Dataflow Worker Ip Should Not Be Public

More Info:

Ensure Dataflow jobs worker ip is not public

Risk Level

Critical

Address

Security

Compliance Standards

CBP, CISGCP, HITRUST, SOC2, GDPR, NISTCSF, PCIDSS

Triage and Remediation

Remediation

Using Console

Using CLI

To remediate the “Dataflow Worker IP Should Not Be Public” misconfiguration in GCP using GCP CLI, you can follow these step-by-step instructions:

Open the Cloud Shell in your GCP console.
Run the following command to get the list of all Dataflow jobs:

gcloud dataflow jobs list

Identify the job for which you want to remediate the misconfiguration.
Run the following command to update the job with the --no-use-public-ips flag:

gcloud dataflow jobs update <JOB_ID> --no-use-public-ips

Replace <JOB_ID> with the actual ID of the job you want to update.

Verify that the job has been updated by running the following command:

gcloud dataflow jobs describe <JOB_ID> | grep "usePublicIps"

This command should return usePublicIps: false.By following these steps, you should be able to remediate the “Dataflow Worker IP Should Not Be Public” misconfiguration in GCP using GCP CLI.

Using Python

To remediate the misconfiguration “Dataflow Worker IP Should Not Be Public” in GCP using Python, follow these steps:

Open the Google Cloud Console and select the project that you want to work on.
Go to the Dataflow section of the console.
Select the Dataflow job that you want to remediate.
Click on the “Edit” button to edit the job configuration.
In the “Networking” section, select “Custom” for the “Worker IP Configuration” option.
In the “Custom” section, select “Private” for the “Worker IP” option.
Click on the “Save” button to save the changes to the job configuration.

Here is the Python code to remediate the misconfiguration:

from googleapiclient.discovery import build
from google.oauth2 import service_account

# Set the project ID and Dataflow job ID
project_id = 'your-project-id'
job_id = 'your-job-id'

# Set the worker IP configuration to private
body = {
    'environment': {
        'workerIPAddressConfiguration': 'WORKER_IP_PRIVATE'
    }
}

# Authenticate with GCP using a service account key file
credentials = service_account.Credentials.from_service_account_file('path/to/keyfile.json')
dataflow = build('dataflow', 'v1b3', credentials=credentials)

# Update the Dataflow job configuration
request = dataflow.projects().locations().jobs().update(
    projectId=project_id,
    location='us-central1',
    jobId=job_id,
    body=body
)
response = request.execute()

print(response)

Note: Replace “your-project-id”, “your-job-id”, and “path/to/keyfile.json” with your actual project ID, Dataflow job ID, and the path to your service account key file, respectively.

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Dataflow Worker Ip Should Not Be Public

More Info:

Risk Level

Address

Compliance Standards

Triage and Remediation

Remediation

GCP Introduction

GCP Pricing

GCP Threats

GCP Misconfigurations

Resources

GCP Threats

​More Info:

​Risk Level

​Address

​Compliance Standards

​Triage and Remediation

​Remediation

More Info:

Risk Level

Address

Compliance Standards

Triage and Remediation

Remediation