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

The problem requires a cost-effective, scalable, and secure strategy to anonymize 3 TB of PII in BigQuery and Cloud SQL.

Cloud Sensitive Data Protection (Cloud DLP): Cloud DLP is Google Cloud's fully managed service designed for discovering, classifying, and de-identifying sensitive data, including PII, across various Google Cloud services and on-premises environments. It is specifically built for scale and security.

Extract Reference: "Sensitive Data Protection helps you discover, classify, and de-identify sensitive data." and "Sensitive Data Protection includes advanced de-identification techniques, such as tokenization, masking, and format-preserving encryption, to help you protect your sensitive data while preserving its utility for analysis." (Google Cloud Documentation: "Sensitive Data Protection overview" - https://cloud.google.com/sensitive-data-protection/docs/overview)

Data Profiling: Cloud DLP's data profiling feature automatically scans your data (e.g., BigQuery tables, Cloud SQL databases) to identify where sensitive data resides and to understand its characteristics. This is crucial for developing an effective de-identification strategy.

Extract Reference: "Data profiles provide metrics and insights about your sensitive and high-risk data, helping you make informed decisions about data protection, access, and storage." and "When you configure Sensitive Data Protection to profile data in BigQuery, Cloud Storage, and Datastore, it automatically scans your data for sensitive information." (Google Cloud Documentation: "About data profiles" - https://cloud.google.com/sensitive-data-protection/docs/concepts-data-profiles)

De-identification Templates and Custom Configurations: After profiling identifies the PII, Cloud DLP offers various de-identification methods, which can be applied using pre-built templates or custom configurations. This allows for flexible and targeted anonymization. For BigQuery specifically, DLP can integrate with remote functions for de-identification at query time, minimizing data movement.

Extract Reference: "De-identification techniques, like encryption, obfuscate raw sensitive identifiers in your data. These techniques let you preserve the utility of your data for joining or analytics, while reducing the risk of handling the data." and "You can use this tutorial to replace that pipeline with a SQL query for only re-identification or both de-identification and re-identification." (Google Cloud Documentation: "De-identify BigQuery data at query time | Sensitive Data Protection Documentation" - https://cloud.google.com/sensitive-data-protection/docs/deidentify-bq-tutorial)

Let's evaluate the other options:

B. Copy small subset... create a custom anonymization script... apply the script to the entire 3 TB dataset: Relying on a "custom anonymization script" for 3 TB of data is generally not scalable, cost-effective, or secure compared to a managed service like Cloud DLP. Custom scripts require significant development, testing, maintenance, and robust error handling for large datasets, and might introduce security vulnerabilities.

C. Export all 3TB of data... to Cloud Storage... anonymize... Re-import: While feasible, exporting and re-importing 3 TB of data is a very time-consuming and potentially costly process due to data transfer and storage operations. Cloud DLP can often process data in-place or integrate more efficiently, especially with BigQuery. This option might not be the most cost-effective or efficient.

D. Inspect a representative sample... develop a custom script: Similar to option B, this relies on a custom script, which is less ideal for scalability, cost-effectiveness, and security than a managed service like Cloud DLP for 3 TB of sensitive data. Sample inspection is a good initial step, but the subsequent custom script for anonymization is the weak point.

Therefore, using Cloud DLP's data profiling to inform a de-identification strategy with its managed templates and configurations is the most appropriate, scalable, secure, and cost-effective approach for anonymizing PII in BigQuery and Cloud SQL.

To enable Engineering Group A to attach a Compute Engine instance to a specific subnet (10.1.1.0/24) in a Shared VPC, you should grant the Compute Network User Role at the subnet level. This role allows users to use the subnetwork for their instances without giving them broader permissions at the project level.

Step-by-Step:

Identify the Subnet: Locate the subnet (10.1.1.0/24) in the host project.

Grant Role:

Navigate to the GCP Console > VPC network > VPC networks.

Select the Shared VPC host project and locate the specific subnet.

Click on "Edit" and go to the "IAM & Admin" section.

Assign the "Compute Network User" role to Engineering Group A at the subnet level.

Verification: Ensure that Engineering Group A can now attach Compute Engine instances to the specified subnet.

Shared VPC Overview

Compute Network User Role

Comprehensive and Detailed Explanation From Exact Extract:

The requirements are met by granting access at the highest point in the resource hierarchy that encompasses all the necessary resources, using the least privileged role required.

Least Privilege Role: The team needs to read data and not modify or delete it. The roles/bigquery.dataViewer role is the correct least privileged role for read-only access to data.

Minimize Operational Overhead: Granting the role at the Folder level ensures that the access is automatically inherited by all current and future projects within that folder, drastically reducing the operational overhead compared to granting the role per project (C) or per dataset (A).

Scope: The Folder scope (Data Warehouse folder) is the container for all BigQuery data in the projects within the folder, making it the ideal single point of granting access.

Extracts:

"IAM roles are inherited down the resource hierarchy... Granting a role at the folder level will grant the principal that role across all projects within that folder, including any projects created in the future." (Source 10.1)

"The BigQuery Data Viewer (roles/bigquery.dataViewer) role grants permission to read data in BigQuery tables and views... It does not grant permissions to modify or delete the data, adhering to the principle of least privilege for read-only tasks." (Source 10.2)

To migrate a legacy application to GCP without knowing what ports it uses and ensuring the environment is secure, the best approach is to use a "Lift & Shift" method in an isolated project and analyze the traffic using VPC Flow logs. Here’s a step-by-step explanation:

Isolated Project:

Create a new, isolated project within your GCP environment to host the legacy application. This isolation ensures that any potential misconfigurations do not affect other projects.

Lift & Shift:

Migrate the application as-is (lift and shift) to the new isolated project. This involves moving the application without altering its architecture.

Enable Internal TCP Traffic:

Configure VPC Firewall rules to allow all internal TCP traffic within the VPC network. This step ensures that the application components can communicate internally without interruption.

Use VPC Flow Logs:

Enable VPC Flow logs to capture information about the traffic to and from your application. VPC Flow logs provide details about the source, destination, port, and protocol of the traffic.

Analyze Traffic:

Analyze the VPC Flow logs to identify the necessary ports and protocols used by the application.

Based on this analysis, create specific firewall rules to allow only the required traffic, thereby tightening security.

Implementation Steps:

Navigate to the VPC network section in the GCP Console.

Create a new VPC or use an existing one, and configure firewall rules to allow internal TCP traffic.

Enable VPC Flow logs from the VPC network settings.

Migrate your application to the new project.

Monitor and analyze the VPC Flow logs to refine your firewall rules.

By following these steps, you can safely migrate the application, understand its network requirements, and secure it appropriately in the new GCP environment.

Google Cloud VPC Documentation

VPC Flow Logs Documentation

Dedicated Interconnect provides a high-bandwidth (up to 80 Gbps per connection) and secure connection between your on-premises network and Google Cloud. It ensures reliable and high-speed data transfer, meeting the requirement of at least 50 Gbps bandwidth.

Steps:

Set Up Dedicated Interconnect: Order a Dedicated Interconnect connection through the Google Cloud Console.

Configure VLAN Attachments: Set up VLAN attachments to segment traffic between your on-premises network and Google Cloud.

Establish BGP Sessions: Configure BGP sessions for dynamic routing and failover.

Rotating a user-managed Service Account key involves creating a new key, updating your application to use the new key, and then deleting the old key to maintain security. Here’s the step-by-step process:

Create a New Key: Use the Google Cloud Console or gcloud command-line tool to create a new key for the service account. This generates a new key pair and provides you with the private key.

gcloud iam service-accounts keys create new-key-file.json --iam-account=YOUR_SERVICE_ACCOUNT_EMAIL

Update Application: Update your application configuration to use the new key. This might involve replacing the old key file with the new one or updating the environment variables or configurations that point to the key file.

Delete the Old Key: Once you have confirmed that the application is working correctly with the new key, delete the old key from the service account to ensure it cannot be used for unauthorized access.

gcloud iam service-accounts keys delete OLD_KEY_ID --iam-account=YOUR_SERVICE_ACCOUNT_EMAIL

This process ensures that your service account keys are regularly rotated, reducing the risk of key compromise.

References

Managing Service Account Keys

Service Account Key Rotation

VPC Peering is between organizations not between Projects in an organization. That is Shared VPC. In this case, both projects are in same organization so having VPC Service Controls around both projects with necessary rules should be fine.

https://cloud.google.com/vpc-service-controls/docs/overview

Objective: Implement an encryption at-rest strategy that balances key management complexity and control for sensitive and non-sensitive data, ensuring FIPS 140-2 L1 compliance.

Solution: Use Google default encryption for non-sensitive data and Cloud Key Management Service (KMS) for sensitive data.

Steps:

Step 1: Store non-sensitive data using Google Cloud’s default encryption, which automatically encrypts data at rest without additional configuration.

Step 2: For sensitive data, use Cloud KMS to create and manage encryption keys.

Step 3: Configure key rotation policies for the keys managed by Cloud KMS to meet compliance requirements.

Step 4: Ensure that all data encryption keys used by Cloud KMS comply with FIPS 140-2 Level 1 standards.

By using Google default encryption for non-sensitive data and Cloud KMS for sensitive data, you can manage encryption efficiently while maintaining control over key residency and rotation for sensitive data.

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