Log Schema Reference

In this guide, you will find common fields used to build YAML-based schemas when onboarding Custom Log Types and Lookup Table schemas.

Required fields throughout this page are in bold.

LogSchema fields

Each log schema contains the following fields:

fields ([]FieldSchema)
- The fields in each Log Event.
parser (ParserSpec)
- A parser that can convert non-JSON logs to JSON and/or perform custom transformations

CI/CD schema fields

Additionally, schemas defined using a CI/CD workflow can contain the following fields:

schema (string)
- The name of the schema
description (string)
- A short description that will appear in the UI
referenceURL (string)
- A link to an external document which specifies the log structure. Often, this is a link to a 3rd party's documentation.
fieldDiscoveryEnabled (boolean)
- Indicates whether field discovery will be enabled for this schema or not.

See the Custom Logs page for information on how to manage schemas through a CI/CD pipeline using Panther Analysis Tool (PAT).

Example

The example below contains the CI/CD fields mentioned above.

schema: Custom.MySchema
description: (Optional) A handy description so I know what the schema is for.
referenceURL: (Optional) A link to some documentation on the logs this schema is for.
fieldDiscoveryEnabled: true
parser:
  csv:
    delimiter: ','
    hasHeader: true
fields:
- name: action
  type: string
  required: true
- name: time
  type: timestamp
  timeFormats:
    - unix

ParserSpec

A ParserSpec specifies a parser to use to convert non-JSON input to JSON. Only one of the following fields can be specified:

fastmatch (FastmatchParser{}): Use fastmatch parser
- Learn more on Fastmatch Log Parser.
regex (RegexParser{}): Use regex parser
- Learn more on Regex Log Parser.
csv (CSVParser{}): Use csv parser
- Note: The columns field is required when there are multiple CSV schemas in the same log source.
- Learn more on CSV Log Parser.
script: Use script parser
- Learn more on Script Log Parser.

See the fields for fastmatch, regex, and csv in the tabs below.

Parser `fastmatch` fields

match ([]string): One or more patterns to match log lines against. This field cannot be empty.
emptyValues ([]string): Values to consider as null.
expandFields (map[string]string): Additional fields to be injected by expanding text templates.
trimSpace (bool): Trim space surrounding each value.

Parser `regex` fields

match ([]string): A pattern to match log lines against (can be split it into parts for documentation purposes). This field cannot be empty.
patternDefinitions (map[string]string): Additional named patterns to use in match pattern.
emptyValues ([]string): Values to consider as null.
expandFields (map[string]string): Additional fields to be injected by expanding text templates.
trimSpace (bool): Trim space surrounding each value.

Parser `csv` fields

delimiter (string): A character to use as field delimiter.
hasHeader (bool): Use first row to derive column names (unless columns is set also in which case the header is just skipped).
columns ([]string, required(without hasHeader), non-empty): Names for each column in the CSV file. If not set, the first row is used as a header.
emptyValues ([]string): Values to consider as null.
trimSpace (bool): Trim space surrounding each value.
expandFields (map[string]string): Additional fields to be injected by expanding text templates.

Parser `script` fields

function (string): The Starlark function to run per event

FieldSchema

A FieldSchema defines a field and its value. The field is defined by:

name (string)
- The name of the field.
required (boolean)
- If the field is required or not.
description (string)
- Some text documenting the field.
copy (object)
- If present, the field's value will be copied from the referenced object.
rename (object)
- If present, the field's name will be changed.
concat (object)
- If present, the field's value will be the combination of the values of two or more other fields.
split (object)
- If present, the field's value will be extracted from another string field by splitting it based on a separator.
mask (object)
- If present, the field's value will be masked.

Its value is defined using the fields of a ValueSchema.

ValueSchema

A ValueSchema defines a value and how it should be processed. Each ValueSchema has a type field that can be of the following values:

Type Values

Description

string

A string value

int

A 32-bit integer number in the range -2147483648, 2147483647

smallint

A 16-bit integer number in the range -32768, 32767

bigint

A 64-bit integer number in the range -9223372036854775808, 9223372036854775807

float

A 64-bit floating point number

boolean

A boolean value true / false

timestamp

A timestamp value

array

A JSON array where each element is of the same type

object

A JSON object of known keys

json

Any valid JSON value (JSON object, array, number, string, boolean)

The fields of a ValueSchema depend on the value of the type.

Type

Field

Value

Description

object

fields (required)

[]FieldSpec

An array of FieldSpec objects describing the fields of the object.

array

element (required)

ValueSchema

A ValueSchema describing the elements of an array.

timestamp

timeFormats (required)

[]String

An array specifying the formats to use for parsing the timestamp (see Timestamps)

timestamp

isEventTime

Boolean

A flag to tell Panther to use this timestamp as the Log Event Timestamp.

string

indicators

[]String

Tells Panther to extract indicators from this value (see Indicators)

string

validate

See Validate

Validation rules for the string value

Timestamps

Timestamps are defined by setting the type field to timestamp and specifying the timestamp format using the timeFormats field.

Panther always stores timestamp values in Universal Coordinated Time (UTC). This means:

If a timestamp field value indicates a timezone other than UTC (with a UTC offset), Panther converts it to UTC.
- For example, if an incoming timestamp field had a value of 2025-07-02T00:15:30-08:00 (where the -08:00 offset means it's in Pacific Standard Time [PST]), Panther will store it as 2025-07-02 08:15:30.000000000 (converted to UTC).
If a timestamp field value does not indicate a timezone, Panther assumes it is in UTC and stores it as-is.

See the allowed timeFormats values below:

timeFormats value

Example

Description

rfc3339

2022-04-04T17:09:17Z

The most common timestamp format.

unix_auto

1649097448 (seconds) 1649097491531 (milliseconds) 1649097442000000 (microseconds) 1649097442000000000 (nanoseconds)

Timestamp expressed in time passed since UNIX epoch time. It can handle seconds, milliseconds, microseconds, and nanoseconds.

unix

1649097448

Timestamp expressed in seconds since UNIX epoch time. It can handle fractions of seconds as a decimal part.

unix_ms

1649097491531

Timestamp expressed in milliseconds since UNIX epoch time.

unix_us

1649097442000000

Timestamp expressed in microseconds since UNIX epoch time.

unix_ns

1649097442000000000

Timestamp expressed in nanoseconds since UNIX epoch time. Scientific float notation is supported.

The timeFormats field was introduced in Panther v1.46 to support multiple timestamp formats in custom log schemas. While timeFormat is still supported for log sources set up before v1.46, use timeFormats for all new schemas.

Defining a custom format

You can also define a custom format by using strftime notation. For example:

# The field is a timestmap using a custom timestamp format like "2020-09-14 14:29:21"
- name: ts
  type: timestamp
  timeFormats:
    - "%Y-%m-%d %H:%M:%S" # note the quotes required for proper YAML syntax

Panther's strftime format supports using %N code to parse nanoseconds. For example:

%H:%M:%S.%N can be used to parse 11:12:13.123456789

Using multiple time formats

When multiple time formats are defined, each of them will be tried sequentially until successful parsing is achieved:

- name: ts
  type: timestamp
  timeFormats:
    - rfc3339
    - unix

Timestamp values can be marked with isEventTime: true to tell Panther that it should use this timestamp as the p_event_time field. It is possible to set isEventTime on multiple fields. This may be useful in situations where logs have optional or mutually exclusive fields holding event time information. Since there can only be a single p_event_time for every log event, the priority is defined using the order of fields in the schema.

Working with `timeFormats` in schema tests

When writing schema tests to be run with the pantherlog test command:

If your schema field has a single timeFormats value, for backwards compatibility, configurations will retain the same format.
If your schema field has multiple timeFormats values, you must define the timestamp field value in the result payload formatted as YYYY-MM-DD HH:MM:SS.fffffffff.

Example with a single timeFormats value:

- name: singleFormatTimestamp
  type: timestamp
  timeFormats:
    - unix

input: >
  {
    "singleFormatTimestamp": "1666613239"
  }
result: >
  {
    "singleFormatTimestamp": "1666613239"
  }

Example with multiple timeFormats values:

- name: multipleFormatTimestamp
  type: timestamp
  timeFormats:
    - unix
    - rfc3339

input: >
  {
    "multipleFormatTimestamp": "1666613239"
  }
result: >
  {
    "multipleFormatTimestamp": "2022-10-24 12:07:19.459326000"
  }

Indicators

Values of string type can be used as "indicators." To mark a field as an indicator, set the indicators field to an array of indicator scanner names (more than one may be used). This will instruct Panther to store the value of this field in the relevant p_any_ field.

For a list of values that are valid to use in the indicators field, see Standard Fields.

For example:

# Will scan the value as IP address and store it to `p_any_ip_addresses`
- name: remote_ip
  type: string
  indicators: [ ip ]

# Will scan the value as a domain name and/or IP address.
# Will store the result in `p_any_domain_names` and/or `p_any_ip_addresses`
- name: target_url
  type: string
  indicators: [ url ]

Validate

Under the validate key, you can specify conditions for the field's value that must be met in order for an incoming log to match this schema.

It's also possible to use validate on the element key (where type: string) to perform validation on each element of an array value.

`allow` and `deny` validation

You can validate values of string type by declaring an allowlist or denylist. Only logs with field values that match (or do not match) the values in allow/deny will be parsed with this schema. This means you can have multiple log types that have common overlapping fields but differ on values of those fields.

# Will only allow 'login' and 'logout' event_type values to match this log type
- name: event_type
  type: string
  validate:
    allow: [ "login", "logout"]
    
# Will match if log has any event_type value other than 'login' and 'logout'
- name: event_type
  type: string
  validate:
    deny: [ "login", "logout"]
    
# Will match logs with a severities field with value 'info' or 'low' 
- name: severities
  type: array
  element:
    type: string
    validate:
      allow: ["info", "low"]

`ip` and `cidr` format validation

Values of string type can be restricted to match well-known formats. Currently, Panther supports the ip and cidr formats to require that a string value be a valid IP address or CIDR range.

ip and cidr validation can be combined with allow or deny rules but doing so is somewhat redundant—for example, if you allow two IP addresses, then adding an ip validation will simply ensure that your validation will not include false positives if the IP addresses in your list are not valid.

# Will allow valid ipv4 IP addresses e.g. 100.100.100.100
- name: address
  type: string
  validate:
    ip: "ipv4"
    
# Will allow valid ipv6 CIDR ranges 
# e.g. 2001:0db8:85a3:0000:0000:0000:0000:0000/64
- name: address
  type: string
  validate:
    cidr: "ipv6"
    
# Will allow any valid ipv4 or ipv6 address
- name: address
  type: string
  validate:
    ip: "any"
    
# All elements of the addresses array must be valid ipv4 ID addresses
- name: addresses
  type: array
  element:
    type: string
    validate:
      ip: "ipv4"

Using JSON schema in an IDE

If your code editor or integrated development environment (IDE) supports JSON Schema, you can configure it to use this schema file for Panther schemas and this schema-tests file for schema tests. Doing so will allow you to receive suggestions and error messages while developing Panther schemas and their tests.

JetBrains custom JSON schemas

See the JetBrains documentation for instructions on how to configure JetBrains IDEs to use custom JSON Schemas.

VSCode custom JSON schemas

See the VSCode documentation for instructions on how to configure VSCode to use JSON Schemas.

Stream type

While performing certain actions in the Panther Console, such as configuring an S3 bucket for Data Transport or inferring a custom schema from raw logs, you need to select a log stream type.

View example log events for each type below.

Stream type

Description

Example log event(s)

Auto

Panther will automatically detect the appropriate stream type.

n/a

Lines

Events are separated by a new line character.

"10.0.0.1","[email protected]","France"
"10.0.0.2","[email protected]","France"
"10.0.0.3","[email protected]","France"

JSON

Events are in JSON format.

{ 
    "ip": "10.0.0.1", 
    "un": "[email protected]", 
    "country": "France" 
}
OR
{ "ip": "10.0.0.1", "un": "[email protected]", "country": "France" }{ "ip": "10.0.0.2", "un": "[email protected]", "country": "France" }{ "ip": "10.0.0.3", "un": "[email protected]", "country": "France" }
OR
{ "ip": "10.0.0.1", "un": "[email protected]", "country": "France" }
{ "ip": "10.0.0.2", "un": "[email protected]", "country": "France" }
{ "ip": "10.0.0.3", "un": "[email protected]", "country": "France"OR

JSON Array

Events are inside an array of JSON objects.

OR Events are inside an array of JSON objects, which is the value to a key in a top-level object. This can be known as an "enveloped array."

[
	{ "ip": "10.0.0.1", "username": "[email protected]", "country": "France" },
	{ "ip": "10.0.0.2", "username": "[email protected]", "country": "France" },
	{ "ip": "10.0.0.3", "username": "[email protected]", "country": "France" }
]
OR
{ "events": [
        { "ip": "10.0.0.1", "username": "[email protected]", "country": "France" },
	{ "ip": "10.0.0.2", "username": "[email protected]", "country": "France" },
	{ "ip": "10.0.0.3", "username": "[email protected]", "country": "France" }
    ] 
}

CloudWatch Logs

Events came from CloudWatch Logs.

{
  "owner": "111111111111",
  "logGroup": "services/foo/logs",
  "logStream": "111111111111_CloudTrail/logs_us-east-1",
  "messageType": "DATA_MESSAGE",
  "logEvents": [
      {
          "id": "31953106606966983378809025079804211143289615424298221568",
          "timestamp": 1432826855000,
          "message": "{\"ip\": \"10.0.0.1\", \"user\": \"[email protected]\", \"country\": \"France\"}"
      },
      {
          "id": "31953106606966983378809025079804211143289615424298221569",
          "timestamp": 1432826855000,
          "message": "{\"ip\": \"10.0.0.2\", \"user\": \"[email protected]\", \"country\": \"France\"}"
      },
      {
          "id": "31953106606966983378809025079804211143289615424298221570",
          "timestamp": 1432826855000,
          "message": "{\"ip\": \"10.0.0.3\", \"user\": \"[email protected]\", \"country\": \"France\"}"
      }
  ]
}

XML (Beta)

Events are in XML format. Learn more about XML is parsed in XML stream type.

<log>
    <id>1</id>
    <data>first log</data>
</log>
<log>
    <id>2</id>
    <data>second log</data>
</log>

XML stream type

The XML stream type is in open beta starting with Panther version 1.114, and is available to all customers. Please share any bug reports and feature requests with your Panther support team.

When parsing XML log events, Panther converts XML elements to JSON objects—at a high level, element names are turned into keys and text content becomes values. Learn more about how to create a custom schema for XML logs here.

In greater detail, here is how Panther processes an XML file:

Each top-level XML element is processed as a separate event.
- Nested elements are enclosed in a nested object.
Element names become field names.
- If multiple elements in the same level of nesting have the same name, an array field is created where the key is the shared element name, and the value is an array of the elements' contents (i.e., text content, attributes, nested fields, etc.).
Text content becomes field values.
- If an element has only text content (and no attributes nor nested elements), the text content is parsed directly as the field value.
- If an element has both 1) text content and 2) at least one attribute or nested element, the text content is stored as the value of a text key.
- If an element is empty (i.e., has no text content), it's given a value of null.
- If text content is broken up by one or more elements, it is concatenated with a space between each part.
Element attributes (e.g., <User role="admin">) are added as key/value pairs alongside text content in a shared nested object.
- If an attribute name conflicts with the name of a nested element (which will become a field name in the resulting nested object, like the attribute name), the attribute name is given an _attr suffix. If the attribute name is text and the element has text content (which will generate a nested text key), the attribute field will become text_attr.
- The xmlns attribute (declaring an XML namespace) is automatically skipped.

Example XML input:

<log>
    <id>1</id>
    <data>first log</data>
    <user></user>
</log>
<log>
    <id>2</id>
    <data level="info">second log</data>
</log>
<log>
    <id>3</id>
    <data>
        text before element
        <source>app</source>
        text after element
    </data>
</log>
<log>
    <data>fourth log
        <severity text="sev">high</severity>
    </data>
</log>
<log>
    <data id="123" name="John">
        <name>John1</name>
        <name>John2</name>
    </data>
</log>

Panther processes as:

{"id":"1","data":"first log","user":null}
{"id":"2","data":{"text":"second log","level":"info"}}
{"id":"3","data":{"text":"text before element text after element","source":"app"}}
{"data":{"text":"fourth log","severity":{"text_attr":"sev","text":"high"}}}
{"data":{"id":"123","name_attr":"John","name":["John1","John2"]}}

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LogSchema fields

CI/CD schema fields

Example

ParserSpec

Parser fastmatch fields

Parser regex fields

Parser csv fields

Parser script fields

FieldSchema

ValueSchema

Timestamps

Defining a custom format

Using multiple time formats

Working with timeFormats in schema tests

Indicators

Validate

allow and deny validation

ip and cidr format validation

Using JSON schema in an IDE

JetBrains custom JSON schemas

VSCode custom JSON schemas

Stream type

XML stream type

Parser `fastmatch` fields

Parser `regex` fields

Parser `csv` fields

Parser `script` fields

Working with `timeFormats` in schema tests

`allow` and `deny` validation

`ip` and `cidr` format validation