Time Period filters at the top of the Raw Events section.
Click Infer Schema to generate a schema.
On the Infer New Schema modal that pops up, enter the following:
New Schema Name: The name of the schema that will map to the table in the data lake once the schema is published.
The name will always start with
Custom.and must have a capital letter after.
S3 Prefix: Use an existing prefix that was set up prior to inferring the schema or a new prefix.
The prefix you choose will filter data from the corresponding prefix in the S3 bucket to the schema you've inferred.
If you don't need to specify a specific prefix, you can leave this field empty to use the catch-all prefix that is called
*.
Click Infer Schema.
At the top of the page, you will see '<schema name>' was successfully inferred.
Click Done.
The schema will then be placed in a Draft mode until you're ready to publish to production after testing.
Review the schema and its fields by clicking its name.
Since the schema is in Draft, you can change, remove, or add fields as needed.
Test the schema with raw data
Once your schemas and prefixes are defined, you can proceed to testing the schema configuration against raw data.
In the Test Schemas section at the top of the screen, click Run Test.
On the Test Schemas modal that pops up, select the Time Period you would like to test your schema against, then click Start Test.
Depending on the time range and amount of data, the test may take a few minutes to complete.
Once the test is started, the results appear with the amount of matched and unmatched events.
Matched Events represent the number of events that would successfully classify against the schema configuration.
Unmatched Events represent the number of events that would not classify against the schema.
If there are Unmatched Events, inspect the errors and the JSON to decipher what caused the failures.
Click Back to Schemas, make changes as needed, and test the schema again.
Click Back to Schemas.
In the upper right corner, click Save.
The inferred schema is now attached to your log source.
Inferring custom schemas from historical S3 data
You can infer and save one or multiple schemas for a custom S3 log source from historical data in your S3 bucket (i.e., data that was added to the bucket before it was onboarded as a log source in Panther).
Prerequisite: Onboard your S3 bucket to Panther
Follow the instructions to onboard an S3 bucket onto Panther without having a schema in place.
If you have onboarded the S3 source with a custom IAM role, that role must have the
ListBucketpermission.
Step 1: View the S3 bucket structure in Panther
After creating your S3 bucket source in Panther, you can view your S3 bucket's structure and data in the Panther Console:
In the Panther Console, navigate to Configure > Log Sources. Click into your S3 log source.
In the log source's Overview tab, scroll down to the Attach a Schema to start classifying the data section.
On the right side of the I want to generate a schema from bucket data tile, click Start.
You will be redirected to a folder inspection of your S3 bucket. Here, you can view and navigate through all folders and objects in the S3 bucket.
Alternatively, you can access the folder inspection of your S3 bucket via the success page after onboarding your S3 source in Panther. From that page, click Attach or Infer Schemas.
Step 2: Navigate through your data
While viewing the folder inspection, click an object.
A slide-out panel will appear, displaying a preview of its events:
If the events fail to render correctly (either generating an error or displaying events improperly), it's possible the wrong stream type has been chosen for the S3 bucket source. If this is the case, click Selected Logs Format is n:
Step 3: Indicate if each folder has existing schema or a new one should be inferred
After reviewing what's included in your bucket, you can determine if one or multiple schemas is necessary to represent all of the bucket's data. Next, you can select folders that include data with distinct structures and either infer a new schema, or assign an existing one.
Determine whether one or more schemas will need to be inferred from the data in your S3 bucket.
If all data in the S3 bucket is of the same structure (and therefore can be represented by one schema), you can leave the default Infer New Schema option selected on the bucket level. This generates a single schema for all data in the bucket.
If the S3 bucket includes data that need to be classified in multiple schemas, follow the steps below for each folder in the bucket:
Select a folder and click Include.
Alternatively, if there is a folder or subfolder that you do not want Panther to process, select it and click Exclude.
If you have an existing schema that matches the data, click the Schema dropdown on the right side of the row, then select the schema:
By default, each newly included folder has the Infer New Schema option selected.
Click Infer
nSchemas.
Step 4: Wait for schemas to be inferred
The schema inference process may take up to 15 minutes. You can leave this page while the process completes. You can also stop this process early, and keep the schema(s) inferred during the time that the process ran.
Step 5: Review the results
After the inference process is complete, you can view the resulting schemas and the number of events that were used during each schema's inference. You can also validate how each schema parses raw events.
Click the play icon on the right side of each row.
Click the Events tab to see the raw and normalized events.
Click the Schema tab to see the generated schema.
Step 6: Name the schema(s) and save source
Before saving the source, name each of the newly inferred schemas with a unique name by clicking Add name.
After all new schemas have been named, you will be able to click Save Source in the upper right corner.
Inferring a custom schema from HTTP data received in Panther
You can generate and publish a schema for a custom log source from live data streaming from an HTTP (webhook) source into Panther. You will first view your HTTP data in Panther, then infer a schema, then test the schema.
View raw HTTP data
After creating your HTTP source in Panther, you can view raw data coming into Panther and infer a schema from it:
Follow the instructions to set up an HTTP log source in Panther.
Do not select a schema during HTTP source setup.
While viewing your log source's Overview tab, scroll down to the Attach a schema to start classifying data section.
Choose from the following options:
I want to add an existing schema: Choose this option if you already created a schema. Click Start in the tile.
You will be navigated to the HTTP source edit page, where you can make a selection in the Schemas - Optional field:
HTTP source edit page
I want to generate a schema: Select this option to generate a schema from live data. Click Start in the tile.
Note that you may need to wait a few minutes after
POSTing the events to the HTTP endpoint for them to be visible in Panther.On the page you are directed to, under Raw Events, you can view the raw data Panther has received within the last week:
HTTP Raw events This data is displayed from
data-archiver, a Panther-managed S3 bucket that retains raw HTTP source logs for 15 days.
Infer a schema from raw data
If you choose I want to generate a schema in the previous section, now you can infer a schema.
Once you see data populating within Raw Events, click Infer Schema.
On the Infer New Schema modal that pops up, enter the:
New Schema Name: Enter a descriptive name. It will always start with
Custom.and must have a capital letter after.
Click Infer Schema.
At the top of the page, you will see '<schema name>' was successfully inferred.
Click Done.
The schema will be placed in Draft mode until you're ready to publish it, after testing.
Click the draft schema's name to review its inferred fields.
Since the schema is in Draft, you can add, remove, and otherwise change fields as needed.
Test the schema with raw data
Once your schema is defined, you can proceed to test the schema configuration against raw data.
In the Test Schemas section at the top of the screen, click Run Test.
In the Test Schemas pop-up modal, select the Time Period you would like to test your schema against, then click Start Test.
Depending on the time range and amount of data, the test may take a few minutes to complete.
Once the test is started, the results appear with the amount of matched and unmatched events.
Matched Events represent the number of events that would successfully classify against the schema configuration.
Unmatched Events represent the number of events that would not classify against the schema.
If there are Unmatched Events, inspect the errors and the JSON to decipher what caused the failures.
Click Back to Schemas, make changes as needed, and test the schema again.
Click Back to Schemas.
In the upper right corner, click Save.
The inferred schema is now attached to your log source.
Log events that were sent to the HTTP source before it had a schema attached, which were used to infer the schema, are then ingested into Panther.
Create the schema yourself
How to create a custom schema manually
To create a custom schema manually:
In the left-hand navigation bar of your Panther Console, click Configure > Schemas.
In the upper right corner, click Create New.
Enter a Schema ID, Description, and Reference URL.
The Description is meant for content about the table, while the Reference URL can be used to link to internal resources.
Optionally enable Automatic Field Discovery by clicking its toggle
ON. Learn more on Field Discovery.In the Schema section, in the Create your schema from scratch tile, click Start.
The Schema section will default to using Separate Sections. If you'd like to write your entire schema in one editor window, click Single Editor.
In the Parser section, if your schema requires a parser other than the Default (JSON/XML) parser, select it. Learn more about the other parser options on the following pages:
In the Fields & Indicators section, write or paste your YAML log schema fields.
See Writing schemas to learn more about schema composition.
You can use Panther-generated schema field suggestions.
(Optional) In the Universal Data Model section, define Core Field mappings for your schema.
Learn more in Mapping Core Fields in Custom Log Schemas.
At the bottom of the window, click Run Test to verify your schema contains no errors.
Note that syntax validation only checks the syntax of the Log Schema. It can still fail to save due to name conflicts.
Click Save.
You can now navigate to Configure > Log Sources and add a new source or modify an existing one to use the new Custom.SampleAPI _Log Type. Once Panther receives events from this source, it will process the logs and store them in the custom_sampleapi table.
You can also now write detections to match against these logs and query them using Search or Data Explorer.
Writing schemas
See the tabs below to learn more about how to write a schema for JSON, XML, and text logs.
Writing a schema for JSON logs
To parse log files where each line is JSON, you must define a log schema that describes the structure of each log entry.
You can edit the YAML specifications directly in the Panther Console or they can be prepared offline in your editor/IDE of choice. For more information on the structure and fields in a Log Schema, see the Log Schema Reference.
It's also possible to use the starlark parser with JSON logs to perform transformations outside of those that are natively supported by Panther.
In the example schemas below, the first tab displays the JSON log structure and the second tab shows the Log Schema.
{
"method": "GET",
"path": "/-/metrics",
"format": "html",
"controller": "MetricsController",
"action": "index",
"status": 200,
"params": [],
"remote_ip": "1.1.1.1",
"user_id": null,
"username": null,
"ua": null,
"queue_duration_s": null,
"correlation_id": "c01ce2c1-d9e3-4e69-bfa3-b27e50af0268",
"cpu_s": 0.05,
"db_duration_s": 0,
"view_duration_s": 0.00039,
"duration_s": 0.0459,
"tag": "test",
"time": "2019-11-14T13:12:46.156Z"
}Minified JSON log example:
{"method":"GET","path":"/-/metrics","format":"html","controller":"MetricsController","action":"index","status":200,"params":[],"remote_ip":"1.1.1.1","user_id":null,"username":null,"ua":null,"queue_duration_s":null,"correlation_id":"c01ce2c1-d9e3-4e69-bfa3-b27e50af0268","cpu_s":0.05,"db_duration_s":0,"view_duration_s":0.00039,"duration_s":0.0459,"tag":"test","time":"2019-11-14T13:12:46.156Z"}
fields:
- name: time
description: Event timestamp
required: true
type: timestamp
timeFormats:
- rfc3339
isEventTime: true
- name: method
description: The HTTP method used for the request
type: string
- name: path
description: The path used for the request
type: string
- name: remote_ip
description: The remote IP address the request was made from
type: string
indicators: [ ip ] # the value will be appended to `p_any_ip_addresses` if it's a valid ip address
- name: duration_s
description: The number of seconds the request took to complete
type: float
- name: format
description: Response format
type: string
- name: user_id
description: The id of the user that made the request
type: string
- name: params
type: array
element:
type: object
fields:
- name: key
description: The name of a Query parameter
type: string
- name: value
description: The value of a Query parameter
type: string
- name: tag
description: Tag for the request
type: string
- name: ua
description: UserAgent header
type: strinllWriting a schema for XML logs
Panther intermediately parses XML logs into JSON, which means you can use all the tools available for JSON logs described in the JSON logs tab. Learn how Panther parses XML into JSON in XML stream type, then create your schema accordingly.
Note that because XML does not support data types other than strings, all values in the corresponding JSON representation will be depicted as strings (e.g., "ip": "192.168.1.100"). When defining your schema, you can use the appropriate types for each field, as seen in the Log schema example below.
Raw XML log:
<log>
<id>12345</id>
<timestamp>2023-11-14T13:12:46.156Z</timestamp>
<event type="security" priority="high">
<message>Unauthorized access attempt detected</message>
<source>
<ip>192.168.1.100</ip>
<user>admin</user>
</source>
<details>
<action>login_failed</action>
<reason>invalid_credentials</reason>
</details>
</event>
</log>How the raw XML log is converted into JSON:
{
"id": "12345",
"timestamp": "2023-11-14T13:12:46.156Z",
"event": {
"type": "security",
"priority": "high",
"message": "Unauthorized access attempt detected",
"source": {
"ip": "192.168.1.100",
"user": "admin"
},
"details": {
"action": "login_failed",
"reason": "invalid_credentials"
}
}
}How the log schema to parse this log would look:
fields:
- name: id
description: Unique log identifier
type: string
required: true
- name: timestamp
description: Event timestamp
type: timestamp
timeFormats:
- rfc3339
isEventTime: true
- name: event
description: Event details
type: object
fields:
- name: type
description: Type of event
type: string
- name: priority
description: Event priority level
type: string
- name: message
description: Event message
type: string
- name: source
description: Source information
type: object
fields:
- name: ip
description: Source IP address
type: string
indicators: [ ip ]
- name: user
description: Username
type: string
- name: details
description: Additional event details
type: object
fields:
- name: action
description: Action performed
type: string
- name: reason
description: Reason for action (if applicable)
type: stringWriting a schema for text logs
Panther handles logs that are not structured as JSON/XML by using a 'parser' that translates each log line into key/value pairs and feeds it as JSON to the rest of the pipeline. You can define a text parser using the parser field of the Log Schema. Panther provides the following parsers for non-JSON/XML formatted logs:
Name
Description
Match each line of text against one or more simple patterns
Use regular expression patterns to handle more complex matching, such as conditional fields, case-insensitive matching, etc.
Treat log files as CSV mapping column names to field names
Parse text logs, or perform transformations on json logs
Schema field suggestions
When creating or editing a custom schema, you can use field suggestions generated by Panther. To use this functionality:
In the Panther Console, click into the YAML schema editor.
To edit an existing schema, click Configure > Schemas > [name of schema you would like to edit] > Edit.
To create a new schema, click Configure > Schemas > Create New.
Press
Command+Ion macOS (orControl+Ion PC).The schema editor will display available properties and operations based on the position of the text cursor.
Managing custom schemas
Editing a custom schema
Panther allows custom schemas to be edited. Specifically, you can perform the following actions:
Add new fields.
Rename or delete existing fields.
Edit, add, or remove all properties of existing fields.
Modify the
parserconfiguration to fix bugs or add new patterns.
To edit a custom schema:
Navigate to your custom schema's details page in the Panther Console.
In the upper-right corner of the details page, click Edit.
Modify the schema as desired.
You can use Panther-generated schema field suggestions.
To more easily see your changes (or copy or revert deleted lines), click Single Editor, then Diff View.
In the upper-right corner, click Update.
Click Run Test to check the YAML for structural compliance. Note that the rules will only be checked after you click Update. The update will be rejected if the rules are not followed.
Update related detections and saved queries
Editing schema fields might require updates to related detections and saved queries. Click Related Detections in the alert banner displayed above the schema editor to view, update, and test the list of affected detections and saved queries.
Query implications
Queries will work across changes to a Type provided the query does not use a function or operator which requires a field type that is not castable across Types.
Good example: The Type is edited from
stringtointwhere all existing values are numeric (i.e."1"). A query using the functionsumaggregates old and new values together.Bad example: The Type is edited from
stringtointwhere some of the existing values are non-numeric (i.e."apples"). A query using the functionsumexcludes values that are non-numeric.
Query castability table
This table shows which Types can be cast as each Type when running a query. Schema editing allows any Type to be changed to another Type.
boolean
same
yes
yes
yes
no
no
string
yes
same
numbers only
numbers only
numbers only
numbers only
int
yes
yes
same
yes
yes
numbers only
bigint
yes
yes
yes
same
yes
numbers only
float
yes
yes
yes
yes
same
numbers only
timestamp
no
yes
no
no
no
same
Archiving and unarchiving a custom schema
You can archive and unarchive custom schemas in Panther. You might choose to archive a schema if it's no longer used to ingest data, and you do not want it to appear as an option in various dropdown selectors throughout Panther. In order to archive a schema, it must not be in use by any log sources. Schemas that have been archived still exist indefinitely; it is not possible to permanently delete a schema.
Archiving a schema does not affect any data ingested using that schema already stored in the data lake—it is still queryable using Data Explorer and Search. By default, archived schemas are not shown in the schema list view (visible on Configure > Schemas), but can be shown by modifying Status, within Filters, in the upper right corner. In Data Explorer, tables of archived schemas are not shown under Tables.
Attempting to create a new schema with the same name as an archived schema will result in a name conflict, and prompt you to instead unarchive and edit the existing schema.
To archive or unarchive a custom schema:
In the Panther Console, navigate to Configure > Schemas.
Locate the schema you'd like to archive or unarchive.
On the right-hand side of the schema's row, click the Archive or Unarchive icon.
If you are archiving a schema and it is currently associated to one or more log sources, the confirmation modal will prompt you to first detach the schema. Once you have done so, click Refresh.
On the confirmation modal, click Continue.
Testing a custom schema
To validate that a custom schema will work against your logs, you can test it against sample logs:
In the left-hand navigation bar in your Panther Console, click Configure > Schemas.
Click on a custom schema's name.
In the upper-right corner of the schema details page, click Test Schema.
Uploading log schemas with the Panther Analysis Tool
If you choose to maintain your log schemas outside of the Panther Console, perhaps to keep them under version control and review changes before updating, you can upload the YAML files programmatically with the Panther Analysis Tool (PAT).
The uploader command receives a base path as an argument and then proceeds to recursively discover all files with extensions .yml and .yaml.
panther_analysis_tool update-custom-schemas --path ./schemasThe uploader will check if an existing schema exists and proceed with the update or create a new one if no matching schema name is found.
The schemafield must always be defined in the YAML file and be consistent with the existing schema name for an update to succeed. For a list of all available CI/CD fields see our Log Schema Reference.
Schemas uploaded via PAT are validated against the same criteria as updates made in the Panther Console.
Troubleshooting custom logs
Visit the Panther Knowledge Base to view articles about custom log sources that answer frequently asked questions and help you resolve common errors and issues.
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