Pass the Google Cloud Certified Professional-Cloud-Security-Engineer Questions and answers with Dumpstech

To ensure that an external user cannot gain access to an internal application on Google App Engine even if an employee’s password is compromised, configure Cloud Identity-Aware Proxy (IAP).

Enable IAP:

Go to the Cloud Console, navigate to the App Engine application, and select "Identity-Aware Proxy".

Enable IAP for the application.

Configure Access Policies:

Set up access policies to restrict who can access the application.

Use IAM roles to grant access only to specific users or groups.

Enforce Authentication:

IAP enforces Google authentication, ensuring that users must log in with their GSuite credentials.

Enable Multi-Factor Authentication (MFA):

Enforce 2FA for all GSuite users to add an extra layer of security.

Advantages:

Protection against Compromised Credentials: Even if passwords are compromised, attackers cannot access the application without passing IAP authentication.

Centralized Access Management: Easily manage and monitor access through IAM and IAP policies.

Identity-Aware Proxy Overview

Setting up IAP

Comprehensive and Detailed Explanation From Exact Extract:

The key requirement is restricting access based on the client device (i.e., "corporate computers"). Context-Aware Access (CAA) is the specific Google Cloud tool designed to enforce access based on contextual factors, including the device security status or IP address.

Context Restriction: Context-Aware Access allows you to define an Access Level based on attributes like device policy compliance, operating system, or IP address range—this addresses the "corporate computers" requirement.

Isolation and Control: The Access Level is then enforced via an Organization Policy applied at the Project Level (or the folder/organization level), which fulfills the requirement to isolate access to Cloud Storage for this project and restrict the access to specific resources (Cloud Storage).

VPC Service Controls (VPC SC) (Option B) are great for isolating projects and preventing data exfiltration, but its primary access restriction mechanisms are based on IP range, not fine-grained device security posture and user identity together, making CAA the more precise tool for device-specific enforcement. Also, applying VPC SC ingress/egress based on IP addresses for end-user access can be complex and less flexible than CAA.

IAM (Option D) only controls who (identity) can access a resource, not where or how (context) they are accessing it from.

Extracts:

"Context-Aware Access (CAA) integrates with Google Workspace or Cloud Identity to enforce granular access to Google Cloud resources based on a user's context, such as their location, device security status, and IP address." (Source 7.1)

"To enforce CAA for Google Cloud resources like Cloud Storage, you create an Access Level that defines the required context (e.g., only corporate-managed devices) and apply it via an Organization Policy constraint (e.g., iam.allowedServices) at the project level." (Source 7.2)

"CAA allows you to restrict access based on the device security posture, a key requirement for enforcing 'corporate computer' access." (Source 7.3)

When a vulnerability patch is released for a running container in Google Kubernetes Engine (GKE), the recommended approach is to update the application code or apply the patch directly to the codebase. Then, a new container image should be built incorporating these changes. After building the new image, it should be deployed to replace the running containers. This method ensures that the containers run the updated, secure code.

Steps:

Update Application Code: Modify the application code or dependencies to incorporate the vulnerability patch.

Build New Image: Use a tool like Docker to build a new container image with the updated code.

Push New Image: Push the new container image to the Container Registry.

Update Deployments: Update the Kubernetes deployment to use the new image. This can be done by modifying the image tag in the deployment YAML file.

Redeploy Containers: Apply the updated deployment configuration using kubectl apply -f .yaml, which will redeploy the containers with the new image.

https://cloud.google.com/bigquery/docs/scan-with-dlp

Cloud Data Loss Prevention API allows to detect and redact or remove sensitive data before the comments or reviews are published. Cloud DLP will read information from BigQuery, Cloud Storage or Datastore and scan it for sensitive data.

Uniform Bucket-Level Access: Enable uniform bucket-level access for all your Cloud Storage buckets. This feature ensures that access control is applied consistently at the bucket level, simplifying management and improving security.

Domain Restricted Sharing: Enforce domain-restricted sharing through an organization policy. This policy ensures that only users within your organization’s domain can access the data in the buckets, preventing public exposure.

Policy Enforcement: Apply the necessary IAM policies and ensure that no buckets are configured to allow public access. This combination of settings ensures that data in Cloud Storage buckets remains private and accessible only to authorized users within your organization. References:

Google Cloud - Uniform Bucket-Level Access

Google Cloud - Organization Policy Service

To comply with the regulation to keep instance logging data within Europe, specifically in the Netherlands (region europe-west4), you need to configure Cloud Logging to ensure that all logs are stored in a specified region. Here are the steps to achieve this:

Create a Cloud Storage Bucket:

Go to the Google Cloud Console.

Navigate to Cloud Storage and create a new bucket.

Set the location type to "Region" and select "europe-west4" as the region.

Configure Log Sink:

Go to the Logging section in the Google Cloud Console.

Create a new log sink and configure it to export logs to the Cloud Storage bucket you just created.

Ensure the sink includes all logs you want to keep within europe-west4.

Verify Configuration:

Ensure that the logs are being exported to the Cloud Storage bucket by checking the contents of the bucket.

This setup ensures that all your logging data remains within the specified region, adhering to compliance requirements.

References

Creating and Managing Log Sinks

Cloud Storage Locations

To monitor and get notified in case the script causing the Compute Engine instance to crash is executed again, you should create an Alerting Policy in Stackdriver (now known as Google Cloud Monitoring). The Process Health condition can be set to monitor the number of executions of the script and ensure it remains below the desired threshold. By enabling notifications, you will be alerted if this threshold is exceeded.

Step-by-Step:

Log Script Executions: Ensure that the script execution is logged.

Create a User-Defined Metric: Go to Google Cloud Console > Logging > Logs-based Metrics, and create a new user-defined metric that counts the number of times the script executes.

Set Up Alerting Policy:

Navigate to Google Cloud Console > Monitoring > Alerting.

Click on “Create Policy”.

Add a condition and select “Logs-based Metric”.

Configure the condition to trigger when the number of script executions exceeds the threshold.

Configure Notifications: Add notification channels (email, SMS, etc.) to the alerting policy.

Save and Test: Save the policy and test to ensure notifications are received when the script is executed beyond the threshold.

Google Cloud Logging User-defined Metrics

Google Cloud Monitoring Alerting Policies

Assured Workloads for Google Cloud allows you to deploy regulated workloads with data residency, access, and support requirements. It helps you configure your environment in a manner that aligns with specific compliance frameworks and standards.

To ensure user-uploaded comments and product reviews do not include sensitive data before publication, use the Cloud Data Loss Prevention (DLP) API.

Enable DLP API:

Go to the Cloud Console and navigate to APIs & Services > Library.

Search for "Data Loss Prevention API" and enable it.

Configure DLP API:

Create an inspection template specifying the types of sensitive data to detect.

Set up de-identification templates if you want to redact or mask sensitive data.

Implement DLP in Application:

Use the Google Cloud DLP Client Library for the desired programming language.

Send the text data to the DLP API for inspection before saving or publishing.

from google.cloud import dlp_v2 dlp_client = dlp_v2.DlpServiceClient() parent = f"projects/{project_id}" item = {"value": "User comment text here"} inspect_config = {"info_types": [{"name": "PERSON_NAME"}, {"name": "CREDIT_CARD_NUMBER"}]} response = dlp_client.inspect_content(parent=parent, inspect_config=inspect_config, item=item)

Cloud Data Loss Prevention API Documentation

DLP API Client Libraries

For a client concerned about the control of their encryption keys and not wanting to store these keys within the same cloud service provider (CSP) as the data, the following solutions are suitable:

Customer-supplied encryption keys (A):

With customer-supplied encryption keys, clients manage their own encryption keys outside of Google Cloud and supply them to encrypt and decrypt data. This ensures that the keys are not stored in Google Cloud, providing full control over the key management process.

Cloud External Key Manager (D):

Cloud External Key Manager (EKM) allows clients to integrate an external key management system (KMS) with Google Cloud services. This setup enables the client to keep their encryption keys outside Google Cloud while still allowing the data to be encrypted and decrypted within Google Cloud services. This method offers an additional layer of security and control over the encryption keys.

These options provide robust solutions for clients requiring external key management and enhanced control over their encryption processes.

References

Customer-Supplied Encryption Keys

Cloud External Key Manager