To remediate the misconfiguration of not monitoring Storage ACL operations count in GCP using GCP CLI, you can follow the below steps:

1. Open the Cloud Shell in the GCP console.

2. Run the following command to create a new bucket in the project:

   gsutil mb gs://<bucket-name>
   

3. Run the following command to enable logging for the bucket:

   gsutil logging set on -b gs://<bucket-name> gs://<log-bucket-name>/<log-object-prefix>
   

   Replace <bucket-name> with the name of the bucket that you created in step 2, and <log-bucket-name> and <log-object-prefix> with the name of the bucket and object prefix where you want to store the logs.

4. Run the following command to create a new sink for the logs:

   gcloud logging sinks create <sink-name> storage.googleapis.com/<bucket-name> --include-children --log-filter='resource.type="gcs_bucket" protoPayload.methodName="storage.buckets.updateIamPolicy" protoPayload.resourceName="projects/_/buckets/<bucket-name>"'
   

   Replace <sink-name> with a name for the new sink, and <bucket-name> with the name of the bucket that you created in step 2.

5. Run the following command to grant the logging.logWriter role to the sink service account:

   gcloud projects add-iam-policy-binding <project-id> --member=serviceAccount:<sink-service-account> --role=roles/logging.logWriter
   

   Replace <project-id> with the ID of the project that contains the bucket, and <sink-service-account> with the email address of the service account that was created for the sink.

6. Verify that the sink is working correctly by updating the IAM policy for the bucket:

   gsutil iam ch allUsers:objectViewer gs://<bucket-name>
   

7. Check the logs in the log bucket to verify that the sink is working correctly:

   gsutil ls gs://<log-bucket-name>/<log-object-prefix>
   

   This should show a log entry for the IAM policy update that you performed in step 6.

By following these steps, you should be able to remediate the misconfiguration of not monitoring Storage ACL operations count in GCP using GCP CLI.

To remediate the misconfiguration “Cloud Monitoring Should Monitor Storage ACL Operations Count” for GCP using Python, follow these steps:

1. Open the Google Cloud Console and select the project where the storage bucket is located.

2. Go to the Cloud Storage section.

3. Find the bucket that needs to be monitored and click on it.

4. Click on the “Permissions” tab.

5. Click on the “Add Members” button.

6. Add the service account that will be used for monitoring. This service account must have the Cloud Monitoring Agent role.

7. Click on the “Add” button.

8. Open the Cloud Shell from the Google Cloud Console.

9. Install the Google Cloud Storage Python library by running the following command:

   pip install google-cloud-storage
   

10. Create a Python script that will monitor the ACL operations count for the storage bucket. Here’s an example script:

from google.cloud import monitoring_v3
from google.oauth2 import service_account
from google.cloud import storage

# Set up credentials
credentials = service_account.Credentials.from_service_account_file(
    '<path-to-service-account-key>')

# Set up the Monitoring client
client = monitoring_v3.MetricServiceClient(credentials=credentials)

# Set up the Storage client
storage_client = storage.Client(credentials=credentials)

# Set up the metric descriptor
project_name = '<project-name>'
metric_type = 'storage.googleapis.com/storage/bucket/acl/operation_count'
metric_descriptor = monitoring_v3.types.MetricDescriptor()
metric_descriptor.type = metric_type
metric_descriptor.metric_kind = (
    monitoring_v3.enums.MetricDescriptor.MetricKind.CUMULATIVE)
metric_descriptor.value_type = (
    monitoring_v3.enums.MetricDescriptor.ValueType.INT64)
metric_descriptor.description = 'The number of ACL operations on a Cloud Storage bucket.'
metric_descriptor.unit = '1'
client.create_metric_descriptor(
    name=client.project_path(project_name),
    metric_descriptor=metric_descriptor)

# Set up the time series
bucket_name = '<bucket-name>'
time_series = monitoring_v3.types.TimeSeries()
time_series.metric.type = metric_type
time_series.resource.type = 'gcs_bucket'
time_series.resource.labels['bucket_name'] = bucket_name
time_series.resource.labels['project_id'] = project_name

# Get the ACL operations count
bucket = storage_client.bucket(bucket_name)
acl_operations_count = bucket.acl.user("allUsers").get()["operationCount"]

# Set the data point
point = monitoring_v3.types.Point()
point.value.int64_value = acl_operations_count
now = time.time()
point.interval.end_time.seconds = int(now)
point.interval.end_time.nanos = int(
    (now - point.interval.end_time.seconds) * 10**9)
time_series.points.append(point)

# Write the time series data
client.create_time_series(
    name=client.project_path(project_name),
    time_series=[time_series])

Replace the placeholders <path-to-service-account-key>, <project-name>, and <bucket-name> with the actual values.

11. Save the Python script and run it from the Cloud Shell by running the following command:

    python <script-name>.py
    

    Replace <script-name> with the actual name of the Python script.

12. Verify that the metric is being collected by going to the Cloud Monitoring section of the Google Cloud Console and checking that the metric is listed under the “Metrics Explorer” tab.