Free Practice Questions for Google Professional-Cloud-Security-Engineer Exam

To evaluate Google Cloud Platform (GCP) for PCI compliance and identify Google's inherent controls, you should review the "Google Cloud Platform: Customer Responsibility Matrix". This document provides detailed information about the shared responsibility model, outlining the security controls managed by Google and those that are the responsibility of the customer.

Steps to access and use the document:

Access the Document:

Go to the Google Cloud compliance resource center.

Locate the "Customer Responsibility Matrix" for PCI DSS compliance.

Review Inherent Controls:

The document lists various controls and specifies whether they are managed by Google, the customer, or both.

It covers different aspects such as infrastructure security, data protection, and compliance requirements.

Analyze PCI Compliance:

Use the matrix to understand which PCI DSS requirements are inherently addressed by Google Cloud.

Identify the controls you need to implement and manage as a customer to ensure full compliance.

By reviewing this document, you can gain a comprehensive understanding of the inherent controls provided by Google Cloud and the responsibilities you must fulfill to achieve PCI compliance.

Google Cloud Compliance Documentation

PCI DSS Compliance on Google Cloud

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

o ensure that users can only access the data in a BigQuery table during working hours, you can assign the BigQuery Data Viewer role with an IAM condition that specifies the allowed access times. This method leverages IAM Conditions, which allow you to define and enforce time-based access policies. Here's how to do it:

Identify the BigQuery Table: Determine which BigQuery table(s) require restricted access.

Create an IAM Policy with Conditions: Define an IAM policy that includes a condition for time-based access. You can do this using the Google Cloud Console, gcloud command-line tool, or directly editing the IAM policy JSON.

Specify Working Hours: In the IAM condition, specify the time frame during which access is allowed. For example, you can set access to be allowed from 9 AM to 5 PM on weekdays.

Assign the Role with Conditions: Apply the policy to the users or groups who need access. Ensure that the condition is correctly attached to the BigQuery Data Viewer role.

Example using gcloud:

gcloud projects add-iam-policy-binding [PROJECT_ID] \

--member=user:[USER_EMAIL] \

--role=roles/bigquery.dataViewer \

--condition=expression="(request.time.getFullYear() == 2024) && (request.time.getDayOfWeek() in [1, 2, 3, 4, 5]) && (request.time.getHours() >= 9) && (request.time.getHours() < 17)",title="Working hours condition",description="Access limited to working hours"

References

Google Cloud IAM Conditions

Google Cloud BigQuery IAM Roles

Objective: Ensure that a Cloud Storage bucket in Project A can only be readable from Project B and prevent data access or copying to Cloud Storage buckets outside the network, even with correct credentials.

Solution: Use VPC Service Controls to create a security perimeter.

Steps:

Step 1: Open the Google Cloud Console.

Step 2: Navigate to the VPC Service Controls page.

Step 3: Create a new service perimeter.

Step 4: Add Project A and Project B to the service perimeter.

Step 5: Include Cloud Storage service in the perimeter configuration.

Step 6: Define access levels to ensure that only resources within the perimeter can access the Cloud Storage bucket.

By setting up a VPC Service Controls perimeter, you can enforce security boundaries that restrict data access and movement to within defined projects, providing an extra layer of protection beyond IAM permissions.

To handle PII data ingestion and ensure both redaction and re-identification for analytics purposes, you can use Cloud Data Loss Prevention (DLP) with appropriate techniques for masking and encryption.

Cloud Data Loss Prevention (DLP) with Cryptographic Hashing (C):

Use Cloud DLP to apply cryptographic hashing to PII data. Hashing transforms the data into a fixed-length string that is not directly readable, providing a layer of obfuscation. This helps in masking the PII while retaining the ability to verify data integrity.

Cloud Data Loss Prevention (DLP) with Deterministic Encryption using AES-SIV (E):

Apply deterministic encryption using AES-SIV through Cloud DLP. Deterministic encryption ensures that the same input will always produce the same encrypted output, allowing you to re-identify the PII when necessary. This method enables secure encryption while allowing data re-identification for analytics.

By combining these two approaches, you can effectively mask PII for privacy protection and later re-identify it when required for analysis.

References

Cloud Data Loss Prevention Documentation

Data Redaction and Masking Techniques

Review Admin Activity logs:

Admin Activity logs contain entries for API calls that modify or read the configuration or metadata of resources.

These logs are useful for monitoring and auditing administrative actions, including those that could indicate malicious activity on a Cloud SQL instance.

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:

To ensure servers do not have any direct communication with the public internet, they must be configured without a public IP address.

VPC and Private Subnet: A Virtual Private Cloud (VPC) network provides the isolated, internal network structure. A subnet is the logical partition within the VPC.

Private IP Address: Assigning only a private IP address to the database servers ensures they can only communicate internally within the VPC (or connected on-premises networks) and cannot directly connect to or be connected from the public internet.

Extracts:

"Resources in a VPC network can be assigned two types of IP addresses: internal (private) and external (public). If a VM is not assigned an external IP address, it can only communicate internally with other resources in the VPC network..." (Source 6.1)

Option A and C involve assigning a public IP address, which violates the "no direct communication with the public internet" rule. Option D uses NAT to provide outbound internet connectivity, which also violates the requirement.

De-identifying sensitive data Cloud Data Loss Prevention (DLP) can de-identify sensitive data in text content, including text stored in container structures such as tables. De-identification is the process of removing identifying information from data. The API detects sensitive data such as personally identifiable information (PII), and then uses a de-identification transformation to mask, delete, or otherwise obscure the data. For example, de-identification techniques can include any of the following: Masking sensitive data by partially or fully replacing characters with a symbol, such as an asterisk (*) or hash (#). Replacing each instance of sensitive data with a token, or surrogate, string. Encrypting and replacing sensitive data using a randomly generated or pre-determined key. When you de-identify data using the CryptoReplaceFfxFpeConfig or CryptoDeterministicConfig infoType transformations, you can re-identify that data, as long as you have the CryptoKey used to originally de-identify the data. https://cloud.google.com/dlp/docs/deidentify-sensitive-data