Storage.buckets.delete

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Event Information

1. The storage.buckets.delete event in GCP for Cloud Storage indicates that a bucket has been deleted in the Cloud Storage service.
2. This event is triggered when a user or an automated process initiates the deletion of a bucket.
3. It is important to note that the deletion of a bucket is irreversible and all objects and data within the bucket will be permanently deleted.

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Examples

1. Unauthorized deletion: If security is impacted with storage.buckets.delete in GCP for Cloud Storage, one example could be an unauthorized user gaining access to the necessary permissions and deleting critical buckets. This could result in the loss of important data and disrupt business operations.
2. Data loss: Another example could be accidental deletion of buckets containing valuable data. If proper access controls and safeguards are not in place, a user with delete permissions could mistakenly delete a bucket, leading to permanent data loss.
3. Compliance violations: The deletion of buckets without proper documentation and audit trails can lead to compliance violations. Organizations that need to adhere to specific regulations, such as GDPR or HIPAA, may face penalties if data is deleted without proper authorization and record-keeping.

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Remediation

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Using Console

1. Enable versioning for Cloud Storage buckets:
   • Go to the GCP Console and navigate to the Cloud Storage section.
   • Select the bucket for which you want to enable versioning.
   • Click on the “Edit bucket permissions” button.
   • In the “Bucket permissions” tab, click on the “Add members” button.
   • Add the appropriate IAM member with the necessary permissions.
   • Click on the “Add” button to save the changes.
2. Implement access controls for Cloud Storage buckets:
   • Go to the GCP Console and navigate to the Cloud Storage section.
   • Select the bucket for which you want to implement access controls.
   • Click on the “Edit bucket permissions” button.
   • In the “Bucket permissions” tab, click on the “Add members” button.
   • Add the appropriate IAM member with the necessary permissions.
   • Click on the “Add” button to save the changes.
3. Enable audit logging for Cloud Storage buckets:
   • Go to the GCP Console and navigate to the Cloud Storage section.
   • Select the bucket for which you want to enable audit logging.
   • Click on the “Edit bucket permissions” button.
   • In the “Bucket permissions” tab, click on the “Add members” button.
   • Add the appropriate IAM member with the necessary permissions.
   • Click on the “Add” button to save the changes.

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Using CLI

1. Enable versioning for the affected bucket:
   • Use the following command to enable versioning for a specific bucket:
     Copy

     Ask AI

     gsutil versioning set on gs://[BUCKET_NAME]
     

2. Set appropriate access controls for the bucket:
   • Use the following command to set the bucket’s access control to private:
     Copy

     Ask AI

     gsutil iam ch allUsers:objectViewer gs://[BUCKET_NAME]
     

3. Enable object lifecycle management to automatically delete outdated objects:
   • Use the following command to set a lifecycle rule for the bucket:
     Copy

     Ask AI

     gsutil lifecycle set [LIFECYCLE_CONFIG_FILE] gs://[BUCKET_NAME]
     

     Replace [LIFECYCLE_CONFIG_FILE] with the path to a JSON file containing the lifecycle configuration.

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Using Python

1. Enable versioning for Cloud Storage buckets:
   • Use the google-cloud-storage library to interact with Cloud Storage in Python.
   • Use the get_bucket() method to retrieve the bucket object.
   • Use the versioning_enabled property to check if versioning is already enabled.
   • If versioning is not enabled, use the enable_versioning() method to enable it.

from google.cloud import storage

def enable_versioning(bucket_name):
    storage_client = storage.Client()
    bucket = storage_client.get_bucket(bucket_name)
    
    if not bucket.versioning_enabled:
        bucket.enable_versioning()
        print(f"Versioning enabled for bucket: {bucket_name}")
    else:
        print(f"Versioning already enabled for bucket: {bucket_name}")

# Usage
enable_versioning("my-bucket")

2. Set appropriate access controls for Cloud Storage buckets:
   • Use the google-cloud-storage library to interact with Cloud Storage in Python.
   • Use the get_bucket() method to retrieve the bucket object.
   • Use the iam property to access the IAM policies of the bucket.
   • Use the bindings property to modify the access control bindings.
   • Use the add_member() method to add a new member with the desired role.

from google.cloud import storage

def set_bucket_acl(bucket_name, member, role):
    storage_client = storage.Client()
    bucket = storage_client.get_bucket(bucket_name)
    
    policy = bucket.iam.policy
    policy.bindings.append({"role": role, "members": [member]})
    
    bucket.iam.policy = policy
    bucket.iam.save()
    
    print(f"Added member '{member}' with role '{role}' to bucket: {bucket_name}")

# Usage
set_bucket_acl("my-bucket", "user:[email protected]", "roles/storage.objectViewer")

3. Enable logging and monitoring for Cloud Storage buckets:
   • Use the google-cloud-logging library to interact with Cloud Logging in Python.
   • Use the google-cloud-monitoring library to interact with Cloud Monitoring in Python.
   • Use the get_bucket() method from google-cloud-storage to retrieve the bucket object.
   • Use the create_sink() method from google-cloud-logging to create a log sink for the bucket.
   • Use the create_metric_descriptor() method from google-cloud-monitoring to create a custom metric descriptor for the bucket.

from google.cloud import storage, logging, monitoring_v3

def enable_logging(bucket_name, project_id):
    storage_client = storage.Client()
    logging_client = logging.Client(project=project_id)
    monitoring_client = monitoring_v3.MetricServiceClient()
    
    bucket = storage_client.get_bucket(bucket_name)
    
    log_sink = logging_client.create_sink(
        "my-log-sink",
        f"storage.googleapis.com/projects/{project_id}/buckets/{bucket_name}",
        filter_=f'resource.type="gcs_bucket" AND resource.labels.bucket_name="{bucket_name}"'
    )
    
    metric_descriptor = monitoring_client.create_metric_descriptor(
        "my-metric",
        "Custom metric for Cloud Storage bucket",
        "Custom metric for Cloud Storage bucket",
        "custom.googleapis.com/storage/bucket",
        metric_kind=monitoring_v3.MetricDescriptor.MetricKind.GAUGE,
        value_type=monitoring_v3.MetricDescriptor.ValueType.INT64,
        unit="1",
        labels=[
            monitoring_v3.LabelDescriptor(
                key="bucket_name",
                value_type=monitoring_v3.LabelDescriptor.ValueType.STRING,
                description="The name of the Cloud Storage bucket"
            )
        ]
    )
    
    print(f"Logging and monitoring enabled for bucket: {bucket_name}")

# Usage
enable_logging("my-bucket", "my-project-id")