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Setting up a Shared VPC allows you to create a centrally managed network that spans multiple projects. Here's how you can achieve this while ensuring separation of duties:

Create a Host Project:

Create a project that will act as the host project for your Shared VPC.

Configure Shared VPC:

In the host project, enable the Shared VPC feature.

Create Service Projects:

Create separate service projects for different teams, such as developers and other stakeholders.

Assign Roles:

Security Team: Grant the Compute Network Admin role. This allows the security team to manage network resources, such as firewall rules, subnets, and routes.

Developers: Share the host project’s network with the service projects. Assign roles like Compute Instance Admin to developers in the service projects, enabling them to create and manage VM instances without altering network configurations.

Firewall Management:

The security team will define and manage firewall rules within the host project, ensuring consistent and secure network policies.

Benefits:

Separation of Duties: Security teams handle networking, and developers focus on application deployment and management.

Centralized Control: Network policies are centrally managed, ensuring compliance and security.

Scalability: Easy to add new projects and teams without compromising the overall network security.

References

Google Cloud VPC Documentation

Managing Resources with Shared VPC

Objective: Provide evidence of access reviews for an upcoming audit.

Solution: Use Policy Analyzer to review and report on IAM policies.

Steps:

Step 1: Open the Google Cloud Console.

Step 2: Navigate to the Policy Analyzer tool.

Step 3: Select the project for which you need to review access policies.

Step 4: Use the tool to generate reports on IAM roles and permissions.

Step 5: Export the reports as evidence for the audit.

Policy Analyzer provides detailed insights into IAM policies, helping you to review access configurations and generate necessary reports for compliance and auditing purposes.

The problem requires ensuring that only images that have passed vulnerability scanning and meet corporate policies are allowed to be deployed to GKE, with the process being automated and integrated into the existing CI/CD pipeline.

Binary Authorization: This Google Cloud service is purpose-built to enforce deployment policies on images before they are run on Google Kubernetes Engine (GKE), Cloud Run, and other deployable platforms. It acts as a policy gate that prevents the deployment of non-compliant images.

Extract Reference: "Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE), Cloud Run, and Anthos clusters." and "With Binary Authorization, you can require images to be signed by trusted authorities and enforce validation policies during deployment." (Google Cloud Documentation: "Binary Authorization overview" - https://cloud.google.com/binary-authorization/docs/overview)

Artifact Analysis (part of Container Analysis): Artifact Analysis (which includes Container Analysis) provides vulnerability scanning capabilities for container images stored in Artifact Registry. It generates findings and metadata about vulnerabilities.

Extract Reference: "Container Analysis is a service that scans your images for known vulnerabilities and provides metadata about them." (Google Cloud Documentation: "Overview | Container Analysis" - https://cloud.google.com/container-analysis/docs/overview)

Binary Authorization can be configured to integrate with Artifact Analysis (or other attestors) to check for vulnerability scan results as part of its deployment policy.

Integration and Automation: Binary Authorization policies can require attestations before deployment. An attestation confirms that an image meets specific criteria (e.g., it has passed a vulnerability scan, it was signed by an approved CI/CD process, it adheres to corporate policies). Cloud Build can be configured to generate these attestations after a successful vulnerability scan (using Artifact Analysis). This fully automates the process and integrates directly into the CI/CD pipeline.

Extract Reference: "With Binary Authorization, you create a policy that enforces your requirements. The policy defines rules that govern deployment. For example, a policy can require all images to be signed by a trusted authority before deployment." (Google Cloud Documentation: "Binary Authorization overview" - https://cloud.google.com/binary-authorization/docs/overview)

Let's evaluate the other options:

A. Custom script in Cloud Build... Fail the build: While scanning during the build is good practice (shift-left security), failing the build only prevents the image from being pushed. It doesn't prevent a developer or an automated process from manually deploying an old or non-compliant image that might already exist in Artifact Registry, or from bypassing the build system. The enforcement needs to happen at deployment time.

B. Configure GKE to use only images from a specific... trusted Artifact Registry repository. Manually inspect all images: Manually inspecting images is not automated and does not scale for a "large number of containerized applications." It also doesn't programmatically enforce vulnerability scan results or corporate policies.

D. Enable Artifact Analysis vulnerability scanning and regularly scan images... Remove any images that do not meet... before deployment: This describes scanning and remediation, which are important. However, it's a reactive approach ("remove any images") rather than a proactive enforcement ("only images that... are allowed to be deployed"). There's still a window where a non-compliant image could be deployed before it's removed. Binary Authorization is the enforcement gate.

Therefore, configuring Binary Authorization with a policy that integrates with Artifact Analysis (or requires attestations based on its findings) is the most robust, automated, and Google-recommended solution for enforcing deployment policies based on vulnerability scanning and corporate compliance.

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

Google Cloud Armor provides protection against DDoS attacks and allows you to define security policies to control access to your application. It enables you to block traffic from specific IP addresses or ranges, making it suitable for denying traffic from a list of malicious IP addresses while protecting your application from being directly exposed to the internet.

Steps:

Set Up Cloud Armor: Enable Cloud Armor in your Google Cloud Console.

Create Security Policies: Define security policies that specify the rules for allowing or denying traffic based on IP addresses.

Attach Policies to Backend Services: Apply these security policies to the backend services of your web application.

To ensure that the developers can view audit logs for the development environment and the security team can review all logs, you should grant IAM roles at the appropriate resource levels:

Grant logging.admin Role to the Security Team:

Assign the logging.admin role to the security team at the organization resource level.

This grants the security team full access to all logging data across the organization, including both production and development environments.

Grant logging.viewer Role to the Developer Team:

Assign the logging.viewer role to the developer team at the folder resource level that contains all the development projects.

This restricts the developers' access to only view logs in the development environment, ensuring they do not have access to production logs.

By using these roles and assigning them at the appropriate levels, you ensure that each team has the access they need while adhering to the principle of least privilege.

To remove personally identifiable information (PII) from files older than 12 months and archive the anonymized files for retention purposes, you can use Google Cloud Data Loss Prevention (DLP).

Create a Cloud DLP Inspection Job:

Go to the Cloud DLP section in the Google Cloud Console.

Create an inspection job that scans files in your Cloud Storage bucket for PII.

Configure the job to only target files that are older than 12 months.

Configure De-identification:

In the inspection job settings, configure de-identification actions to remove or obfuscate PII in the files.

Specify the transformation techniques appropriate for your data, such as masking or tokenization.

Archive Anonymized Files:

Set up the job to move the de-identified files to another Cloud Storage bucket designated for archival.

Ensure this bucket has the appropriate retention policies and access controls in place.

Delete Original Files:

After de-identification and archiving, configure the job to delete the original files from the source bucket.

This approach ensures that PII is effectively removed from old files and that the anonymized data is securely archived, maintaining compliance with data retention and privacy policies.

Understanding Organization Policies:

Organization policies are rules that can be set at different levels of the resource hierarchy in GCP to enforce governance and compliance.

These policies can be set at the organization node, folders, and projects, and they are inherited down the hierarchy unless explicitly overridden.

Hierarchy and Policy Inheritance:

The provided resource hierarchy has an organization node (Example.com), folders (Folder 1 and Folder 2), and a project (Project 2) under Folder 2 with a specific VPC (VPC A).

Each node in the hierarchy can have its own policies, and these policies are inherited by child nodes unless overridden.

Analyzing the Policies in the Hierarchy:

Organization Node Policy:

json

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{ "constraint": "constraints/compute.restrictLoadBalancerCreationForTypes", "listPolicy": { "allValues": "DENY" } }

This policy at the organization node denies all load balancer types.

Folder 2 Policy:

json

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{ "constraint": "constraints/compute.restrictLoadBalancerCreationForTypes", "listPolicy": { "deniedValues": ["INTERNAL_TCP_UDP", "INTERNAL_HTTP_HTTPS"] } }

This policy at Folder 2 denies the creation of INTERNAL_TCP_UDP and INTERNAL_HTTP_HTTPS load balancers.

Project 2 Policy:

json

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{ "constraint": "constraints/compute.restrictLoadBalancerCreationForTypes", "listPolicy": { "deniedValues": ["EXTERNAL_TCP_PROXY", "EXTERNAL_SSL_PROXY"] } }

This policy at Project 2 denies the creation of EXTERNAL_TCP_PROXY and EXTERNAL_SSL_PROXY load balancers.

Policy Application to VPC A:

Since policies are inherited, VPC A (which is within Project 2 under Folder 2) will be affected by the policies of both Folder 2 and Project 2.

Combining the denied values from both Folder 2 and Project 2:

From Folder 2: INTERNAL_TCP_UDP, INTERNAL_HTTP_HTTPS

From Project 2: EXTERNAL_TCP_PROXY, EXTERNAL_SSL_PROXY

Conclusion:

VPC A will have the following load balancer types denied: INTERNAL_TCP_UDP, INTERNAL_HTTP_HTTPS, EXTERNAL_TCP_PROXY, EXTERNAL_SSL_PROXY.

To fulfill the requirements of preserving logs for 12 years and ensuring data residency within European boundaries, the best approach is to use Google Cloud's operations suite (formerly Stackdriver) with a custom log bucket configured in the desired region.

Configure Cloud Logging Agent:

Install and configure the Cloud Logging agent on your Compute Engine instances. This agent collects logs from your application and system and sends them to Google Cloud's operations suite.

Create a Custom Log Bucket:

In the Cloud Logging interface, create a custom log bucket in the EUROPE-WEST1 region. This bucket will store your logs and can be configured with a custom retention period.

Set Custom Retention Policy:

Configure the retention policy for the custom log bucket to 12 years. This ensures that all logs are preserved for the required duration.

Ship Logs to the Custom Log Bucket:

Modify the logging configuration to direct logs from the Cloud Logging agent to the custom log bucket. This can be done through the logging configuration settings in the Cloud Console or by updating the agent configuration files.

This solution minimizes overhead by using managed services and ensures cost-effectiveness by leveraging Cloud Logging's built-in capabilities for log storage and retention management.

References

Cloud Logging Documentation

Creating and Managing Logs Buckets

To delegate management of access control permissions to each business unit effectively, organizing projects into folders and assigning permissions to Google groups at the folder level allows for scalable and manageable access control. Using Google Cloud Directory Sync (GCDS) to synchronize users and groups from the on-premises directory service ensures that access controls are maintained and updated automatically as users change roles or leave the company.

Steps:

Organize Projects in Folders: Create a folder structure in the Google Cloud Resource Manager to organize projects by business unit.

Assign Permissions to Google Groups: Use IAM to assign necessary permissions to Google Groups at the folder level, ensuring each business unit can manage access controls for their own projects.

Synchronize Users and Groups: Use GCDS to sync users and group memberships from your on-premises directory service to Google Cloud Identity, ensuring that changes in the on-premises directory are reflected in Google Cloud.