Storage.buckets.create

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Event Information

• The storage.buckets.create event in GCP for CloudStorage refers to the event triggered when a new storage bucket is created in the Cloud Storage service.
• This event indicates that a user or application has successfully created a new bucket to store objects in Cloud Storage.
• It can be used to track and monitor the creation of buckets, allowing administrators to keep an audit trail of bucket creation activities in their GCP environment.

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Examples

1. Unauthorized access: If security is impacted with storage.buckets.create in GCP for Cloud Storage, it could potentially allow unauthorized users to create new storage buckets. This can lead to the creation of unsecured storage containers, which may result in data breaches or unauthorized access to sensitive information.
2. Data leakage: If security is impacted with storage.buckets.create in GCP for Cloud Storage, it could enable malicious actors to create buckets and store sensitive data without proper access controls. This can lead to data leakage, where confidential information is exposed to unauthorized individuals or entities.
3. Resource abuse: If security is impacted with storage.buckets.create in GCP for Cloud Storage, it could be exploited to create a large number of storage buckets, leading to resource abuse. This can result in increased costs, performance degradation, and potential denial of service attacks, impacting the overall availability and performance of the Cloud Storage service.

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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() method from google-cloud-monitoring to create a metric for the bucket.

from google.cloud import storage
from google.cloud import logging_v2
from google.cloud import monitoring_v3

def enable_logging_and_monitoring(bucket_name, project_id):
    storage_client = storage.Client()
    bucket = storage_client.get_bucket(bucket_name)
    
    logging_client = logging_v2.LoggingServiceV2Client()
    logging_client.create_sink(
        project_id,
        f"storage-{bucket_name}-sink",
        filter_=f'resource.type="gcs_bucket" AND resource.labels.bucket_name="{bucket_name}"',
        destination=f"storage.googleapis.com/projects/{project_id}/buckets/{bucket_name}"
    )
    
    monitoring_client = monitoring_v3.MetricServiceClient()
    monitoring_client.create_metric(
        project_id,
        {
            "type": "logging.googleapis.com/user/{bucket_name}",
            "labels": {
                "bucket_name": bucket_name
            },
            "metric_kind": "GAUGE",
            "value_type": "DOUBLE",
            "unit": "1",
            "display_name": f"Storage Bucket {bucket_name} Size",
            "description": f"Size of the {bucket_name} bucket in bytes"
        }
    )
    
    print(f"Logging and monitoring enabled for bucket: {bucket_name}")

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