Vertex AI audit logging information

If you're looking for information about audit logs created by Vertex AI Workbench, see the audit logging pages for managed notebooks or user-managed notebooks.

This document describes the audit logs created by Vertex AI as part of Cloud Audit Logs.

Overview

Google Cloud services write audit logs to help you answer the questions, "Who did what, where, and when?" within your Google Cloud resources.

Your Google Cloud projects contain only the audit logs for resources that are directly within the Google Cloud project. Other Google Cloud resources, such as folders, organizations, and billing accounts, contain the audit logs for the entity itself.

For a general overview of Cloud Audit Logs, see Cloud Audit Logs overview. For a deeper understanding of the audit log format, see Understand audit logs.

Available audit logs

The following types of audit logs are available for Vertex AI:

  • Admin Activity audit logs

    Includes "admin write" operations that write metadata or configuration information.

    You can't disable Admin Activity audit logs.

  • Data Access audit logs

    Includes "admin read" operations that read metadata or configuration information. Also includes "data read" and "data write" operations that read or write user-provided data.

    To receive Data Access audit logs, you must explicitly enable them.

For fuller descriptions of the audit log types, see Types of audit logs.

Audited operations

The following table summarizes which API operations correspond to each audit log type in Vertex AI:

Audit logs category Vertex AI operations
Admin Activity audit logs batchPredictionJobs.cancel
batchPredictionJobs.create
batchPredictionJobs.delete
customJobs.cancel
customJobs.create
customJobs.delete
dataLabelingJobs.cancel
dataLabelingJobs.create
dataLabelingJobs.delete
datasets.create
datasets.delete
datasets.export
datasets.import
datasets.patch
endpoints.create
endpoints.delete
endpoints.deployModel
endpoints.patch
endpoints.undeployModel
featurestores.create
featurestores.delete
featurestores.patch
featurestores.setIamPolicy
featurestores.entityTypes.create
featurestores.entityTypes.delete
featurestores.entityTypes.patch
featurestores.entityTypes.setIamPolicy
featurestores.entityTypes.features.batchCreate
featurestores.entityTypes.features.create
featurestores.entityTypes.features.delete
featurestores.entityTypes.features.patch
hyperparameterTuningJobs.cancel
hyperparameterTuningJobs.create
hyperparameterTuningJobs.delete
indexEndpoints.create
indexEndpoints.delete
indexEndpoints.deployIndex
indexEndpoints.mutateDeployedIndex
indexEndpoints.patch
indexEndpoints.undeployIndex
metadataStores.create
metadataStores.delete
metadataStores.artifacts.create
metadataStores.artifacts.delete
metadataStores.artifacts.patch
metadataStores.artifacts.purge
metadataStores.contexts.addContextArtifactsAndExecutions
metadataStores.contexts.addContextChildren
metadataStores.contexts.create
metadataStores.contexts.delete
metadataStores.contexts.patch
metadataStores.contexts.purge
metadataStores.executions.addExecutionEvents
metadataStores.executions.create
metadataStores.executions.delete
metadataStores.executions.patch
metadataStores.executions.purge
metadataStores.metadataSchemas.create
migratableResources.batchMigrate
modelDeploymentMonitoringJobs.create
modelDeploymentMonitoringJobs.delete
modelDeploymentMonitoringJobs.patch
modelDeploymentMonitoringJobs.pause
modelDeploymentMonitoringJobs.resume
models.delete
models.deleteVersion
models.export
models.mergeVersionAliases
models.patch
models.upload
models.evaluations.import
models.evaluations.slices.batchImport
modelMonitors.create
modelMonitors.delete
modelMonitors.update
modelMonitoringJobs.create
modelMonitoringJobs.delete
operations.cancel
pipelineJobs.cancel
pipelineJobs.create
pipelineJobs.delete
schedules.create
schedules.delete
schedules.update
specialistPools.create
specialistPools.delete
specialistPools.patch
studies.create
studies.delete
studies.trials.addTrialMeasurement
studies.trials.complete
studies.trials.create
studies.trials.delete
studies.trials.stop
studies.trials.suggest
tensorboards.create
tensorboards.delete
tensorboards.patch
tensorboards.experiments.create
tensorboards.experiments.delete
tensorboards.experiments.patch
tensorboards.experiments.write
tensorboards.experiments.runs.batchCreate
tensorboards.experiments.runs.create
tensorboards.experiments.runs.delete
tensorboards.experiments.runs.patch
tensorboards.experiments.runs.write
tensorboards.experiments.runs.timeSeries.batchCreate
tensorboards.experiments.runs.timeSeries.create
tensorboards.experiments.runs.timeSeries.delete
tensorboards.experiments.runs.timeSeries.patch
trainingPipelines.cancel
trainingPipelines.create
trainingPipelines.delete
deploymentResourcePool.create
deploymentResourcePool.delete
Data Access (ADMIN_READ) audit logs batchPredictionJobs.get
batchPredictionJobs.list
customJobs.get
customJobs.list
dataLabelingJobs.get
dataLabelingJobs.list
datasets.get
datasets.list
datasets.annotationSpecs.get
datasets.annotations.list
datasets.savedQueries.list
endpoints.get
endpoints.list
featurestores.get
featurestores.getIamPolicy
featurestores.list
featurestores.searchFeatures
featurestores.entityTypes.get
featurestores.entityTypes.getIamPolicy
featurestores.entityTypes.list
featurestores.entityTypes.features.get
featurestores.entityTypes.features.list
hyperparameterTuningJobs.get
hyperparameterTuningJobs.list
indexEndpoints.get
indexEndpoints.list
indexes.get
indexes.delete
metadataStores.get
metadataStores.list
metadataStores.artifacts.get
metadataStores.artifacts.list
metadataStores.artifacts.queryArtifactLineageSubgraph
metadataStores.contexts.get
metadataStores.contexts.list
metadataStores.contexts.queryContextLineageSubgraph
metadataStores.executions.get
metadataStores.executions.list
metadataStores.executions.queryExecutionInputsAndOutputs
metadataStores.metadataSchemas.get
metadataStores.metadataSchemas.list
migratableResources.search
modelDeploymentMonitoringJobs.get
modelDeploymentMonitoringJobs.list
models.get
models.list
models.listVersions
models.evaluations.get
models.evaluations.list
models.evaluations.slices.get
models.evaluations.slices.list
modelMonitors.get
modelMonitors.list
modelMonitoringJobs.get
modelMonitoringJobs.list
pipelineJobs.get
pipelineJobs.list
schedules.get
schedules.list
specialistPools.get
specialistPools.list
studies.get
studies.list
studies.lookup
studies.trials.checkTrialEarlyStoppingState
studies.trials.get
studies.trials.list
studies.trials.listOptimalTrials
tensorboards.get
tensorboards.list
tensorboards.experiments.get
tensorboards.experiments.list
tensorboards.experiments.runs.get
tensorboards.experiments.runs.list
tensorboards.experiments.runs.timeSeries.batchRead
tensorboards.experiments.runs.timeSeries.exportTensorboardTimeSeries
tensorboards.experiments.runs.timeSeries.get
tensorboards.experiments.runs.timeSeries.list
tensorboards.experiments.runs.timeSeries.read
tensorboards.experiments.runs.timeSeries.readBlobData
trainingPipelines.get
trainingPipelines.list
deploymentResourcePool.get
deploymentResourcePool.list
deploymentResourcePool.queryDeployedModels
Data Access (DATA_READ) audit logs datasets.dataItems.list
endpoints.explain
endpoints.predict
endpoints.rawPredict
featurestores.batchReadFeatureValues
featurestores.entityTypes.exportFeatureValues
featurestores.entityTypes.readFeatureValues
featurestores.entityTypes.streamingReadFeatureValues
modelDeploymentMonitoringJobs.searchModelDeploymentMonitoringStatsAnomalies
modelMonitors.searchModelMonitoringAlerts
modelMonitors.searchModelMonitoringStats
Data Access (DATA_WRITE) audit logs featurestores.entityTypes.importFeatureValues
indexes.create
indexes.patch
indexes.removeDatapoints
indexes.upsertDatapoints

Audit log format

Audit log entries include the following objects:

  • The log entry itself, which is an object of type LogEntry. Useful fields include the following:

    • The logName contains the resource ID and audit log type.
    • The resource contains the target of the audited operation.
    • The timeStamp contains the time of the audited operation.
    • The protoPayload contains the audited information.
  • The audit logging data, which is an AuditLog object held in the protoPayload field of the log entry.

  • Optional service-specific audit information, which is a service-specific object. For earlier integrations, this object is held in the serviceData field of the AuditLog object; later integrations use the metadata field.

For other fields in these objects, and how to interpret them, review Understand audit logs.

Log name

Cloud Audit Logs log names include resource identifiers indicating the Google Cloud project or other Google Cloud entity that owns the audit logs, and whether the log contains Admin Activity, Data Access, Policy Denied, or System Event audit logging data.

The following are the audit log names, including variables for the resource identifiers:

   projects/PROJECT_ID/logs/cloudaudit.googleapis.com%2Factivity
   projects/PROJECT_ID/logs/cloudaudit.googleapis.com%2Fdata_access
   projects/PROJECT_ID/logs/cloudaudit.googleapis.com%2Fsystem_event
   projects/PROJECT_ID/logs/cloudaudit.googleapis.com%2Fpolicy

   folders/FOLDER_ID/logs/cloudaudit.googleapis.com%2Factivity
   folders/FOLDER_ID/logs/cloudaudit.googleapis.com%2Fdata_access
   folders/FOLDER_ID/logs/cloudaudit.googleapis.com%2Fsystem_event
   folders/FOLDER_ID/logs/cloudaudit.googleapis.com%2Fpolicy

   billingAccounts/BILLING_ACCOUNT_ID/logs/cloudaudit.googleapis.com%2Factivity
   billingAccounts/BILLING_ACCOUNT_ID/logs/cloudaudit.googleapis.com%2Fdata_access
   billingAccounts/BILLING_ACCOUNT_ID/logs/cloudaudit.googleapis.com%2Fsystem_event
   billingAccounts/BILLING_ACCOUNT_ID/logs/cloudaudit.googleapis.com%2Fpolicy

   organizations/ORGANIZATION_ID/logs/cloudaudit.googleapis.com%2Factivity
   organizations/ORGANIZATION_ID/logs/cloudaudit.googleapis.com%2Fdata_access
   organizations/ORGANIZATION_ID/logs/cloudaudit.googleapis.com%2Fsystem_event
   organizations/ORGANIZATION_ID/logs/cloudaudit.googleapis.com%2Fpolicy

Service name

Vertex AI audit logs use the service name aiplatform.googleapis.com.

For a list of all the Cloud Logging API service names and their corresponding monitored resource type, see Map services to resources.

Resource types

Vertex AI audit logs use the resource type audited_resource for all audit logs.

For a list of all the Cloud Logging monitored resource types and descriptive information, see Monitored resource types.

Caller identities

The IP address of the caller is held in the RequestMetadata.caller_ip field of the AuditLog object. Logging might redact certain caller identities and IP addresses.

For information about what information is redacted in audit logs, see Caller identities in audit logs.

Enable audit logging

Admin Activity audit logs are always enabled; you can't disable them.

Data Access audit logs are disabled by default and aren't written unless explicitly enabled (the exception is Data Access audit logs for BigQuery, which can't be disabled).

For information about enabling some or all of your Data Access audit logs, see Enable Data Access audit logs.

Permissions and roles

IAM permissions and roles determine your ability to access audit logs data in Google Cloud resources.

When deciding which Logging-specific permissions and roles apply to your use case, consider the following:

  • The Logs Viewer role (roles/logging.viewer) gives you read-only access to Admin Activity, Policy Denied, and System Event audit logs. If you have just this role, you cannot view Data Access audit logs that are in the _Default bucket.

  • The Private Logs Viewer role(roles/logging.privateLogViewer) includes the permissions contained in roles/logging.viewer, plus the ability to read Data Access audit logs in the _Default bucket.

    Note that if these private logs are stored in user-defined buckets, then any user who has permissions to read logs in those buckets can read the private logs. For more information about log buckets, see Routing and storage overview.

For more information about the IAM permissions and roles that apply to audit logs data, see Access control with IAM.

View logs

You can query for all audit logs or you can query for logs by their audit log name. The audit log name includes the resource identifier of the Google Cloud project, folder, billing account, or organization for which you want to view audit logging information. Your queries can specify indexed LogEntry fields, and if you use the Log Analytics page, which supports SQL queries, then you can view your query results as a chart.

For more information about querying your logs, see the following pages:

You can view audit logs in Cloud Logging by using the Google Cloud console, the Google Cloud CLI, or the Logging API.

Console

In the Google Cloud console, you can use the Logs Explorer to retrieve your audit log entries for your Google Cloud project, folder, or organization:

  1. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Explorer:

    Go to Logs Explorer

  2. Select an existing Google Cloud project, folder, or organization.

  3. To display all audit logs, enter either of the following queries into the query-editor field, and then click Run query:

    logName:"cloudaudit.googleapis.com"
    
    protoPayload."@type"="type.googleapis.com/google.cloud.audit.AuditLog"
    
  4. To display the audit logs for a specific resource and audit log type, in the Query builder pane, do the following:

    • In Resource type, select the Google Cloud resource whose audit logs you want to see.

    • In Log name, select the audit log type that you want to see:

      • For Admin Activity audit logs, select activity.
      • For Data Access audit logs, select data_access.
      • For System Event audit logs, select system_event.
      • For Policy Denied audit logs, select policy.
    • Click Run query.

    If you don't see these options, then there aren't any audit logs of that type available in the Google Cloud project, folder, or organization.

    If you're experiencing issues when trying to view logs in the Logs Explorer, see the troubleshooting information.

    For more information about querying by using the Logs Explorer, see Build queries in the Logs Explorer. For information about summarizing log entries in the Logs Explorer by using Gemini, see Summarize log entries with Gemini assistance.

gcloud

The Google Cloud CLI provides a command-line interface to the Logging API. Supply a valid resource identifier in each of the log names. For example, if your query includes a PROJECT_ID, then the project identifier you supply must refer to the currently selected Google Cloud project.

To read your Google Cloud project-level audit log entries, run the following command:

gcloud logging read "logName : projects/PROJECT_ID/logs/cloudaudit.googleapis.com" \
    --project=PROJECT_ID

To read your folder-level audit log entries, run the following command:

gcloud logging read "logName : folders/FOLDER_ID/logs/cloudaudit.googleapis.com" \
    --folder=FOLDER_ID

To read your organization-level audit log entries, run the following command:

gcloud logging read "logName : organizations/ORGANIZATION_ID/logs/cloudaudit.googleapis.com" \
    --organization=ORGANIZATION_ID

To read your Cloud Billing account-level audit log entries, run the following command:

gcloud logging read "logName : billingAccounts/BILLING_ACCOUNT_ID/logs/cloudaudit.googleapis.com" \
    --billing-account=BILLING_ACCOUNT_ID

Add the --freshness flag to your command to read logs that are more than 1 day old.

For more information about using the gcloud CLI, see gcloud logging read.

API

When building your queries, supply a valid resource identifier in each of the log names. For example, if your query includes a PROJECT_ID, then the project identifier you supply must refer to the currently selected Google Cloud project.

For example, to use the Logging API to view your project-level audit log entries, do the following:

  1. Go to the Try this API section in the documentation for the entries.list method.

  2. Put the following into the Request body part of the Try this API form. Clicking this prepopulated form automatically fills the request body, but you need to supply a valid PROJECT_ID in each of the log names.

    {
      "resourceNames": [
        "projects/PROJECT_ID"
      ],
      "pageSize": 5,
      "filter": "logName : projects/PROJECT_ID/logs/cloudaudit.googleapis.com"
    }
    
  3. Click Execute.

Route audit logs

You can route audit logs to supported destinations in the same way that you can route other kinds of logs. Here are some reasons you might want to route your audit logs:

  • To keep audit logs for a longer period of time or to use more powerful search capabilities, you can route copies of your audit logs to Cloud Storage, BigQuery, or Pub/Sub. Using Pub/Sub, you can route to other applications, other repositories, and to third parties.

  • To manage your audit logs across an entire organization, you can create aggregated sinks that can route logs from any or all Google Cloud projects in the organization.

  • If your enabled Data Access audit logs are pushing your Google Cloud projects over your log allotments, you can create sinks that exclude the Data Access audit logs from Logging.

For instructions about routing logs, see Route logs to supported destinations.

Pricing

For more information about pricing, see Cloud Logging pricing summary.