Delete the GCP Vertex AI endpoint from the cloud provider, as well as the CMDB.


GCP > Vertex AI > Endpoint

GCP > Vertex AI > Endpoint > Delete

Process events for GCP Vertex AI service Endpoint resources.
The router receives events from the event handler and creates, updates, or deletes Endpoint resources in the CMDB.


GCP > Project

GCP > Vertex AI > Endpoint > Router

Update the labels for GCP Vertex AI endpoint.


GCP > Vertex AI > Endpoint > Set Labels

Delete the GCP Vertex AI notebook runtime template from the cloud provider, as well as the CMDB.


GCP > Vertex AI > Notebook Runtime Template

GCP > Vertex AI > Notebook Runtime Template > Delete

Process events for GCP Vertex AI service Notebook Runtime Template resources.

The router receives events from the event handler and creates, updates, or deletes Notebook Runtime Template resources in the CMDB.


GCP > Vertex AI > Notebook Runtime Template > Router

Configure whether the GCP Vertex AI API is enabled.


GCP > Vertex AI

GCP > Vertex AI > Set API Enabled

Take an action when an GCP Vertex AI endpoint is not active based on the&#92;n`GCP &gt; Vertex AI &gt; Endpoint &gt; Active &gt; *` policies.&#92;n&#92;nThe Active control determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated compliance&#92;nenvironment, it&#39;s common to end up with a wide range of alarms that are difficult&#92;nand time consuming to clear. The Active control brings automated, well-defined&#92;ncontrol to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`GCP &gt; Vertex AI &gt; Endpoint &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n&#92;n**Note** the contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

GCP > Vertex AI > Endpoint > Active > Age

GCP > Vertex AI > Endpoint > Active > Last Modified

GCP > Vertex AI > Endpoint > Active

Take an action when a GCP Vertex AI endpoint is not approved based on `GCP &gt; Vertex AI &gt; Endpoint &gt; Approved &gt; *` policies.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

GCP > Vertex AI > Endpoint > Approved > Regions

GCP > Vertex AI > Endpoint > Approved > Custom

GCP > Vertex AI > Endpoint > Approved > Usage

GCP > Vertex AI > Enabled

GCP > Vertex AI > Endpoint > Approved

Record and synchronize details for the GCP Vertex AI endpoint into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is&#92;nresponsible for populating and updating all the attributes for that&#92;nresource type in the Guardrails CMDB.&#92;n&#92;n**Note:** If CMDB is set to Skip for a resource, then it will not be added&#92;nto the CMDB, and no controls that target it will run.&#92;n&#92;nCMDB controls also use the Regions policy associated with the resource. If&#92;nregion is not in `GCP &gt; Vertex AI &gt; Endpoint &gt; Regions` policy, the CMDB control will delete the&#92;nresource from the CMDB.&#92;n

GCP > Vertex AI > Endpoint > Regions

GCP > Vertex AI > API Enabled

GCP > Vertex AI > Endpoint > CMDB

Discover GCP Vertex AI endpoint resources and add them to the CMDB.&#92;n&#92;nThe [Discovery](https://turbot.com/guardrails/docs/concepts/guardrails/discovery)&#92;ncontrol is tasked with identifying instances for a particular resource.&#92;nThe Discovery control will periodically search for new target resources and&#92;nsave them to the Guardrails CMDB. Once discovered, resources are then&#92;nresponsible for tracking changes to themselves through the&#92;n[CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control.&#92;n&#92;nNote that Discovery and CMDB controls also use the Regions policy&#92;nassociated with the resource. If the region is not in `GCP &gt; Vertex AI &gt; Endpoint &gt; Regions` policy, the CMDB&#92;ncontrol will delete the resource from the CMDB.&#92;n

GCP > Vertex AI > Regions

GCP > Region

GCP > Vertex AI > Endpoint > Discovery

Take an action when an GCP Vertex AI endpoint labels is not updated based on the `GCP &gt; Vertex AI &gt; Endpoint &gt; Labels &gt; *` policies.&#92;n&#92;nIf the resource is not updated with the labels defined in `GCP &gt; Vertex AI &gt; Endpoint &gt; Labels &gt; Template`, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nSee [Labels](https://turbot.com/guardrails/docs/concepts/guardrails/tagging) for more information.&#92;n

GCP > Vertex AI > Endpoint > Labels > Template

GCP > Vertex AI > Endpoint > Labels

The Usage control determines whether the number of GCP Vertex AI endpoint resources exceeds the configured usage limit for this region.&#92;nYou can configure the behavior of this control with the  `GCP &gt; Vertex AI &gt; Endpoint &gt; Usage` policy, and set the limit with the `GCP &gt; Vertex AI &gt; Endpoint &gt; Usage &gt; Limit` policy.&#92;n

GCP > Vertex AI > Endpoint > Usage > Limit

GCP > Vertex AI > Endpoint > Usage

Take an action when an GCP Vertex AI notebook runtime template is not active based on the&#92;n`GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Active &gt; *` policies.&#92;n&#92;nThe Active control determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated compliance&#92;nenvironment, it&#39;s common to end up with a wide range of alarms that are difficult&#92;nand time consuming to clear. The Active control brings automated, well-defined&#92;ncontrol to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n&#92;n**Note** the contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

GCP > Vertex AI > Notebook Runtime Template > Active > Age

GCP > Vertex AI > Notebook Runtime Template > Active > Last Modified

GCP > Vertex AI > Notebook Runtime Template > Active

Take an action when a GCP Vertex AI notebook runtime template is not approved based on `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Approved &gt; *` policies.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

GCP > Vertex AI > Notebook Runtime Template > Approved > Regions

GCP > Vertex AI > Notebook Runtime Template > Approved > Custom

GCP > Vertex AI > Notebook Runtime Template > Approved > Usage

GCP > Vertex AI > Notebook Runtime Template > Approved

Record and synchronize details for the GCP Vertex AI notebook runtime template into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is&#92;nresponsible for populating and updating all the attributes for that&#92;nresource type in the Guardrails CMDB.&#92;n&#92;n**Note:** If CMDB is set to Skip for a resource, then it will not be added&#92;nto the CMDB, and no controls that target it will run.&#92;n&#92;nCMDB controls also use the Regions policy associated with the resource. If&#92;nregion is not in `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Regions` policy, the CMDB control will delete the&#92;nresource from the CMDB.&#92;n

GCP > Vertex AI > Notebook Runtime Template > Regions

GCP > Vertex AI > Notebook Runtime Template > CMDB

Discover GCP Vertex AI notebook runtime template resources and add them to the CMDB.&#92;n&#92;nThe [Discovery](https://turbot.com/guardrails/docs/concepts/guardrails/discovery)&#92;ncontrol is tasked with identifying instances for a particular resource.&#92;nThe Discovery control will periodically search for new target resources and&#92;nsave them to the Guardrails CMDB. Once discovered, resources are then&#92;nresponsible for tracking changes to themselves through the&#92;n[CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control.&#92;n&#92;nNote that Discovery and CMDB controls also use the Regions policy&#92;nassociated with the resource. If the region is not in `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Regions` policy, the CMDB&#92;ncontrol will delete the resource from the CMDB.&#92;n

GCP > Vertex AI > Notebook Runtime Template > Discovery

The Usage control determines whether the number of GCP Vertex AI notebook runtime template resources exceeds the configured usage limit for this region.&#92;nYou can configure the behavior of this control with the  `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Usage` policy, and set the limit with the `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Usage &gt; Limit` policy.&#92;n

GCP > Vertex AI > Notebook Runtime Template > Usage > Limit

GCP > Vertex AI > Notebook Runtime Template > Usage

Check whether GCP Vertex AI API is enabled.&#92;n&#92;nAPI Enabled refers specifically to the API state of a service in a cloud project. This control determines whether the API state is set as per desired level.&#92;n&#92;nThe `GCP &gt; Vertex AI &gt; API Enabled` control compares the API state against the API Enabled policies, raises an alarm, and takes the defined enforcement action.&#92;n

Record and synchronize details for the GCP Vertex AI into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is&#92;nresponsible for populating and updating all the attributes for that&#92;nresource type in the Guardrails CMDB.&#92;n&#92;n**Note:** If CMDB is set to Skip for a resource, then it will not be added&#92;nto the CMDB, and no controls that target it will run.&#92;n&#92;nCMDB controls also use the Regions policy associated with the resource. If&#92;nregion is not in `GCP &gt; Vertex AI &gt; Vertex AI &gt; Regions` policy, the CMDB control will delete the&#92;nresource from the CMDB.&#92;n

GCP > Vertex AI > CMDB

Discover GCP Vertex AI resources and add them to the CMDB.&#92;n&#92;nThe [Discovery](https://turbot.com/guardrails/docs/concepts/guardrails/discovery)&#92;ncontrol is tasked with identifying instances for a particular resource.&#92;nThe Discovery control will periodically search for new target resources and&#92;nsave them to the Guardrails CMDB. Once discovered, resources are then&#92;nresponsible for tracking changes to themselves through the&#92;n[CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control.&#92;n&#92;nNote that Discovery and CMDB controls also use the Regions policy&#92;nassociated with the resource. If the region is not in `GCP &gt; Vertex AI &gt; Vertex AI &gt; Regions` policy, the CMDB&#92;ncontrol will delete the resource from the CMDB.&#92;n

GCP > Vertex AI > Discovery

dataPersistentDiskSpec

deployedModels

encryptionSpec

projects/1031111111111/locations/us-central1/endpoints/556751111111105344

endpoint

endpointAka

endpointName

endpointServiceNow

eucConfig

idleShutdownConfig

machineSpec

networkSpec

projects/1031111111111/locations/us-central1/notebookRuntimeTemplates/test-template

notebookRuntimeTemplate

notebookRuntimeTemplateAka

invalid - notebookRuntimeTemplate ID is missing

notebookRuntimeTemplateName

notebookRuntimeTemplateServiceNow

notebookRuntimeType

predictRequestResponseLoggingConfig

privateServiceConnectConfig

reservationAffinity

shieldedVmConfig

projects/123456789012/services/aiplatform.googleapis.com

GCP Vertex AI

vertexAi

vertexAiAka

vertexAiName

gcp-vertexai

Determine the action to take when an GCP Vertex AI endpoint, based on the `GCP &gt; Vertex AI &gt; Endpoint &gt; Active &gt; *` policies.&#92;n&#92;nThe control determines whether the resource is in active use, and if not,&#92;nhas the ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`GCP &gt; Vertex AI &gt; Endpoint &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n**Note** the contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The age after which the GCP Vertex AI endpoint&#92;nis no longer considered active. If a create time is unavailable, the time Guardrails discovered the resource is used.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`GCP &gt; Vertex AI &gt; Endpoint &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n**Note** the contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The number of days since the GCP Vertex AI endpoint was last modified before it is considered&#92;ninactive.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`GCP &gt; Vertex AI &gt; Endpoint &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n**Note** the contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n

Determine the action to take when a GCP Vertex AI endpoint is not approved based on `GCP &gt; Vertex AI &gt; Endpoint &gt; Approved &gt; *` policies.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Determine whether the GCP Vertex AI endpoint is allowed to exist.&#92;nThis policy will be evaluated by the Approved control. If a GCP Vertex AI endpoint is not approved, it will be subject to the action specified in the `GCP &gt; Vertex AI &gt; Endpoint &gt; Approved` policy.&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n&#92;n**Note**: The policy value must be a string with a value of `Approved`, `Not approved` or `Skip`, or in the form of YAML objects. The object(s) must contain the key `result` with its value as `Approved` or `Not approved`. A custom title and message can also be added using the keys `title` and `message` respectively.&#92;n

string

A list of GCP regions in which GCP Vertex AI endpoints are approved for use.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and &#39;?&#39; wildcard characters.&#92;n&#92;nThis policy will be evaluated by the Approved control. If a GCP Vertex AI endpoint is created in a region that is not in the approved list, it will be subject to the action specified in the `GCP &gt; Vertex AI &gt; Endpoint &gt; Approved` policy.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Determine whether the GCP Vertex AI endpoint is allowed to exist.&#92;n&#92;nThis policy will be evaluated by the Approved control. If a GCP Vertex AI endpoint is not approved, it will be subject to the action specified in the `GCP &gt; Vertex AI &gt; Endpoint &gt; Approved` policy.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Configure whether to record and synchronize details for the GCP Vertex AI endpoint into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is responsible for populating and updating all the attributes for that resource type in the Guardrails CMDB.&#92;nAll policies and controls in Guardrails are based around the resource, so usually the CMDB policy is set to **&quot;Enforce: Enabled&quot;**.&#92;n&#92;nIf set to Skip then all changes to the CMDB are paused - no new resources will be discovered, no updates will be made and deleted resources will not be removed.&#92;n&#92;nTo cleanup resources and stop tracking changes, set this policy to **&quot;Enforce: Disabled&quot;**.&#92;n&#92;nCMDB controls also use the Regions policy associated with the resource. If region is not in `GCP &gt; Vertex AI &gt; Endpoint &gt; Regions` policy, the CMDB control will delete the resource from the CMDB.&#92;n&#92;n(**Note**: Setting CMDB to **&quot;Skip&quot;** will also pause these changes.)&#92;n

Determine the action to take when an GCP Vertex AI endpoint labels are not updated based on the `GCP &gt; Vertex AI &gt; Endpoint &gt; Labels &gt; *` policies.&#92;n&#92;nThe control ensure GCP Vertex AI endpoint labels include labels defined in `GCP &gt; Vertex AI &gt; Endpoint &gt; Labels &gt; Template`.&#92;n&#92;nLabels not defined in Endpoint Labels Template will not be modified or deleted. Setting a label value to `undefined` will result in the label being deleted.&#92;n&#92;nSee [Labels](https://turbot.com/guardrails/docs/concepts/guardrails/tagging) for more information.&#92;n

The template is used to generate the keys and values for GCP Vertex AI endpoint.&#92;n&#92;nLabels not defined in Endpoint Labels Template will not be modified or deleted. Setting a label value to `undefined` will result in the label being deleted.&#92;n&#92;nSee [Labels](https://turbot.com/guardrails/docs/concepts/guardrails/tagging) for more information.&#92;n

A list of GCP regions in which GCP Vertex AI endpoints are supported for use.&#92;n&#92;nAny endpoints in a region not listed here will not be recorded in CMDB.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and&#92;n&#39;?&#39; wildcard characters.&#92;n

Configure the number of GCP Vertex AI endpoints that can be used for this region and the current consumption against the limit.&#92;nYou can configure the behavior of the control with this  `GCP &gt; Vertex AI &gt; Endpoint &gt; Usage` policy.&#92;n

Maximum number of items that can be created for this region&#92;n

A calculated policy that Guardrails uses to create a compiled list of ALL&#92;npermissions for GCP Vertex AI that is used as&#92;ninput to the control that manages the IAM stack.&#92;n

GCP > Turbot > Permissions > Compiled > Service Permissions

GCP > Turbot > Permissions > Compiled > Service Permissions > @turbot/gcp-vertexai

A calculated policy that Guardrails uses to create a compiled list of ALL permission&#92;nlevels for GCP Vertex AI that is used as input to&#92;nthe stack that manages the Guardrails IAM permissions objects.&#92;n

GCP > Turbot > Permissions > Compiled > Levels

GCP > Turbot > Permissions > Compiled > Levels > @turbot/gcp-vertexai

Determine the action to take when an GCP Vertex AI notebook runtime template, based on the `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Active &gt; *` policies.&#92;n&#92;nThe control determines whether the resource is in active use, and if not,&#92;nhas the ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n**Note** the contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The age after which the GCP Vertex AI notebook runtime template&#92;nis no longer considered active. If a create time is unavailable, the time Guardrails discovered the resource is used.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Active &gt; *`),&#92;nraises an alarm, and takes the defined enforcement action. Each Active&#92;nsub-policy can calculate a status of active, inactive or skipped. Generally,&#92;nif the resource appears to be Active for any reason it will be considered Active.&#92;n**Note** the contrast with Approved, where if the resource appears to be Unapproved&#92;nfor any reason it will be considered Unapproved.&#92;n&#92;nSee [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active) for more information.&#92;n

The number of days since the GCP Vertex AI notebook runtime template was last modified before it is considered&#92;ninactive.&#92;n&#92;nThe [Active](https://turbot.com/guardrails/docs/concepts/guardrails/active)&#92;ncontrol determines whether the resource is in active use, and if not, has&#92;nthe ability to delete / cleanup the resource. When running an automated&#92;ncompliance environment, it&#39;s common to end up with a wide range of alarms&#92;nthat are difficult and time consuming to clear. The Active control brings&#92;nautomated, well-defined control to this process.&#92;n&#92;nThe Active control checks the status of all defined Active policies for the&#92;nresource (`GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Active &gt; *`), raises an alarm, and takes the defined enforcement&#92;naction. Each Active sub-policy can calculate a status of active, inactive&#92;nor skipped. Generally, if the resource appears to be Active for any reason&#92;nit will be considered Active.&#92;n**Note** the contrast with Approved, where if the&#92;nresource appears to be Unapproved for any reason it will be considered&#92;nUnapproved.&#92;n

Determine the action to take when a GCP Vertex AI notebook runtime template is not approved based on `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Approved &gt; *` policies.&#92;n&#92;nThe Approved control checks the status of the defined Approved sub-policies for the resource. If the resource is not approved according to any of these policies, this control raises an alarm and takes the defined enforcement action.&#92;n&#92;nFor any enforcement actions that specify `if new`, e.g., `Enforce: Delete unapproved if new`, this control will only take the enforcement actions for resources created within the last 60 minutes.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Determine whether the GCP Vertex AI notebook runtime template is allowed to exist.&#92;nThis policy will be evaluated by the Approved control. If a GCP Vertex AI notebook runtime template is not approved, it will be subject to the action specified in the `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Approved` policy.&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n&#92;n**Note**: The policy value must be a string with a value of `Approved`, `Not approved` or `Skip`, or in the form of YAML objects. The object(s) must contain the key `result` with its value as `Approved` or `Not approved`. A custom title and message can also be added using the keys `title` and `message` respectively.&#92;n

A list of GCP regions in which GCP Vertex AI notebook runtime templates are approved for use.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and &#39;?&#39; wildcard characters.&#92;n&#92;nThis policy will be evaluated by the Approved control. If a GCP Vertex AI notebook runtime template is created in a region that is not in the approved list, it will be subject to the action specified in the `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Approved` policy.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Determine whether the GCP Vertex AI notebook runtime template is allowed to exist.&#92;n&#92;nThis policy will be evaluated by the Approved control. If a GCP Vertex AI notebook runtime template is not approved, it will be subject to the action specified in the `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Approved` policy.&#92;n&#92;nSee [Approved](https://turbot.com/guardrails/docs/concepts/guardrails/approved) for more information.&#92;n

Configure whether to record and synchronize details for the GCP Vertex AI notebook runtime template into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is responsible for populating and updating all the attributes for that resource type in the Guardrails CMDB.&#92;nAll policies and controls in Guardrails are based around the resource, so usually the CMDB policy is set to **&quot;Enforce: Enabled&quot;**.&#92;n&#92;nIf set to Skip then all changes to the CMDB are paused - no new resources will be discovered, no updates will be made and deleted resources will not be removed.&#92;n&#92;nTo cleanup resources and stop tracking changes, set this policy to **&quot;Enforce: Disabled&quot;**.&#92;n&#92;nCMDB controls also use the Regions policy associated with the resource. If region is not in `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Regions` policy, the CMDB control will delete the resource from the CMDB.&#92;n&#92;n(**Note**: Setting CMDB to **&quot;Skip&quot;** will also pause these changes.)&#92;n

A list of GCP regions in which GCP Vertex AI notebook runtime templates are supported for use.&#92;n&#92;nAny notebook runtime templates in a region not listed here will not be recorded in CMDB.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and&#92;n&#39;?&#39; wildcard characters.&#92;n

Configure the number of GCP Vertex AI notebook runtime templates that can be used for this region and the current consumption against the limit.&#92;nYou can configure the behavior of the control with this  `GCP &gt; Vertex AI &gt; Notebook Runtime Template &gt; Usage` policy.&#92;n

A list of GCP regions in which GCP Vertex AI resources are approved for use.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and&#92;n&#39;?&#39; wildcard characters.&#92;n&#92;nThis policy is the default value for all GCP Vertex AI resources&#39; Approved &gt; Regions policies.&#92;n

GCP > Vertex AI > Approved Regions [Default]

Configure whether to record and synchronize details for the GCP Vertex AI vertex ai into the CMDB.&#92;n&#92;nThe [CMDB](https://turbot.com/guardrails/docs/concepts/guardrails/cmdb) control is responsible for populating and updating all the attributes for that resource type in the Guardrails CMDB.&#92;nAll policies and controls in Guardrails are based around the resource, so usually the CMDB policy is set to **&quot;Enforce: Enabled&quot;**.&#92;n&#92;nIf set to Skip then all changes to the CMDB are paused - no new resources will be discovered, no updates will be made and deleted resources will not be removed.&#92;n&#92;nTo cleanup resources and stop tracking changes, set this policy to **&quot;Enforce: Disabled&quot;**.&#92;n&#92;nCMDB controls also use the Regions policy associated with the resource. If region is not in `GCP &gt; Vertex AI &gt; Vertex AI &gt; Regions` policy, the CMDB control will delete the resource from the CMDB.&#92;n&#92;n(**Note**: Setting CMDB to **&quot;Skip&quot;** will also pause these changes.)&#92;n

A GCP logs [advanced filter](https://cloud.google.com/logging/docs/view/advanced-filters)&#92;nused to specify a subset of log entries that will be forwarded by the logging sink on behalf of the gcp-vertexai mod.&#92;nThis is a read-only policy that is used internally by Guardrails.&#92;n

GCP > Turbot > Event Handlers > Logging > Sink > Compiled Filter

GCP > Turbot > Event Handlers > Logging > Sink > Compiled Filter > @turbot/gcp-vertexai

A template used to generate the keys and values for GCP Vertex AI resources.&#92;n&#92;nBy default, all Vertex AI resource Labels &gt; Template policies will use this value.&#92;n

GCP > Vertex AI > Labels Template [Default]

GCP > Vertex AI > Permissions > Levels

Configure whether permissions policies are in effect for GCP Vertex AI.&#92;nThis setting does not affect Project level permissions (GCP/Admin, GCP/Owner, etc).&#92;n&#92;n**Note:** The behavior of this policy depends on the value of GCP &gt; Permissions.&#92;n

GCP > Vertex AI > Permissions

GCP > Vertex AI > Permissions > Levels > Modifiers

Define the permissions levels that can be used to grant access to Vertex AI&#92;nan GCP project. Permissions levels defined will appear in the UI to assign access to Guardrails users.&#92;n&#92;n**Note:** Some services do not use all permissions levels, and any permissions level that has&#92;nno permissions associated will not be created even if it is selected here.&#92;n

A map of GCP API to Guardrails Permission Level used to customize Guardrails&#39; standard permissions.&#92;nYou can add, remove or redefine the mapping of GCP API operations to Guardrails permissions levels here.&#92;n&#92;n**Note:** Modifiers are cumulative - if you add a permission to the metadata level, it is also added&#92;n to readOnly, operator and admin. Modifier policies set here will “roll up” to the GCP level too - if&#92;n you add a permission to Admin, it will be granted to GCP/Storage/Admin and also GCP/Admin&#92;n&#92;n```&#92;nexample:&#92;n  - &quot;storage.bucket.create&quot;: admin&#92;n  - &quot;sql.database.create&quot;: metadata&#92;n```&#92;n

A list of GCP regions in which GCP Vertex AI resources are supported for use.&#92;n&#92;nThe expected format is an array of regions names. You may use the &#39;*&#39; and&#92;n&#39;?&#39; wildcard characters.&#92;n&#92;nThis policy is the default value for all GCP Vertex AI resources&#39; Regions policies.&#92;n

GCP > Vertex AI > Endpoint > ServiceNow

The Endpoint resource type represents a managed service that allows you to deploy machine learning models for online predictions. Endpoints are used to host machine learning models that are ready for inference, enabling you to serve predictions in real-time or in batches.&#92;n

GCP > Vertex AI > Endpoint > ServiceNow > Configuration Item

GCP > Vertex AI > Endpoint > ServiceNow > Import Set

GCP > Vertex AI > Endpoint > ServiceNow > Configuration Item > Record

GCP > Vertex AI > Endpoint > ServiceNow > Import Set > Archive Columns

GCP > Vertex AI > Endpoint > ServiceNow > Import Set > Insert Mode

GCP > Vertex AI > Endpoint > ServiceNow > Import Set > Record

GCP > Vertex AI > Endpoint > ServiceNow > Import Set > Table Name

GCP > Vertex AI > Notebook Runtime Template > ServiceNow

The NotebookRuntimeTemplate resource type represents a pre-configured environment for running Jupyter notebooks in Google Cloud Vertex AI. It is used to create managed notebook instances with predefined software packages, libraries, and dependencies tailored for machine learning and data science tasks.&#92;n

GCP > Vertex AI > Notebook Runtime Template > ServiceNow > Configuration Item

GCP > Vertex AI > Notebook Runtime Template > ServiceNow > Import Set

GCP > Vertex AI > Notebook Runtime Template > ServiceNow > Configuration Item > Record

GCP > Vertex AI > Notebook Runtime Template > ServiceNow > Import Set > Archive Columns

GCP > Vertex AI > Notebook Runtime Template > ServiceNow > Import Set > Insert Mode

GCP > Vertex AI > Notebook Runtime Template > ServiceNow > Import Set > Record

GCP > Vertex AI > Notebook Runtime Template > ServiceNow > Import Set > Table Name

Google Cloud Vertex AI is a fully managed machine learning (ML) platform designed to help developers and data scientists to build, deploy, and scale ML models. It offers a unified platform for data preparation, model training, hyperparameter tuning, and deployment of ML models, allowing you to manage the end-to-end machine learning lifecycle from a single interface.&#92;n

Mod: gcp-vertexai

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