Google.spanner.admin.instance.v1.instanceadmin.deleteinstance

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Event Information

• The google.spanner.admin.instance.v1.InstanceAdmin.DeleteInstance event in GCP for CloudSpanner indicates that an instance deletion operation has been initiated.
• This event signifies that a request has been made to delete a CloudSpanner instance in the specified project and location.
• The event provides information about the instance being deleted, such as its name, project ID, and location, allowing administrators to track and monitor instance deletion activities in their CloudSpanner environment.

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Examples

• Unauthorized deletion of instances: If security is impacted with google.spanner.admin.instance.v1.InstanceAdmin.DeleteInstance in GCP for CloudSpanner, it could potentially allow unauthorized users or malicious actors to delete instances. This could result in the loss of critical data and disrupt business operations.
• Data exposure: If security is impacted with google.spanner.admin.instance.v1.InstanceAdmin.DeleteInstance, it could lead to the exposure of sensitive data. For example, if an instance is deleted without proper access controls in place, it may expose confidential information to unauthorized individuals.
• Service disruption: Deleting instances without proper authorization or safeguards can lead to service disruption. This can impact the availability and reliability of applications and services that rely on the CloudSpanner database. It is crucial to ensure that only authorized personnel have the necessary permissions to perform instance deletion operations.

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Remediation

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Using Console

1. Enable VPC Service Controls:
   • Go to the GCP Console and navigate to the CloudSpanner instance.
   • Click on the “Edit” button to modify the instance settings.
   • Scroll down to the “VPC Service Controls” section and click on “Enable VPC Service Controls”.
   • Follow the prompts to configure the VPC Service Controls for the CloudSpanner instance.
   • Save the changes.
2. Implement IAM Roles and Permissions:
   • Go to the GCP Console and navigate to the CloudSpanner instance.
   • Click on the “Permissions” tab to manage IAM roles and permissions.
   • Click on “Add Member” to add the desired user or service account.
   • Select the appropriate IAM role(s) for the user or service account.
   • Click on “Save” to apply the changes.
3. Enable Audit Logging:
   • Go to the GCP Console and navigate to the CloudSpanner instance.
   • Click on the “Edit” button to modify the instance settings.
   • Scroll down to the “Audit Logging” section and click on “Enable Audit Logging”.
   • Select the desired audit logs to be enabled (e.g., Admin Activity, Data Access, System Event logs).
   • Save the changes.

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Using CLI

1. Enable VPC Service Controls for CloudSpanner:
   • Use the following command to enable VPC Service Controls for CloudSpanner:
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     Ask AI

     gcloud services vpc-peerings update --service=servicenetworking.googleapis.com --network=projects/[PROJECT_ID]/global/networks/[NETWORK_NAME] --ranges=[IP_RANGE]
     

   • Replace [PROJECT_ID] with your GCP project ID, [NETWORK_NAME] with the name of your VPC network, and [IP_RANGE] with the IP range of your VPC network.
2. Implement IAM Roles and Permissions:
   • Use the following command to grant the necessary IAM roles to the appropriate users or service accounts:
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     Ask AI

     gcloud projects add-iam-policy-binding [PROJECT_ID] --member=[MEMBER] --role=[ROLE]
     

   • Replace [PROJECT_ID] with your GCP project ID, [MEMBER] with the email address or service account of the user, and [ROLE] with the desired IAM role.
3. Enable Audit Logging for CloudSpanner:
   • Use the following command to enable audit logging for CloudSpanner:
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     Ask AI

     gcloud logging sinks create [SINK_NAME] storage.googleapis.com/[BUCKET_NAME] --log-filter="resource.type=spanner_instance"
     

   • Replace [SINK_NAME] with a name for your logging sink, and [BUCKET_NAME] with the name of the Cloud Storage bucket where you want to store the logs.

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Using Python

1. Monitoring and Alerting:
   • Use the Cloud Monitoring API to create custom metrics and alerts for monitoring CloudSpanner instances.
   • Write a Python script to periodically check the metrics and trigger alerts if any thresholds are breached.
   • Here’s an example script to create a custom metric and an alert policy using the Cloud Monitoring API:
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   Ask AI

   from google.cloud import monitoring_v3
   
   client = monitoring_v3.MetricServiceClient()
   
   # Create a custom metric descriptor
   project_name = "projects/my-project"
   metric_descriptor = {
       "type": "custom.googleapis.com/my_custom_metric",
       "metric_kind": monitoring_v3.MetricDescriptor.MetricKind.GAUGE,
       "value_type": monitoring_v3.MetricDescriptor.ValueType.DOUBLE,
       "description": "My custom metric",
       "display_name": "My Custom Metric",
   }
   client.create_metric_descriptor(name=project_name, metric_descriptor=metric_descriptor)
   
   # Create an alert policy
   alert_policy = {
       "display_name": "My Alert Policy",
       "combiner": monitoring_v3.AlertPolicy.ConditionCombinerType.AND,
       "conditions": [
           {
               "condition_threshold": {
                   "filter": 'metric.type="custom.googleapis.com/my_custom_metric"',
                   "comparison": monitoring_v3.ComparisonType.COMPARISON_GT,
                   "threshold_value": 100.0,
                   "duration": {"seconds": 60},
               },
           },
       ],
       "notification_channels": ["projects/my-project/notificationChannels/my-channel"],
   }
   client.create_alert_policy(name=project_name, alert_policy=alert_policy)
   

2. Backup and Disaster Recovery:
   • Use the Cloud Spanner Backup API to create automated backups of your CloudSpanner databases.
   • Write a Python script to schedule and manage the backup process.
   • Here’s an example script to create a backup:
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   Ask AI

   from google.cloud import spanner_admin_database_v1
   
   client = spanner_admin_database_v1.DatabaseAdminClient()
   
   # Create a backup
   backup_name = "projects/my-project/instances/my-instance/backups/my-backup"
   database_name = "projects/my-project/instances/my-instance/databases/my-database"
   backup = spanner_admin_database_v1.Backup(
       database=database_name,
       expire_time=spanner_admin_database_v1.timestamp.Timestamp(),
   )
   client.create_backup(parent="projects/my-project/instances/my-instance", backup_id="my-backup", backup=backup)
   

3. Access Control and Permissions:
   • Use the Cloud Identity and Access Management (IAM) API to manage access control and permissions for CloudSpanner resources.
   • Write a Python script to grant or revoke permissions for users or service accounts.
   • Here’s an example script to grant a user the “Cloud Spanner Admin” role:
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   Ask AI

   from google.cloud import iam
   
   client = iam.IAMClient()
   
   # Grant a user the "Cloud Spanner Admin" role
   member = "user:[email protected]"
   role = "roles/spanner.admin"
   policy = client.get_policy(resource="projects/my-project/instances/my-instance")
   policy.bindings.add(role=role, members=[member])
   client.set_policy(resource="projects/my-project/instances/my-instance", policy=policy)