Google Professional-Cloud-Security-Engineer - Google Cloud Certified - Professional Cloud Security Engineer

When dealing with VPC Service Controls (VPC SC), it's important to ensure that only authorized resources can access sensitive data and services. To allow a resource in Project B to access Pub/Sub in Project A without compromising security, you should configure an ingress policy for the service perimeter in Project A.

Identify the Service Account: Determine the service account in Project B that requires access to the Pub/Sub topic in Project A.

Configure Ingress Policy:

Go to the Google Cloud Console.

Navigate to Security > VPC Service Controls.

Select the service perimeter for Project A.

Add an ingress rule specifying the service account from Project B and allowing it access to the necessary Pub/Sub resources.

Define Conditions: Ensure that the ingress policy adheres to the principle of least privilege, granting only the necessary permissions to collect messages from the Pub/Sub topic.

Save and Apply: Save the policy and apply the changes to enforce the new access controls.

This approach maintains the security boundaries set by VPC SC while enabling the required access from Project B to Project A.

VPC Service Controls Documentation

Configuring Ingress Policies

To manage user accounts and ensure they comply with corporate policies, using Google Cloud Directory Sync (GCDS) allows synchronization between your local identity system and Cloud Identity. The Transfer Tool for Unmanaged Users (TTUU) helps identify and manage conflicting accounts by allowing users to transfer their personal accounts to managed accounts.

Steps:

Synchronize Identities: Use GCDS to sync users from your local identity management system to Cloud Identity, ensuring that all corporate user accounts are managed.

Identify Conflicting Accounts: Use TTUU to find users who have personal Google accounts using corporate email addresses.

Manage Conflicting Accounts: Request users to transfer their personal accounts to managed accounts using TTUU, ensuring all accounts are under corporate control.

https://cloud.google.com/load-balancing/docs/tcp

TCP Proxy Load Balancing is implemented on GFEs that are distributed globally. If you choose the Premium Tier of Network Service Tiers, a TCP proxy load balancer is global. In Premium Tier, you can deploy backends in multiple regions, and the load balancer automatically directs user traffic to the closest region that has capacity. If you choose the Standard Tier, a TCP proxy load balancer can only direct traffic among backends in a single region. https://cloud.google.com/load-balancing/docs/load-balancing-overview#tcp-proxy-load-balancing

The minimum length for passwords in Cloud Identity can be set to 8 characters. This aligns with common security best practices for password policies, ensuring a basic level of complexity and security.

Step-by-Step:

Access Admin Console: Log in to the Google Admin console.

Navigate to Security Settings: Go to Security > Password Management.

Set Minimum Length: Set the minimum length for passwords to 8 characters.

Save Changes: Save the settings and ensure that all user accounts adhere to the new policy.

Google Cloud Identity Security Settings

Password Policy Best Practices

To enforce Data Residency at a platform level, Google Cloud provides the Resource Locations Organization Policy (constraints/gcp.resourceLocations).6 This is the authoritative way to prevent resources (like GKE clusters, Persistent Disks, or Cloud Storage buckets) from being created outside of a specific region.

According to Google Cloud Documentation (Organization Policy - Resource Locations):

"The Resource Locations constraint allows you to define the allowed locations (regions or multi-regions) for all supported Google Cloud services.7 By setting this policy at the project or folder level, you ensure that no user—regardless of their IAM permissions—can create resources in any region other than the one specified (e.g., europe-west4)."

Why this is the best solution:

Preventative: It blocks the API call itself if a user tries to deploy elsewhere.

Efficient: It applies to all services within the project without needing custom code (like Option D) or manual reviews (Option B).

Compliance-focused: It provides a clear audit trail and hard guarantee for compliance officers.

For external applications (outside of Google Cloud) to access resources securely, Workload Identity Federation (WIF) is the modern standard.3 It avoids the use of permanent service account keys and relies on external identity providers (OIDC or SAML) to exchange short-lived Google Cloud tokens.

According to Google Cloud Documentation (Workload Identity Federation):

"You can use Workload Identity Federation to grant on-premises or multi-cloud workloads access to Google Cloud resources without using a service account key. This reduces the risk associated with long-lived service account keys and simplifies the implementation of security best practices."

Key points:

It allows external identities (from AWS, Azure, or OIDC/SAML providers) to assume a Google IAM role.

The access is short-lived and scoped to specifically what the application needs (e.g., storage.objects.create).

Why other options are incorrect:

A is incorrect: Long-lived service account keys are a security liability.

B is incorrect: While a proxy works, it adds architectural complexity and overhead compared to direct federation.

C is incorrect: IP-based ACLs are weak because IPs can be spoofed or changed, and Cloud Storage ACLs do not provide robust identity-based security for public-facing apps.

The problem states that the organization is using Model Garden and needs to ensure users can only access approved models. This implies a need for a central, enforceable control mechanism.

Organization Policies and Constraints: Google Cloud Organization Policy Service allows administrators to centrally control resources across an organization. Constraints are specific types of restrictions that can be applied. For AI Platform (which includes Vertex AI and Model Garden), there are specific constraints designed to control model usage.

vertexai.allowedModels Constraint: This specific organization policy constraint is designed precisely to restrict which models can be used within a given organization, folder, or project. It provides a centralized way to define a list of approved models that users are allowed to access.Extract Reference: "The vertexai.allowedModels constraint allows you to specify a list of model URIs that are allowed to be used within the resource hierarchy." and "This constraint helps organizations enforce compliance and control which models are consumed by their users." (Google Cloud documentation, typically found under Organization Policy Service constraints for Vertex AI or AI Platform)

Let's evaluate the other options:

A. Configure IAM permissions on individual Model Garden to restrict access to specific models: IAM (Identity and Access Management) typically grants permissions at a broader resource level (e.g., project, dataset, model resource). While you can control who can manage models, directly restricting access to specific models within Model Garden for consumption via IAM roles on individual models is not the primary mechanism for enforcing a list of approved models across an organization in a preventative way. Organization policies are designed for this kind of broad, preventative control.

B. Regularly audit user activity logs in Vertex AI to identify and revoke access to unapproved models: Auditing logs is a reactive measure. While important for monitoring and detecting violations, it does not prevent users from accessing unapproved models in the first place. The requirement is to ensure they can only access approved models, implying a proactive control.

C. Train custom models within your Vertex AI project and restrict user access to these models: This is about managing access to custom-trained models, not about controlling access to the collection of models in Model Garden, which often includes pre-trained or publicly available models that need to be whitelisted. It doesn't address the requirement of ensuring users only access approved models from the broader Model Garden collection.

Therefore, implementing an organization policy with the vertexai.allowedModels constraint is the most effective and Google-recommended way to centrally ensure that users can only access approved models within an organization using Model Garden.

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.