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.
Each log schema contains the following fields:
- The fields in each Log Event.
- Define a parser that will convert non-JSON logs to JSON.
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)
See the Custom Logs page for information on how to manage schemas through a CI/CD pipeline using Panther Analysis Tool (PAT).
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
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{}): Usefastmatchparserregex(RegexParser{}): Useregexparsercsv(CSVParser{}): Usecsvparser- Note: The
columnsfield is required when there are multiple CSV schemas in the same log source.
See the fields for
fastmatch, regex, and csv in the tabs below.fastmatch
regex
csv
match([]string): One or more patterns to match log lines against. This field cannot be empty.emptyValues([]string): Values to consider asnull.expandFields(map[string]string): Additional fields to be injected by expanding text templates.trimSpace(bool): Trim space surrounding each value.
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 asnull.expandFields(map[string]string): Additional fields to be injected by expanding text templates.trimSpace(bool): Trim space surrounding each value.
delimiter(string): A character to use as field delimiter.hasHeader(bool): Use first row to derive column names (unlesscolumnsis 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 asnull.trimSpace(bool): Trim space surrounding each value.expandFields(map[string]string): Additional fields to be injected by expanding text templates.
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.
- If present, the field's value will be copied from the referenced
object.
- If present, the field's name will be changed.
- If present, the field's value will be the combination of the values of two or more other fields.
- If present, the field's value will be extracted from another string field by splitting it based on a separator.
- If present, the field's value will be masked.
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) | An array of FieldSpec objects describing the fields of the object. | |
array | element (required) | A ValueSchema describing the elements of an array. | |
timestamp | timeFormats (required) | String | |
timestamp | isEventTime | Boolean | A flag to tell Panther to use this timestamp as the Log Event Timestamp. |
timestamp | isExpiration | Boolean | (For lookup tables only) A flag to tell Panther to ignore all events after this timestamp |
string | indicators | []String | |
string | validate | Validation rules for the string value |
The
timeFormats field was introduced in version 1.46 to support multiple timestamp formats in custom log schemas. While timeFormat is still supported for existing log sources, we recommend using timeFormats for all new schemas.Timestamps are defined by setting the
type field to timestamp and specifying the timestamp format using the timeFormats field. Timestamp formats can be any of the built-in timestamp formats:timeFormat | Example | Description |
|---|---|---|
rfc3339 | 2022-04-04Τ17:09:17Z | The most common timestamp format. |
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 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 11:12:13.123456789When 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 covers the cases where some 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.Schema test cases that are used with the
pantherlog test command must define the time field value in theresult payload formatted as YYYY-MM-DD HH:MM:SS.fffffffff. For backwards compatibility reasons, single time format configurations will retain the same format. Example:
- name: singleFormatTimestamp
type: timestamp
timeFormats:
- unix
input: >
{
"singleFormatTimestamp": "1666613239"
}
result: >
{
"singleFormatTimestamp": "1666613239"
}
When multiple time formats are defined:
- name: multipleFormatTimestamp
type: timestamp
timeFormats:
- unix
- rfc3339
input: >
{
"multipleFormatTimestamp": "1666613239"
}
result: >
{
"multipleFormatTimestamp": "2022-10-24 12:07:19.459326000"
}
Timestamp values in Lookup Table schemas can also be marked with
isExpiration: true. This is used to tell the Panther Rules Engine to ignore new data if the current time is after this timestamp. These can be useful to "time bound" alerts to independent indicators of compromise (IOCs) added via Lookup Tables, which make for richer alert context.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 parse the string and store any indicator values it finds to 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 ]
Values of
string type can be further restricted by declaring a list of values to allow or deny. This allows to have different log types that have common overlapping fields but differ on values of those fields.# Will only allow 'login' and 'logout' event types to match this log type
- name: event_type
type: string
validate:
allow: [ "login", "logout"]
# Will match any event type other than 'login' and 'logout'
- name: event_type
type: string
validate:
deny: [ "login", "logout"]
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. Note that the ip and cidr validation types can be combined with allow or deny rules but it 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"
If your editor or IDE supports JSON Schema, you can download the JSON Schema file for validation and auto-completion by clicking this link.
See the JetBrains documentation for instructions on how to configure JetBrains IDEs to use custom JSON Schemas.
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) |
|---|---|---|
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" } |
JSON Array | Events are inside an array of JSON objects. | [ { "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\"}" } ] } |
Last modified 1mo ago