Creates a DeidentifyTemplate for reusing frequently used configuration for de-identifying content, images, and storage. See https://cloud.google.com/dlp/docs/creating-templates-deid to learn more.
Auto-naming is currently not supported for this resource.
Creates a new job to inspect storage or calculate risk metrics. See https://cloud.google.com/dlp/docs/inspecting-storage and https://cloud.google.com/dlp/docs/compute-risk-analysis to learn more. When no InfoTypes or CustomInfoTypes are specified in inspect jobs, the system will automatically choose what detectors to run. By default this may be all types, but may change over time as detectors are updated.
Auto-naming is currently not supported for this resource.
An auxiliary table contains statistical information on the relative frequency of different quasi-identifiers values. It has one or several quasi-identifiers columns, and one column that indicates the relative frequency of each quasi-identifier tuple. If a tuple is present in the data but not in the auxiliary table, the corresponding relative frequency is assumed to be zero (and thus, the tuple is highly reidentifiable).
An auxiliary table contains statistical information on the relative frequency of different quasi-identifiers values. It has one or several quasi-identifiers columns, and one column that indicates the relative frequency of each quasi-identifier tuple. If a tuple is present in the data but not in the auxiliary table, the corresponding relative frequency is assumed to be zero (and thus, the tuple is highly reidentifiable).
A pattern to match against one or more tables, datasets, or projects that contain BigQuery tables. At least one pattern must be specified. Regular expressions use RE2 syntax; a guide can be found under the google/re2 repository on GitHub.
A pattern to match against one or more tables, datasets, or projects that contain BigQuery tables. At least one pattern must be specified. Regular expressions use RE2 syntax; a guide can be found under the google/re2 repository on GitHub.
Message defining the location of a BigQuery table. A table is uniquely identified by its project_id, dataset_id, and table_name. Within a query a table is often referenced with a string in the format of: :. or ...
Message defining the location of a BigQuery table. A table is uniquely identified by its project_id, dataset_id, and table_name. Within a query a table is often referenced with a string in the format of: :. or ...
Generalization function that buckets values based on ranges. The ranges and replacement values are dynamically provided by the user for custom behavior, such as 1-30 -> LOW 31-65 -> MEDIUM 66-100 -> HIGH This can be used on data of type: number, long, string, timestamp. If the bound Value type differs from the type of data being transformed, we will first attempt converting the type of the data to be transformed to match the type of the bound before comparing. See https://cloud.google.com/dlp/docs/concepts-bucketing to learn more.
Generalization function that buckets values based on ranges. The ranges and replacement values are dynamically provided by the user for custom behavior, such as 1-30 -> LOW 31-65 -> MEDIUM 66-100 -> HIGH This can be used on data of type: number, long, string, timestamp. If the bound Value type differs from the type of data being transformed, we will first attempt converting the type of the data to be transformed to match the type of the bound before comparing. See https://cloud.google.com/dlp/docs/concepts-bucketing to learn more.
Partially mask a string by replacing a given number of characters with a fixed character. Masking can start from the beginning or end of the string. This can be used on data of any type (numbers, longs, and so on) and when de-identifying structured data we'll attempt to preserve the original data's type. (This allows you to take a long like 123 and modify it to a string like **3.
Partially mask a string by replacing a given number of characters with a fixed character. Masking can start from the beginning or end of the string. This can be used on data of any type (numbers, longs, and so on) and when de-identifying structured data we'll attempt to preserve the original data's type. (This allows you to take a long like 123 and modify it to a string like **3.
Message representing a set of files in a Cloud Storage bucket. Regular expressions are used to allow fine-grained control over which files in the bucket to include. Included files are those that match at least one item in include_regex and do not match any items in exclude_regex. Note that a file that matches items from both lists will not be included. For a match to occur, the entire file path (i.e., everything in the url after the bucket name) must match the regular expression. For example, given the input {bucket_name: "mybucket", include_regex: ["directory1/.*"], exclude_regex: ["directory1/excluded.*"]}: * gs://mybucket/directory1/myfile will be included * gs://mybucket/directory1/directory2/myfile will be included (.* matches across /) * gs://mybucket/directory0/directory1/myfile will not be included (the full path doesn't match any items in include_regex) * gs://mybucket/directory1/excludedfile will not be included (the path matches an item in exclude_regex) If include_regex is left empty, it will match all files by default (this is equivalent to setting include_regex: [".*"]). Some other common use cases: * {bucket_name: "mybucket", exclude_regex: [".*\.pdf"]} will include all files in mybucket except for .pdf files * {bucket_name: "mybucket", include_regex: ["directory/[^/]+"]} will include all files directly under gs://mybucket/directory/, without matching across /
Message representing a set of files in a Cloud Storage bucket. Regular expressions are used to allow fine-grained control over which files in the bucket to include. Included files are those that match at least one item in include_regex and do not match any items in exclude_regex. Note that a file that matches items from both lists will not be included. For a match to occur, the entire file path (i.e., everything in the url after the bucket name) must match the regular expression. For example, given the input {bucket_name: "mybucket", include_regex: ["directory1/.*"], exclude_regex: ["directory1/excluded.*"]}: * gs://mybucket/directory1/myfile will be included * gs://mybucket/directory1/directory2/myfile will be included (.* matches across /) * gs://mybucket/directory0/directory1/myfile will not be included (the full path doesn't match any items in include_regex) * gs://mybucket/directory1/excludedfile will not be included (the path matches an item in exclude_regex) If include_regex is left empty, it will match all files by default (this is equivalent to setting include_regex: [".*"]). Some other common use cases: * {bucket_name: "mybucket", exclude_regex: [".*\.pdf"]} will include all files in mybucket except for .pdf files * {bucket_name: "mybucket", include_regex: ["directory/[^/]+"]} will include all files directly under gs://mybucket/directory/, without matching across /
The field type of value and field do not need to match to be considered equal, but not all comparisons are possible. EQUAL_TO and NOT_EQUAL_TO attempt to compare even with incompatible types, but all other comparisons are invalid with incompatible types. A value of type: - string can be compared against all other types - boolean can only be compared against other booleans - integer can be compared against doubles or a string if the string value can be parsed as an integer. - double can be compared against integers or a string if the string can be parsed as a double. - Timestamp can be compared against strings in RFC 3339 date string format. - TimeOfDay can be compared against timestamps and strings in the format of 'HH:mm:ss'. If we fail to compare do to type mismatch, a warning will be given and the condition will evaluate to false.
The field type of value and field do not need to match to be considered equal, but not all comparisons are possible. EQUAL_TO and NOT_EQUAL_TO attempt to compare even with incompatible types, but all other comparisons are invalid with incompatible types. A value of type: - string can be compared against all other types - boolean can only be compared against other booleans - integer can be compared against doubles or a string if the string value can be parsed as an integer. - double can be compared against integers or a string if the string can be parsed as a double. - Timestamp can be compared against strings in RFC 3339 date string format. - TimeOfDay can be compared against timestamps and strings in the format of 'HH:mm:ss'. If we fail to compare do to type mismatch, a warning will be given and the condition will evaluate to false.
Pseudonymization method that generates deterministic encryption for the given input. Outputs a base64 encoded representation of the encrypted output. Uses AES-SIV based on the RFC https://tools.ietf.org/html/rfc5297.
Pseudonymization method that generates deterministic encryption for the given input. Outputs a base64 encoded representation of the encrypted output. Uses AES-SIV based on the RFC https://tools.ietf.org/html/rfc5297.
Pseudonymization method that generates surrogates via cryptographic hashing. Uses SHA-256. The key size must be either 32 or 64 bytes. Outputs a base64 encoded representation of the hashed output (for example, L7k0BHmF1ha5U3NfGykjro4xWi1MPVQPjhMAZbSV9mM=). Currently, only string and integer values can be hashed. See https://cloud.google.com/dlp/docs/pseudonymization to learn more.
Pseudonymization method that generates surrogates via cryptographic hashing. Uses SHA-256. The key size must be either 32 or 64 bytes. Outputs a base64 encoded representation of the hashed output (for example, L7k0BHmF1ha5U3NfGykjro4xWi1MPVQPjhMAZbSV9mM=). Currently, only string and integer values can be hashed. See https://cloud.google.com/dlp/docs/pseudonymization to learn more.
This is a data encryption key (DEK) (as opposed to a key encryption key (KEK) stored by Cloud Key Management Service (Cloud KMS). When using Cloud KMS to wrap or unwrap a DEK, be sure to set an appropriate IAM policy on the KEK to ensure an attacker cannot unwrap the DEK.
This is a data encryption key (DEK) (as opposed to a key encryption key (KEK) stored by Cloud Key Management Service (Cloud KMS). When using Cloud KMS to wrap or unwrap a DEK, be sure to set an appropriate IAM policy on the KEK to ensure an attacker cannot unwrap the DEK.
Replaces an identifier with a surrogate using Format Preserving Encryption (FPE) with the FFX mode of operation; however when used in the ReidentifyContent API method, it serves the opposite function by reversing the surrogate back into the original identifier. The identifier must be encoded as ASCII. For a given crypto key and context, the same identifier will be replaced with the same surrogate. Identifiers must be at least two characters long. In the case that the identifier is the empty string, it will be skipped. See https://cloud.google.com/dlp/docs/pseudonymization to learn more. Note: We recommend using CryptoDeterministicConfig for all use cases which do not require preserving the input alphabet space and size, plus warrant referential integrity.
Replaces an identifier with a surrogate using Format Preserving Encryption (FPE) with the FFX mode of operation; however when used in the ReidentifyContent API method, it serves the opposite function by reversing the surrogate back into the original identifier. The identifier must be encoded as ASCII. For a given crypto key and context, the same identifier will be replaced with the same surrogate. Identifiers must be at least two characters long. In the case that the identifier is the empty string, it will be skipped. See https://cloud.google.com/dlp/docs/pseudonymization to learn more. Note: We recommend using CryptoDeterministicConfig for all use cases which do not require preserving the input alphabet space and size, plus warrant referential integrity.
Create a de-identified copy of the requested table or files. A TransformationDetail will be created for each transformation. If any rows in BigQuery are skipped during de-identification (transformation errors or row size exceeds BigQuery insert API limits) they are placed in the failure output table. If the original row exceeds the BigQuery insert API limit it will be truncated when written to the failure output table. The failure output table can be set in the action.deidentify.output.big_query_output.deidentified_failure_output_table field, if no table is set, a table will be automatically created in the same project and dataset as the original table. Compatible with: Inspect
Create a de-identified copy of the requested table or files. A TransformationDetail will be created for each transformation. If any rows in BigQuery are skipped during de-identification (transformation errors or row size exceeds BigQuery insert API limits) they are placed in the failure output table. If the original row exceeds the BigQuery insert API limit it will be truncated when written to the failure output table. The failure output table can be set in the action.deidentify.output.big_query_output.deidentified_failure_output_table field, if no table is set, a table will be automatically created in the same project and dataset as the original table. Compatible with: Inspect
δ-presence metric, used to estimate how likely it is for an attacker to figure out that one given individual appears in a de-identified dataset. Similarly to the k-map metric, we cannot compute δ-presence exactly without knowing the attack dataset, so we use a statistical model instead.
δ-presence metric, used to estimate how likely it is for an attacker to figure out that one given individual appears in a de-identified dataset. Similarly to the k-map metric, we cannot compute δ-presence exactly without knowing the attack dataset, so we use a statistical model instead.
A DeltaPresenceEstimationHistogramBucket message with the following values: min_probability: 0.1 max_probability: 0.2 frequency: 42 means that there are 42 records for which δ is in [0.1, 0.2). An important particular case is when min_probability = max_probability = 1: then, every individual who shares this quasi-identifier combination is in the dataset.
Deprecated; use InspectionRuleSet instead. Rule for modifying a CustomInfoType to alter behavior under certain circumstances, depending on the specific details of the rule. Not supported for the surrogate_type custom infoType.
Deprecated; use InspectionRuleSet instead. Rule for modifying a CustomInfoType to alter behavior under certain circumstances, depending on the specific details of the rule. Not supported for the surrogate_type custom infoType.
Custom information type based on a dictionary of words or phrases. This can be used to match sensitive information specific to the data, such as a list of employee IDs or job titles. Dictionary words are case-insensitive and all characters other than letters and digits in the unicode Basic Multilingual Plane will be replaced with whitespace when scanning for matches, so the dictionary phrase "Sam Johnson" will match all three phrases "sam johnson", "Sam, Johnson", and "Sam (Johnson)". Additionally, the characters surrounding any match must be of a different type than the adjacent characters within the word, so letters must be next to non-letters and digits next to non-digits. For example, the dictionary word "jen" will match the first three letters of the text "jen123" but will return no matches for "jennifer". Dictionary words containing a large number of characters that are not letters or digits may result in unexpected findings because such characters are treated as whitespace. The limits page contains details about the size limits of dictionaries. For dictionaries that do not fit within these constraints, consider using LargeCustomDictionaryConfig in the StoredInfoType API.
Custom information type based on a dictionary of words or phrases. This can be used to match sensitive information specific to the data, such as a list of employee IDs or job titles. Dictionary words are case-insensitive and all characters other than letters and digits in the unicode Basic Multilingual Plane will be replaced with whitespace when scanning for matches, so the dictionary phrase "Sam Johnson" will match all three phrases "sam johnson", "Sam, Johnson", and "Sam (Johnson)". Additionally, the characters surrounding any match must be of a different type than the adjacent characters within the word, so letters must be next to non-letters and digits next to non-digits. For example, the dictionary word "jen" will match the first three letters of the text "jen123" but will return no matches for "jennifer". Dictionary words containing a large number of characters that are not letters or digits may result in unexpected findings because such characters are treated as whitespace. The limits page contains details about the size limits of dictionaries. For dictionaries that do not fit within these constraints, consider using LargeCustomDictionaryConfig in the StoredInfoType API.
Requirements that must be true before a table is scanned in discovery for the first time. There is an AND relationship between the top-level attributes. Additionally, minimum conditions with an OR relationship that must be met before Cloud DLP scans a table can be set (like a minimum row count or a minimum table age).
Requirements that must be true before a table is scanned in discovery for the first time. There is an AND relationship between the top-level attributes. Additionally, minimum conditions with an OR relationship that must be met before Cloud DLP scans a table can be set (like a minimum row count or a minimum table age).
Determines what tables will have profiles generated within an organization or project. Includes the ability to filter by regular expression patterns on project ID, dataset ID, and table ID.
Determines what tables will have profiles generated within an organization or project. Includes the ability to filter by regular expression patterns on project ID, dataset ID, and table ID.
What must take place for a profile to be updated and how frequently it should occur. New tables are scanned as quickly as possible depending on system capacity.
What must take place for a profile to be updated and how frequently it should occur. New tables are scanned as quickly as possible depending on system capacity.
An entity in a dataset is a field or set of fields that correspond to a single person. For example, in medical records the EntityId might be a patient identifier, or for financial records it might be an account identifier. This message is used when generalizations or analysis must take into account that multiple rows correspond to the same entity.
An entity in a dataset is a field or set of fields that correspond to a single person. For example, in medical records the EntityId might be a patient identifier, or for financial records it might be an account identifier. This message is used when generalizations or analysis must take into account that multiple rows correspond to the same entity.
The rule to exclude findings based on a hotword. For record inspection of tables, column names are considered hotwords. An example of this is to exclude a finding if it belongs to a BigQuery column that matches a specific pattern.
The rule to exclude findings based on a hotword. For record inspection of tables, column names are considered hotwords. An example of this is to exclude a finding if it belongs to a BigQuery column that matches a specific pattern.
Configuration to control the number of findings returned for inspection. This is not used for de-identification or data profiling. When redacting sensitive data from images, finding limits don't apply. They can cause unexpected or inconsistent results, where only some data is redacted. Don't include finding limits in RedactImage requests. Otherwise, Cloud DLP returns an error.
Configuration to control the number of findings returned for inspection. This is not used for de-identification or data profiling. When redacting sensitive data from images, finding limits don't apply. They can cause unexpected or inconsistent results, where only some data is redacted. Don't include finding limits in RedactImage requests. Otherwise, Cloud DLP returns an error.
Buckets values based on fixed size ranges. The Bucketing transformation can provide all of this functionality, but requires more configuration. This message is provided as a convenience to the user for simple bucketing strategies. The transformed value will be a hyphenated string of {lower_bound}-{upper_bound}. For example, if lower_bound = 10 and upper_bound = 20, all values that are within this bucket will be replaced with "10-20". This can be used on data of type: double, long. If the bound Value type differs from the type of data being transformed, we will first attempt converting the type of the data to be transformed to match the type of the bound before comparing. See https://cloud.google.com/dlp/docs/concepts-bucketing to learn more.
Buckets values based on fixed size ranges. The Bucketing transformation can provide all of this functionality, but requires more configuration. This message is provided as a convenience to the user for simple bucketing strategies. The transformed value will be a hyphenated string of {lower_bound}-{upper_bound}. For example, if lower_bound = 10 and upper_bound = 20, all values that are within this bucket will be replaced with "10-20". This can be used on data of type: double, long. If the bound Value type differs from the type of data being transformed, we will first attempt converting the type of the data to be transformed to match the type of the bound before comparing. See https://cloud.google.com/dlp/docs/concepts-bucketing to learn more.
Configuration for setting a minimum likelihood per infotype. Used to customize the minimum likelihood level for specific infotypes in the request. For example, use this if you want to lower the precision for PERSON_NAME without lowering the precision for the other infotypes in the request.
Configuration for setting a minimum likelihood per infotype. Used to customize the minimum likelihood level for specific infotypes in the request. For example, use this if you want to lower the precision for PERSON_NAME without lowering the precision for the other infotypes in the request.
A type of transformation that will scan unstructured text and apply various PrimitiveTransformations to each finding, where the transformation is applied to only values that were identified as a specific info_type.
A type of transformation that will scan unstructured text and apply various PrimitiveTransformations to each finding, where the transformation is applied to only values that were identified as a specific info_type.
Rule set for modifying a set of infoTypes to alter behavior under certain circumstances, depending on the specific details of the rules within the set.
Rule set for modifying a set of infoTypes to alter behavior under certain circumstances, depending on the specific details of the rules within the set.
The inspectTemplate contains a configuration (set of types of sensitive data to be detected) to be used anywhere you otherwise would normally specify InspectConfig. See https://cloud.google.com/dlp/docs/concepts-templates to learn more.
Reidentifiability metric. This corresponds to a risk model similar to what is called "journalist risk" in the literature, except the attack dataset is statistically modeled instead of being perfectly known. This can be done using publicly available data (like the US Census), or using a custom statistical model (indicated as one or several BigQuery tables), or by extrapolating from the distribution of values in the input dataset.
Reidentifiability metric. This corresponds to a risk model similar to what is called "journalist risk" in the literature, except the attack dataset is statistically modeled instead of being perfectly known. This can be done using publicly available data (like the US Census), or using a custom statistical model (indicated as one or several BigQuery tables), or by extrapolating from the distribution of values in the input dataset.
A KMapEstimationHistogramBucket message with the following values: min_anonymity: 3 max_anonymity: 5 frequency: 42 means that there are 42 records whose quasi-identifier values correspond to 3, 4 or 5 people in the overlying population. An important particular case is when min_anonymity = max_anonymity = 1: the frequency field then corresponds to the number of uniquely identifiable records.
Include to use an existing data crypto key wrapped by KMS. The wrapped key must be a 128-, 192-, or 256-bit key. Authorization requires the following IAM permissions when sending a request to perform a crypto transformation using a KMS-wrapped crypto key: dlp.kms.encrypt For more information, see Creating a wrapped key (https://cloud.google.com/dlp/docs/create-wrapped-key). Note: When you use Cloud KMS for cryptographic operations, charges apply.
Include to use an existing data crypto key wrapped by KMS. The wrapped key must be a 128-, 192-, or 256-bit key. Authorization requires the following IAM permissions when sending a request to perform a crypto transformation using a KMS-wrapped crypto key: dlp.kms.encrypt For more information, see Creating a wrapped key (https://cloud.google.com/dlp/docs/create-wrapped-key). Note: When you use Cloud KMS for cryptographic operations, charges apply.
Configuration for a custom dictionary created from a data source of any size up to the maximum size defined in the limits page. The artifacts of dictionary creation are stored in the specified Cloud Storage location. Consider using CustomInfoType.Dictionary for smaller dictionaries that satisfy the size requirements.
Configuration for a custom dictionary created from a data source of any size up to the maximum size defined in the limits page. The artifacts of dictionary creation are stored in the specified Cloud Storage location. Consider using CustomInfoType.Dictionary for smaller dictionaries that satisfy the size requirements.
Datastore partition ID. A partition ID identifies a grouping of entities. The grouping is always by project and namespace, however the namespace ID may be empty. A partition ID contains several dimensions: project ID and namespace ID.
Datastore partition ID. A partition ID identifies a grouping of entities. The grouping is always by project and namespace, however the namespace ID may be empty. A partition ID contains several dimensions: project ID and namespace ID.
Publish a message into a given Pub/Sub topic when DlpJob has completed. The message contains a single field, DlpJobName, which is equal to the finished job's DlpJob.name. Compatible with: Inspect, Risk
Publish a message into a given Pub/Sub topic when DlpJob has completed. The message contains a single field, DlpJobName, which is equal to the finished job's DlpJob.name. Compatible with: Inspect, Risk
Send a Pub/Sub message into the given Pub/Sub topic to connect other systems to data profile generation. The message payload data will be the byte serialization of DataProfilePubSubMessage.
Send a Pub/Sub message into the given Pub/Sub topic to connect other systems to data profile generation. The message payload data will be the byte serialization of DataProfilePubSubMessage.
If set, the detailed findings will be persisted to the specified OutputStorageConfig. Only a single instance of this action can be specified. Compatible with: Inspect, Risk
If set, the detailed findings will be persisted to the specified OutputStorageConfig. Only a single instance of this action can be specified. Compatible with: Inspect, Risk
An auxiliary table containing statistical information on the relative frequency of different quasi-identifiers values. It has one or several quasi-identifiers columns, and one column that indicates the relative frequency of each quasi-identifier tuple. If a tuple is present in the data but not in the auxiliary table, the corresponding relative frequency is assumed to be zero (and thus, the tuple is highly reidentifiable).
An auxiliary table containing statistical information on the relative frequency of different quasi-identifiers values. It has one or several quasi-identifiers columns, and one column that indicates the relative frequency of each quasi-identifier tuple. If a tuple is present in the data but not in the auxiliary table, the corresponding relative frequency is assumed to be zero (and thus, the tuple is highly reidentifiable).
User specified templates and configs for how to deidentify structured, unstructures, and image files. User must provide either a unstructured deidentify template or at least one redact image config.
User specified templates and configs for how to deidentify structured, unstructures, and image files. User must provide either a unstructured deidentify template or at least one redact image config.
How to handle transformation errors during de-identification. A transformation error occurs when the requested transformation is incompatible with the data. For example, trying to de-identify an IP address using a DateShift transformation would result in a transformation error, since date info cannot be extracted from an IP address. Information about any incompatible transformations, and how they were handled, is returned in the response as part of the TransformationOverviews.
How to handle transformation errors during de-identification. A transformation error occurs when the requested transformation is incompatible with the data. For example, trying to de-identify an IP address using a DateShift transformation would result in a transformation error, since date info cannot be extracted from an IP address. Information about any incompatible transformations, and how they were handled, is returned in the response as part of the TransformationOverviews.
Set of primitive values supported by the system. Note that for the purposes of inspection or transformation, the number of bytes considered to comprise a 'Value' is based on its representation as a UTF-8 encoded string. For example, if 'integer_value' is set to 123456789, the number of bytes would be counted as 9, even though an int64 only holds up to 8 bytes of data.
Set of primitive values supported by the system. Note that for the purposes of inspection or transformation, the number of bytes considered to comprise a 'Value' is based on its representation as a UTF-8 encoded string. For example, if 'integer_value' is set to 123456789, the number of bytes would be counted as 9, even though an int64 only holds up to 8 bytes of data.
The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide.
Represents a whole or partial calendar date, such as a birthday. The time of day and time zone are either specified elsewhere or are insignificant. The date is relative to the Gregorian Calendar. This can represent one of the following: * A full date, with non-zero year, month, and day values. * A month and day, with a zero year (for example, an anniversary). * A year on its own, with a zero month and a zero day. * A year and month, with a zero day (for example, a credit card expiration date). Related types: * google.type.TimeOfDay * google.type.DateTime * google.protobuf.Timestamp
Represents a whole or partial calendar date, such as a birthday. The time of day and time zone are either specified elsewhere or are insignificant. The date is relative to the Gregorian Calendar. This can represent one of the following: * A full date, with non-zero year, month, and day values. * A month and day, with a zero year (for example, an anniversary). * A year on its own, with a zero month and a zero day. * A year and month, with a zero day (for example, a credit card expiration date). Related types: * google.type.TimeOfDay * google.type.DateTime * google.protobuf.Timestamp
Represents a time of day. The date and time zone are either not significant or are specified elsewhere. An API may choose to allow leap seconds. Related types are google.type.Date and google.protobuf.Timestamp.
Represents a time of day. The date and time zone are either not significant or are specified elsewhere. An API may choose to allow leap seconds. Related types are google.type.Date and google.protobuf.Timestamp.
Creates an InspectTemplate for reusing frequently used configuration for inspecting content, images, and storage. See https://cloud.google.com/dlp/docs/creating-templates to learn more.
Auto-naming is currently not supported for this resource.
Creates an InspectTemplate for reusing frequently used configuration for inspecting content, images, and storage. See https://cloud.google.com/dlp/docs/creating-templates to learn more.
Auto-naming is currently not supported for this resource.
Creates a job trigger to run DLP actions such as scanning storage for sensitive information on a set schedule. See https://cloud.google.com/dlp/docs/creating-job-triggers to learn more.
Auto-naming is currently not supported for this resource.
Creates a DeidentifyTemplate for reusing frequently used configuration for de-identifying content, images, and storage. See https://cloud.google.com/dlp/docs/creating-templates-deid to learn more.
Auto-naming is currently not supported for this resource.
Likelihood to return for this CustomInfoType. This base value can be altered by a detection rule if the finding meets the criteria specified by the rule. Defaults to VERY_LIKELY if not specified.
Only returns findings equal to or above this threshold. The default is POSSIBLE. In general, the highest likelihood setting yields the fewest findings in results and the lowest chance of a false positive. For more information, see Match likelihood.
Schema used for writing the findings for Inspect jobs. This field is only used for Inspect and must be unspecified for Risk jobs. Columns are derived from the Finding object. If appending to an existing table, any columns from the predefined schema that are missing will be added. No columns in the existing table will be deleted. If unspecified, then all available columns will be used for a new table or an (existing) table with no schema, and no changes will be made to an existing table that has a schema. Only for use with external storage.
How much data to include in the Pub/Sub message. If the user wishes to limit the size of the message, they can use resource_name and fetch the profile fields they wish to. Per table profile (not per column).
Gets an InspectTemplate. See https://cloud.google.com/dlp/docs/creating-templates to learn more.
args Arguments passed to this invoke. Arguments for getInspectTemplate.
options Invoke options controlling this call.
Gets a discovery configuration.
args Arguments passed to this invoke. Arguments for getOrganizationDiscoveryConfig.
options Invoke options controlling this call.
Gets an InspectTemplate. See https://cloud.google.com/dlp/docs/creating-templates to learn more.
args Arguments passed to this invoke. Arguments for getOrganizationInspectTemplate.
options Invoke options controlling this call.
Gets a DeidentifyTemplate. See https://cloud.google.com/dlp/docs/creating-templates-deid to learn more.
args Arguments passed to this invoke. Arguments for getOrganizationsDeidentifyTemplate.
options Invoke options controlling this call.
