version (0,required)0). Its purpose is to allow backwards compatibility with future versions of the log schema.fastmatch (FastmatchParser{}): Use fastmatch parserregex (RegexParser{}): Use regex parsercsv (CSVParser{}): Use csv parserfastmatch, regex, and csv in the tabs below.fastmatch fieldsmatch (required, []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.regex fieldsmatch (required, []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.csvdelimiter (required, 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.name (required, String) required (Boolean) description (String) ValueSchema defines a value and how it should be processed. Each ValueSchema has a type field that can be of the following values:stringint-2147483648, 2147483647smallint-32768, 32767bigint-9223372036854775808, 9223372036854775807floatbooleantrue / falsetimestamparrayobjectjsonValueSchema depend on the value of the type.objectfields (required)FieldSpec objects describing the fields of the object.timestampisEventTimeBooleantimestampisExpirationBooleantype field to timestamp and specifying the timestamp format using the timeFormat field. Timestamp formats can be one of the built-in timestamp formats:rfc3339unixunix_msunix_usunix_nsisEventTime: 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.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.string type can be used as indicators. In order to mark a field as an indicator, you must set the indicators field to an array of indicator scanner names. This will instruct Panther to parse the string and store any indicator values it finds to the relevant field. For example:indicators field (more than one may be used):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.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.isEmbeddedJSON: true flag. This flag is valid for values of type object, array and json.message as: