Professional-Cloud-Security-Engineer Google Cloud Certified - Professional Cloud Security Engineer Questions and Answers

Enable VPC Service Controls:

VPC Service Controls help mitigate the risk of data exfiltration by allowing you to define a security perimeter around GCP resources.

Set up a service perimeter around your BigQuery project to restrict data access to within the defined perimeter.

Create Access Levels:

In the Google Cloud Console, navigate to the Access Context Manager.

Define access levels based on IP address conditions, specifying the authorized source IP addresses that are allowed to access your BigQuery resources.

These access levels are used to enforce policies that restrict who can access your sensitive data based on their IP addresses.

Apply Service Perimeter with Access Levels:

Apply the created access levels to the service perimeter to ensure that only requests originating from the specified IP addresses are able to access BigQuery tables.

This setup ensures that the sensitive PII data is not accessible from unauthorized IP addresses, reducing the risk of data exfiltration.

https://cloud.google.com/identity/solutions/automate-user-provisioning#cloud_identity_automated_provisioning

"Cloud Identity has a catalog of automated provisioning connectors, which act as a bridge between Cloud Identity and third-party cloud apps."

"Date shifting techniques randomly shift a set of dates but preserve the sequence and duration of a period of time. Shifting dates is usually done in context to an individual or an entity. That is, each individual's dates are shifted by an amount of time that is unique to that individual."

Workload Identity is the Google-recommended method for GKE workloads to access Google Cloud services. It eliminates the need for manual management of service account keys (JSON files), which are a major security risk if leaked.1

According to Google Cloud Documentation (Workload Identity Federation for GKE):

"Workload Identity is the recommended way for your workloads running on GKE to access Google Cloud services in a secure and manageable way. It allows a Kubernetes service account in your GKE cluster to act as a Google IAM service account.2 When you use Workload Identity, you don't need to manage service account keys or store them as Kubernetes secrets."

Why other options are incorrect:

A is incorrect: Service account keys are long-lived and difficult to rotate. Using them increases the risk of credential theft.

C & D are incorrect: Attaching a service account to a Node gives every pod running on that node the same permissions. This violates the Principle of Least Privilege because different workloads on the same node should have different permissions.

https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints - constraints/compute.trustedImageProjects

This list constraint defines the set of projects that can be used for image storage and disk instantiation for Compute Engine. If this constraint is active, only images from trusted projects will be allowed as the source for boot disks for new instances.

https://cloud.google.com/vpc/docs/packet-mirroring

Packet Mirroring clones the traffic of specified instances in your Virtual Private Cloud (VPC) network and forwards it for examination. Packet Mirroring captures all traffic and packet data, including payloads and headers.

Generate Supply Chain Levels for Software Artifacts (SLSA) level 3 assurance by using Cloud Build: SLSA is a framework for ensuring the integrity of software artifacts. By using Cloud Build, you can automate the build process and generate SLSA level 3 compliance, which includes verifiable build steps and provenance.

View the build provenance in the Security insights side panel within the Google Cloud console: The build provenance provides a detailed history of how the software was built, including the source code, build process, and any dependencies. This information is accessible through the Security insights side panel in the Google Cloud console, allowing you to verify the integrity and authenticity of your software artifacts.

References

Supply Chain Levels for Software Artifacts (SLSA) documentation

Cloud Build documentation

Security insights in Google Cloud console

Uniform bucket-level access allows you to manage permissions at the bucket level, rather than at the object level. This simplifies permission management and ensures that access to objects is controlled consistently via IAM roles, without allowing uploaders full control over the objects.

Steps:

Enable Uniform Bucket-Level Access: In the Google Cloud Console, enable uniform bucket-level access for the Cloud Storage bucket.

Configure IAM Policies: Assign appropriate IAM roles to users and groups to control access to the bucket.

Audit Logging: Enable Cloud Audit Logs to track access and modifications to the bucket.

This question requires implementing fine-grained data access control across multiple services based on the Principle of Least Privilege.

Project/Service Access (IAM): Granting project-level group permissions with specific Cloud IAM roles (e.g., BigQuery Data Viewer) is the primary way to control who has access to which project's resources.

Data Isolation (Service-Specific): To ensure only relevant data is accessed and to protect sensitive information within the datasets, you must use the most granular control mechanism available for each service:

BigQuery: Authorized Views allow access to specific query results (subsets of data) without granting access to the underlying table.

Cloud Storage: Uniform bucket-level access simplifies and tightens security by forcing all access to be controlled by IAM, preventing accidental object-level exposure.

Cloud SQL: Database Roles are the native, most granular way to control access within the database itself (e.g., read-only access to specific tables).

Extracts (Conceptual Basis):

"The principle of least privilege dictates that users should only have the permissions necessary to perform their jobs. Granular access is enforced using a combination of IAM roles and service-native access controls." (Source 5.1)

"For BigQuery, using authorized views is the standard way to limit data exposure to users who should only see a subset of data." (Source 5.2)

Google Cloud Armor helps to protect applications from DDoS attacks and web application firewall (WAF) threats like XSS and SQLi. To use Google Cloud Armor security policies, certain requirements must be met:

External Load Balancers: Google Cloud Armor is specifically designed to work with external HTTP(S) load balancers, which handle traffic at the edge of the Google Cloud network. This type of load balancer provides a global frontend that can distribute traffic to various backends.

Load Balancing Scheme: The backend service associated with the Google Cloud Armor policy must have an EXTERNAL load balancing scheme. This scheme allows the service to accept traffic from outside the Google Cloud network, which is necessary for applying security policies effectively at the network edge.

These requirements ensure that Google Cloud Armor can inspect and filter incoming traffic before it reaches your web application's backend services, providing an additional layer of security against common web vulnerabilities.

References

Google Cloud Armor Documentation

External HTTP(S) Load Balancing Overview

To enforce access control policies for applications and resources in Google Cloud, the recommended service is Identity-Aware Proxy (IAP).

Identity-Aware Proxy (IAP):

IAP allows you to control access to your applications and resources based on the identity of the user and the context of the request. It integrates with IAM to provide fine-grained access control, ensuring that only authorized users can access specific resources.

IAP helps enforce security policies at the application layer, providing an additional layer of protection beyond traditional network-based security measures.

References

Identity-Aware Proxy documentation

To ensure that data is encrypted while in use by the virtual machines (VMs) and enforce this policy across your organization, you should use Confidential VM instances. Here are the steps:

Enable Confidential VM:

Ensure that Confidential VMs are available in your selected regions and enabled for your project.

Set Organization Policy:

Implement an organization policy to enforce the use of Confidential VM instances for all VMs across your organization.

Use the Google Cloud Console or the gcloud command-line tool to set this policy. Example command:

gcloud resource-manager org-policies set-policy my_policy.yaml

Example my_policy.yaml:

name: organizations/1234567890/policies/compute.requireConfidentialCompute spec: rules: - enforce: true

Verify and Monitor:

Ensure that all newly created VMs across your organization are Confidential VMs.

Regularly monitor compliance through the Google Cloud Console and set up alerts if non-compliant VMs are created.

Benefits:

Data Encryption in Use: Confidential VMs ensure that data is encrypted not just at rest and in transit but also while in use.

Policy Enforcement: Organization policies provide a way to enforce security configurations across all projects under your organization.

References

Confidential Computing Documentation

Creating and Managing Organization Policies

Physical Token for MFA: Implement multi-factor authentication (MFA) using physical tokens (such as security keys) for super admin accounts. This adds an extra layer of security to the highest privilege accounts.

Non-Privileged Identities: Provide super admins with separate non-privileged accounts for daily activities. This practice minimizes the risk associated with using highly privileged accounts for routine tasks.

Account Management: Ensure that super admin accounts are only used for tasks requiring elevated privileges, reducing exposure to potential security threats. These measures enhance the security of super admin accounts, protecting your Google Cloud organization from unauthorized access. References:

Google Cloud - Best Practices for Securing Cloud Identity

Google Cloud - Using Security Keys

The problem requires exposing a sensitive AI model endpoint internally (strictly isolated from the internet) to a defined list of projects within the organization.

Internal Exposure and Isolation: An "internal Application Load Balancer" is suitable for exposing services within your VPC network, ensuring they are not accessible from the internet.

Private Service Connect (PSC): This is the key technology for securely and privately exposing services from one VPC network (the service producer, where the model is) to other VPC networks (the service consumers, the defined list of projects) within the same or different organizations. PSC allows consumers to access services using internal IP addresses, with traffic remaining on Google's private network. You can configure a service attachment that points to the internal load balancer, and then permit specific consumer projects to connect to this service attachment.Extract Reference: "Private Service Connect is a capability of Google Cloud networking that allows consumers to access managed services privately from inside their VPC network. Similarly, it allows managed service producers to host these services in their own separate VPC networks and offer a private connection to their consumers." (Google Cloud Documentation: "Private Service Connect | VPC" - https://cloud.google.com/vpc/docs/private-service-connect)

Extract Reference: "Private Service Connect endpoints are internal IP addresses in a consumer VPC network that can be directly accessed by clients in that network. Endpoints are created by deploying a forwarding rule that references a service attachment or a bundle of Google APIs." (Google Cloud Documentation: "About Private Service Connect | VPC" - https://cloud.google.com/vpc/docs/private-service-connect)

Extract Reference: "Private Service Connect can be used to access managed services that are owned by Google, third-party software as a service (SaaS) companies, or other teams within the consumer's own company. Both published services and Google APIs can be targets of Private Service Connect." (Google Cloud Documentation: "About Private Service Connect | VPC" - https://cloud.google.com/vpc/docs/private-service-connect)

Let's evaluate the other options:

A. Shared VPC and central firewall rules: While Shared VPC centralizes network management, it does not provide a direct managed service exposure mechanism like PSC for a model endpoint to specific projects. It's more about sharing subnets and network resources. Administering all firewall rules centrally would also not meet the need for exposing only this specific model to a defined list of projects in a managed, private service pattern.

B. Activate Private Google Access (PGA): Private Google Access allows VMs without external IP addresses to access Google APIs and services (like Cloud Storage, BigQuery, etc.) privately from within their VPC network. It's for consuming Google services, not for exposing custom services hosted in a Google Cloud project to other projects.

D. External Application Load Balancer + Cloud Armor: An "external Application Load Balancer" exposes the service to the internet. While Cloud Armor can restrict access based on IP addresses, it still involves internet exposure, which contradicts the "strictly isolated from the internet" requirement. Restricting to "Google Cloud IP addresses" doesn't guarantee access only to a defined list of projects and still exposes the service externally.

Therefore, creating an internal Application Load Balancer and exposing it via Private Service Connect is the most suitable and secure solution for this scenario.

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.

In the Google Cloud resource hierarchy, Organization Policy is a powerful tool for governance, but its effectiveness depends on strict control over who can modify it. If a policy set at the folder level was "overwritten," it means a principal with the Organization Policy Administrator role (roles/orgpolicy.policyAdmin) at the project level (or folder level) changed the inheritance or defined a more permissive policy.1

According to Google Cloud Documentation (Organization Policy Service - IAM Roles):

"To manage organization policies, a principal must have the Organization Policy Administrator role. This role should be granted only at the Organization level to a limited set of trusted security or compliance administrators to prevent project owners from overriding security guardrails."

Why Option A is the best fix:

By removing the role from everyone except the central security team at the Organization level, you ensure that no one else has the technical permission to "Manage Policy" or click "Override" at any child node (folder or project).

B is insufficient because if a user has the role at the organization level, they can still apply overrides at the folder or project level.

C and D (Security Postures) are detective controls. While the secure_ai_extended template helps monitor drift, it does not prevent the occurrence. The question asks for a solution that "prevents a repeat occurrence," which requires a preventative IAM change.

To access a user's Google Drive on their behalf without relying on the user's credentials and following Google-recommended practices, you should use a service account with domain-wide delegation.

Create a Service Account:

Go to the Cloud Console, navigate to IAM & Admin > Service Accounts.

Click "Create Service Account" and provide necessary details.

Grant Domain-Wide Delegation:

Edit the service account to enable "G Suite Domain-wide Delegation".

Download the JSON key file.

Configure API Access in G Suite:

Go to the Google Admin Console.

Navigate to Security > API Controls > Domain-wide Delegation.

Add a new API client and use the client ID from the service account.

Authorize the necessary API scopes (e.g., https://www.googleapis.com/auth/drive).

Implement in Application:

Use the Google API Client Library for the desired language.

Load the service account credentials and perform user impersonation to access Google Drive.

Domain-wide Delegation of Authority

Using OAuth 2.0 for Server to Server Applications

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.

Setting up separate service perimeters for dev, staging, and prod environments allows for more granular control and monitoring. Automating the addition of new projects to the respective perimeters ensures that all projects are consistently secured without manual intervention.

Steps:

Set Up Service Perimeters: Use Access Context Manager to define and configure three separate service perimeters for dev, staging, and prod.

Deploy Monitoring Function: Create a Cloud Function that monitors the "implementation" folder for new projects using Stackdriver (Cloud Monitoring) and Cloud Pub/Sub.

Automate Perimeter Updates: Configure the Cloud Function to execute Terraform scripts that automatically add new projects to the appropriate service perimeter.

The problem is the detection of secrets (sensitive data patterns) within the environment variables of deployed resources (Cloud Functions) in a timely, automated manner.

Sensitive Data Protection (SDP), formerly Cloud DLP, is the purpose-built Google Cloud service for scanning and classifying sensitive data patterns. It can be configured to scan code, configuration, or environment variables and integrate its findings directly with Security Command Center (SCC).

Extracts:

"Sensitive Data Protection provides highly configurable, automated detection of sensitive data, including API keys, passwords, and other credentials, using both pre-built and custom infoTypes." (Source 8.1)

"SDP can be integrated with Cloud Functions and other resource configurations to scan environment variables or configuration files for secrets. Violations can be automatically routed to Security Command Center as findings." (Source 8.2)

Option D (DAST) scans the application code or running application logic, but the requirement specifies the secrets are in the environment variables, which are part of the configuration/deployment metadata, making SDP the correct detection tool.

Provision a Cloud NAT Instance:

Cloud NAT (Network Address Translation) allows instances without external IP addresses to access the internet securely.

In the Google Cloud Console, navigate to the VPC Network section and select Cloud NAT.

Create a new Cloud NAT configuration, specifying the VPC and region where your Compute Engine VMs are deployed.

Configure Cloud NAT:

Ensure that the Cloud NAT instance is configured to provide outbound internet connectivity for the VMs in your specified subnet.

This setup allows the VMs to access the internet for package updates and external installations without requiring public IP addresses.

Enable Private Google Access:

Private Google Access allows VMs in a subnet to reach Google APIs and services using internal IP addresses.

In the Google Cloud Console, navigate to the VPC Network section and select Subnets.

Edit the subnet used by your Compute Engine VMs and enable Private Google Access.

Update DevOps Scripts:

Ensure that your DevOps scripts are updated to work with the new network configuration.

Test the build process to confirm that the VMs can access necessary resources and complete the build pipeline successfully.

The most secure and compliant way to manage a policy exception in Google Cloud is through the resource hierarchy using Organization Policies.

Restrictive Baseline: The policy should be applied at the Organization level to enforce the baseline (no external IPs) across the entire company, ensuring minimum policy drift.

Exception and Least Privilege: The regulated unit is placed in its own Folder (isolation). The restrictive policy is then overridden or enforced with an exclusion at this Folder level to grant the exception only where needed. This ensures the exception is applied to the smallest scope necessary, adhering to least privilege.

Extracts:

"Organization Policy is inherited down the resource hierarchy... You can override inherited policies by enforcing a different policy at a lower level." (Source 2.1)

"To implement an exception, the most secure approach is to set the restrictive policy at the highest possible level (e.g., Organization) and override or enforce an exclusion at the lowest possible level (e.g., Project or Folder) where the exception is required." (Source 2.2)

By applying the restriction broadly and granting the exception narrowly at the folder level, you maintain central control and minimize the blast radius of the exception.

https://cloud.google.com/vpc/docs/firewalls#service-accounts-vs-tags

https://cloud.google.com/vpc/docs/firewalls#service-accounts-vs-tags

A service account represents an identity associated with an instance. Only one service account can be associated with an instance. You control access to the service account by controlling the grant of the Service Account User role for other IAM principals. For an IAM principal to start an instance by using a service account, that principal must have the Service Account User role to at least use that service account and appropriate permissions to create instances (for example, having the Compute Engine Instance Admin role to the project).

For scenarios involving multi-party collaboration or highly sensitive data where even the "Workload Operator" should not see the data, Confidential Space is the specific Google Cloud solution. It builds on Confidential Computing but adds a software framework for "Trustless" data processing.

According to Google Cloud Documentation (Confidential Space Overview):

"Confidential Space provides a secure environment for multiple parties to share sensitive data without any party (including the cloud provider or the project administrator) being able to see the data or the code being executed. It uses attestations to verify the integrity of the workload and Workload Identity Federation to release encryption keys or access only to verified, authorized workloads."

Key components of the solution:

Separation of Duties: Confidential Space explicitly defines roles for the Data Collaborator (owns data), Workload Author (writes code), and Workload Operator (runs infrastructure). The operator can manage the VM but cannot access the memory or the data.

Attestation: The system uses hardware-rooted attestation to ensure that the code running in the environment is exactly what was approved.

Output Protection: The processed results can be written to a bucket where only the authorized "Data Scientist" (Data Collaborator role) can retrieve them.

Why other options are incorrect:

A, C, and D are incorrect: While they use Confidential VMs (which encrypt data in RAM), they do not inherently provide the attestation-based trust framework or the rigorous separation of duties required to prevent a VM administrator (operator) from potentially accessing the data via standard VM management tools or during the ingestion phase. Confidential Space is the only one that mandates a signed "attestation" before granting access to data.

The problem requires restricting the types of Google Cloud services that can be deployed within specific folders to enforce compliance, without affecting other parts of the resource hierarchy, using the most efficient and simple method.

Organization Policies: Organization policies allow you to define centralized, programmatic controls over your Google Cloud resources. They apply hierarchically, meaning a policy set on a folder applies to all projects and resources within that folder and its descendants.

Restrict Resource Service Usage Constraint: This specific organization policy constraint is designed precisely for controlling which Google Cloud services can be used (and thus deployed/created resources for) within a given part of the resource hierarchy. It supports both allowlists and denylists of service API identifiers.

Extract Reference: "The Restrict Resource Service Usage constraint controls the runtime access to all in-scope resources." and "This constraint can be used in two mutually exclusive ways: Denylist - resources of any service that isn't denied are allowed. Allowlist - resources of any service that isn't allowed are denied." (Google Cloud Documentation: "Restricting resource usage | Resource Manager Documentation" - https://cloud.google.com/resource-manager/docs/organization-policy/restricting-resources)

Folder-Level Application: Applying this organization policy at the folder level directly meets the requirement of applying restrictions "only to the designated folders without affecting other parts of the resource hierarchy." This is more efficient and simpler than applying a global policy with numerous exceptions.

Let's evaluate the other options:

B. Implement IAM conditions on service account creation within each folder: IAM conditions control permissions for who can do what. While they can be used for very fine-grained access control, they are not designed to restrict the types of services that can be deployed directly. Controlling service account creation doesn't prevent a user with appropriate permissions from deploying other resources.

C. Create a global organization policy... and apply exceptions: While technically possible, this is less efficient and simple if the goal is to only restrict specific folders. Managing exceptions for the entire rest of the organization would be more complex than simply applying the policy directly where it's needed.

D. Configure VPC Service Controls perimeters around each folder: VPC Service Controls primarily prevent data exfiltration and restrict API access at a network perimeter level. They are not designed to restrict which types of Google Cloud services can be deployed within a project or folder; rather, they control how allowed services interact with each other and with external endpoints.

Therefore, creating an organization policy with the "Restrict Resource Service Usage" constraint at the folder level is the most efficient, simple, and direct method to achieve the stated goal.

https://cloud.google.com/storage/docs/public-access-prevention

Public access prevention protects Cloud Storage buckets and objects from being accidentally exposed to the public. If your bucket is contained within an organization, you can enforce public access prevention by using the organization policy constraint storage.publicAccessPrevention at the project, folder, or organization level.

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

The problem describes an application with two Cloud Run services (Service A - frontend, Service B - backend) and requires setting up IAM with the principle of least privilege. Service A calls Service B.

Principle of Least Privilege: This principle dictates that each entity (in this case, a Cloud Run service) should only have the minimum permissions necessary to perform its function.

Separate Service Accounts for Separate Services: To adhere to the principle of least privilege, it's best practice to assign a unique service account to each distinct service or component. This ensures that a compromise of one service account does not grant excessive permissions across other services. Service A needs permissions to run itself and to invoke Service B. Service B only needs permissions to run itself.Extract Reference: "Assign a service account to a Cloud Run service. The service account acts as the identity for your service and determines what permissions your revisions have when executing requests. It is a best practice to grant each service account only the permissions that are required to run the specific service (principle of least privilege)." (Google Cloud documentation: https://cloud.google.com/run/docs/configuring/service-accounts)

Authentication for Cloud Run Services: When one Cloud Run service (caller) needs to invoke another Cloud Run service (callee), the caller must be authorized to do so. This is typically achieved by assigning the roles/run.invoker role on the callee service to the caller's service account.Extract Reference: "To allow a service to invoke another service, grant the roles/run.invoker role on the called service to the caller's service account." (Google Cloud documentation: https://cloud.google.com/run/docs/securing/service-to-service)

Let's evaluate the options:

A. Create a new service account with the permissions to run service A and service B. Require authentication for service B. Permit only the new service account to call the backend. This violates the principle of least privilege by giving a single service account permissions for both services. If that service account were compromised, both services would be affected.

B. Create two separate service accounts. Grant one service account the permissions to execute service A, and grant the other service account the permissions to execute service B. Require authentication for service B. Permit only the service account for service A to call the back-end. This aligns perfectly with least privilege. Service A gets its own identity, Service B gets its own identity. Service A's service account is then granted run.invoker permissions on Service B, allowing it to call the backend while Service B requires authentication. This is the recommended approach.

C. Use the Compute Engine default service account to run service A and service B. Require authentication for service B. Permit only the default service account to call the backend. The Compute Engine default service account often has broad permissions (e.g., editor role in its project). Using it violates the principle of least privilege and is generally discouraged for production applications due to the potential for excessive permissions.

D. Create three separate service accounts. Grant one service account the permissions to execute service A. Grant the second service account the permissions to run service B. Grant the third service account the permissions to communicate between both services A and B. Require authentication for service B. Call the back-end by authenticating with a service account key for the third service account. This introduces unnecessary complexity with a third service account just for communication. More critically, using a service account key for authentication is generally discouraged in Cloud Run environments where ADC (Application Default Credentials) can be used, as managing keys securely becomes an operational overhead and security risk. Cloud Run services automatically use their attached service accounts for authentication when making calls to other Google Cloud services, including other Cloud Run services.

Therefore, option B is the best solution, adhering to the principle of least privilege and Google Cloud best practices for Cloud Run service-to-service authentication.

Activate Security Command Center (SCC) Premium: Security Command Center (SCC) Premium provides advanced security analytics and best practice recommendations for your Google Cloud environment. It includes functionalities such as asset discovery, vulnerability scanning, and security findings.

Create a Custom Rule to Mute Irrelevant Security Findings:

Navigate to the Security Command Center (SCC) in the Google Cloud Console.

Go to the "Settings" tab and find the "Mute findings" section.

Create a new mute rule by specifying the conditions that match the irrelevant controls you want to disregard. These conditions can be based on attributes such as resource type, finding type, and other metadata.

Apply this mute rule, which will ensure that the specified findings are not evaluated in your security posture assessments.

Ensure Continuous Compliance Monitoring:

The mute rules will automatically filter out the irrelevant findings, ensuring that only relevant controls from the CIS Google Cloud Computing Foundations Benchmark v1.3.0 are evaluated.

Regularly review and update the mute rules to adapt to any changes in your compliance requirements or security posture.

Managing IAM permissions at the KeyRing level is more efficient and scalable compared to managing them at the individual Key level. By creating a single KeyRing and placing all encryption keys within it, you can apply uniform IAM permissions to the entire KeyRing, simplifying the management of permissions.

Steps:

Create a KeyRing: Set up a single KeyRing in Cloud KMS for all the encryption keys required for the persistent disks.

Create Encryption Keys: Generate the necessary encryption keys within this KeyRing.

Set IAM Permissions: Assign IAM roles and permissions to the KeyRing to manage access control at this level, ensuring that all keys within the KeyRing inherit these permissions.

The requirement to ensure the master encryption key material never leaves the on-premises hardware (HSM) and retaining the unilateral ability to revoke access are the defining features of Cloud External Key Manager (Cloud EKM).

Key Residency: Cloud EKM allows you to use encryption keys stored and managed in a supported external key management system, such as an on-premises HSM, for encrypting data in Google Cloud services like BigQuery and Cloud Storage. This ensures the key material remains in your accredited hardware.

Unilateral Control: Since Google Cloud must request the key from the external system for every encryption/decryption operation, revoking access (by disabling the key or revoking Google's access) in the external system immediately renders the data in Google Cloud inaccessible, granting the customer unilateral control.

Extracts:

"Cloud External Key Manager (Cloud EKM) is a cloud service that lets you encrypt data in Google Cloud with keys you manage outside of Google Cloud." (Source 6.1)

"The key material is stored on an external system, such as a Cloud EKM partner or an on-premises HSM, and never leaves that system." (Source 6.1)

"The customer can unilaterally revoke access to the key at the EKM system, making the encrypted data in Google Cloud inaccessible, which is a key requirement for highly regulated industries." (Source 6.2)

The problem requires restricting admin access to Google Cloud APIs based on geographic location (Canada and Germany) and environment (development and production projects).

VPC Service Controls (VPC SC): VPC Service Controls is designed to create security perimeters around Google Cloud resources and services. Its primary purpose is to prevent data exfiltration and control access to Google Cloud APIs based on the context of the request, which includes the source IP address.

Extract Reference: "VPC Service Controls provides an extra layer of security defense for Google Cloud services that is independent of Identity and Access Management (IAM). While IAM enables granular identity-based access control, VPC Service Controls enables broader context-based perimeter security, including controlling data egress across the perimeter." (Google Cloud Documentation: "Overview of VPC Service Controls" - https://cloud.google.com/vpc-service-controls/docs/overview)

Service Perimeters for Environments: Creating dedicated perimeters for development and production projects allows for logical separation of environments, which aligns with the "dedicated projects for development and production" structure.

Ingress Policies with Geographic Restrictions: VPC Service Controls uses "ingress rules" to define who and from where requests can enter a service perimeter. These ingress rules can be configured to allow access based on various attributes, including the source IP address of the request. By allowing access from specific IP ranges corresponding to Canada and Germany, you effectively restrict administrative access to APIs from those countries. You can define "access levels" (which can include IP subnets or geographical origins) and attach them to ingress policies.

Extract Reference: "To allow ingress to resources, VPC Service Controls evaluates sources and identityType attributes as an AND condition... You must specify an accessLevel or a resource (Google Cloud project or VPC network), or set accessLevel attribute to *." (Google Cloud Documentation: "Ingress and egress rules | VPC Service Controls" - https://cloud.google.com/vpc-service-controls/docs/ingress-egress-rules)

Extract Reference (for Context-Aware Access which underpins access levels): "You can create different types of Context-Aware Access policies for accessing apps: IP, device, geographic origin, and custom access-level attributes." (Google Workspace Admin Help: "Protect your business with Context-Aware Access" - https://support.google.com/a/answer/9275380) - While this references Workspace apps, the underlying mechanism of Access Context Manager (used by VPC SC) supports geographic restrictions.

Let's evaluate the other options:

A. Create dedicated firewall policies... restrict access based on geolocations: VPC firewall rules operate at the network level (Layers 3/4) within a VPC. They control traffic between VM instances or to/from the internet for network services. They do not directly control admin access to Google Cloud APIs (e.g., via the console or gcloud CLI calls) originating from outside the VPC.

B. Activate the organization policy on the folders to restrict resource location: The Resource Location Restriction organization policy constraint restricts where new resources can be created or stored (e.g., data residency requirements). It does not restrict where administrators can connect from to manage these resources or access APIs.

D. Create dedicated IAM Groups... Grant access: IAM (Identity and Access Management) controls who can access what resources and what actions they can perform. It does not natively provide control over where the access originates from (e.g., country-specific IP addresses).

Therefore, VPC Service Controls with properly configured ingress policies based on source IP/Access Levels is the recommended and most effective method for restricting admin access to Google Cloud APIs by geographic location and environment.

To ensure that Vertex AI Workbench Instances (formerly AI Platform Notebooks) are automatically updated and that users cannot modify operating system settings, it's crucial to implement organizational policies that enforce these requirements.

disableRootAccess Organization Policy:This policy prevents users from obtaining root access on virtual machines. By enforcing this policy, you ensure that users cannot make unauthorized changes to the operating system settings, maintaining the integrity and security of the instances.

requireAutoUpgradeSchedule Organization Policy:This policy mandates that virtual machines have an auto-upgrade schedule for their operating systems. By enforcing this policy, you ensure that instances are automatically kept up-to-date with the latest security patches and updates, reducing the risk of vulnerabilities.

Given the options:

Option A: Enabling VM Manager helps in managing updates and configurations but does not inherently prevent users from altering OS settings.

Option B: Enforcing the disableRootAccess and requireAutoUpgradeSchedule organization policies directly addresses both requirements: preventing unauthorized OS modifications and ensuring automatic updates.

Option C: Assigning specific roles controls user permissions but does not enforce OS-level restrictions or automatic updates.

Option D: Implementing firewall rules to prevent SSH access adds a layer of security but does not ensure automatic updates or prevent OS modifications through other means.

Therefore, Option B is the most effective approach, as it directly enforces the necessary policies to meet both requirements.

Storing secrets securely is crucial for maintaining the integrity and confidentiality of your applications. Here is how you can achieve this using Google Cloud Platform:

Encrypt the Secrets: Use Customer-Managed Encryption Keys (CMEK) to encrypt your secrets. CMEK allows you to have greater control over the encryption keys used to protect your data. This ensures that even if the storage medium is compromised, the secrets remain protected by strong encryption.

Store in Cloud Storage: Store the encrypted secrets in Google Cloud Storage. Cloud Storage is a secure and scalable object storage service. By using encrypted storage, you can ensure that the secrets are securely stored and can only be accessed by authorized entities.

This method provides a secure and managed way to store secrets, ensuring that they are not exposed in plain text within your source code management system.

References

Customer-Managed Encryption Keys (CMEK)

Google Cloud Storage Security

"In order to be able to keep using the existing identity management system, identities need to be synchronized between AD and GCP IAM. To do so google provides a tool called Cloud Directory Sync. This tool will read all identities in AD and replicate those within GCP. Once the identities have been replicated then it's possible to apply IAM permissions on the groups. After that you will configure SAML so google can act as a service provider and either you ADFS or other third party tools like Ping or Okta will act as the identity provider. This way you effectively delegate the authentication from Google to something that is under your control."

To securely access Google Cloud APIs from CI/CD pipelines running on Google Kubernetes Engine (GKE), follow these steps:

Create Service Accounts:

Create individual service accounts for each CI/CD pipeline. This ensures isolation and minimal permissions per pipeline.

Use a naming convention that includes an identifier for each pipeline, such as pipeline-a-sa, pipeline-b-sa, etc.

Configure Kubernetes Service Accounts:

Create Kubernetes service accounts for each CI/CD pipeline pod.

Map Kubernetes Service Accounts to Google Service Accounts:

Use Workload Identity to associate Kubernetes service accounts with the corresponding Google service accounts. This allows the pods to authenticate to Google Cloud APIs securely.

Example command to bind the Kubernetes service account to the Google service account:

gcloud iam service-accounts add-iam-policy-binding \ --role roles/iam.workloadIdentityUser \ --member "serviceAccount:.svc.id.goog[/]" \ @.iam.gserviceaccount.com

Deploy CI/CD Pipelines:

Ensure each pipeline runs in dedicated pods that use the specific Kubernetes service accounts configured earlier.

This setup ensures that each pipeline has the necessary permissions to interact with Google Cloud APIs securely, adhering to the principle of least privilege.

References

Using Workload Identity

Managing Service Accounts

Challenge:

Ensuring secure access to Google Cloud resources from GitHub Actions CI/CD pipelines without directly managing service account keys.

Workload Identity Federation:

Allows for the delegation of access to Google Cloud resources based on federated identities, such as those from GitHub.

Benefits:

This approach eliminates the need to manage service account keys, reducing the risk of key leakage.

It leverages GitHub's identity provider capabilities to authenticate and authorize access.

Steps to Configure Workload Identity Federation:

Step 1: Create a workload identity pool in Google Cloud.

Step 2: Add GitHub as an identity provider within the pool.

Step 3: Configure the necessary permissions and bindings for the identity pool to allow GitHub Actions to access Google Cloud resources.

Step 4: Update the GitHub Actions workflow to use the identity federation for authentication.

Objective: Migrate ongoing data backup and disaster recovery solutions to GCP.

Solution: Use Cloud Storage with scheduled tasks and gsutil.

Steps:

Step 1: Set up a Cloud Interconnect to ensure stable networking connectivity between the on-premises environment and GCP.

Step 2: Create a Cloud Storage bucket to store backups.

Step 3: Use gsutil, a command-line tool for Cloud Storage, to create scripts for data transfer.

Step 4: Schedule these scripts using cron jobs or another scheduling tool to automate the backup process.

Using Cloud Storage with scheduled tasks and gsutil ensures efficient and reliable backup and disaster recovery while leveraging stable connectivity provided by Cloud Interconnect.

Implied IPv4 allow egress rule. An egress rule whose action is allow, destination is 0.0.0.0/0, and priority is the lowest possible (65535) lets any instance send traffic to any destination

Implied IPv4 deny ingress rule. An ingress rule whose action is deny, source is 0.0.0.0/0, and priority is the lowest possible (65535) protects all instances by blocking incoming connections to them.

https://cloud.google.com/vpc/docs/firewalls?hl=en#default_firewall_rules

To share a service (like an AI model) across project boundaries privately and securely—without complex VPC peering or exposing it to the internet—Private Service Connect (PSC) is the recommended solution.14

According to Google Cloud Documentation (Private Service Connect for Vertex AI/Custom Services):

"Private Service Connect allows a service producer to expose a service (via an Internal Load Balancer) to service consumers in other VPC networks or projects.15 The producer creates a Service Attachment that points to the load balancer.16 The consumer creates a PSC Endpoint in their own VPC. Traffic stays entirely within the Google backbone, and the producer can maintain an 'Allowlist' of Project IDs that are permitted to connect."

Why this fits the requirements:

Isolation: The model remains in its own project and is not reachable via the internet (Option C is out).

Granular Control: PSC allows you to explicitly list which Project IDs can access the service attachment (Option A).17

Scalability: It avoids the "transitive peering" and "IP overlap" limitations often found in Shared VPC or Peering architectures (Option B).

To meet the requirements of rotating the master key every 45 days, achieving FIPS 140-2 Level 3 validation, and ensuring the master key is stored redundantly in multiple US regions, you should use Customer-managed encryption keys with Cloud HSM. Here’s how:

Set Up Cloud HSM:

Deploy Cloud HSM in your Google Cloud environment. Cloud HSM provides a hardware-based key management solution that meets FIPS 140-2 Level 3 compliance.

Create and Manage Keys:

Create your encryption keys in Cloud HSM. These keys can be managed and rotated per your policy requirements.

Key Rotation:

Set up a key rotation schedule to rotate the master key every 45 days. Cloud HSM allows you to automate this process.

Geographic Redundancy:

Ensure that your Cloud HSM configuration spans multiple regions within the US to achieve redundancy. This will ensure that your keys are available even if a particular region experiences an outage.

Compliance:

Cloud HSM’s FIPS 140-2 Level 3 validation ensures that your encryption keys are managed in a secure and compliant manner.

Benefits:

Security and Compliance: Meets stringent compliance requirements.

Automated Management: Simplifies key management and rotation.

Redundancy: Ensures high availability of keys across multiple regions.

References

Cloud HSM Documentation

Key Management with Cloud KMS and Cloud HSM

To investigate and remediate the issue of public access to Cloud Storage buckets, you can follow these steps:

Change Bucket Permissions:

Navigate to the Cloud Storage section in the Google Cloud Console.

For each affected bucket, remove any public access permissions (e.g., removing allUsers or allAuthenticatedUsers from the IAM policy).

Ensure that only authorized users have the necessary permissions to access the buckets.

Query Data Access Audit Logs:

Go to the Logging section in the Google Cloud Console.

Query the audit logs for the affected buckets to identify any unauthorized access. You can use filters to search for access by unauthorized users.

Correct the Misconfiguration:

After correcting the permissions, mute the relevant findings in the Security Command Center to indicate that the issue has been resolved.

This helps in maintaining a clear view of ongoing security issues and ensures the findings are not flagged again unless there's a new occurrence.

By following these steps, you ensure that the buckets are no longer publicly accessible, investigate any potential unauthorized access, and update the Security Command Center status to reflect the resolution of the issue.

Cloud Storage IAM Permissions

Viewing Audit Logs

Security Command Center Documentation

To secure a container supply chain, you need two things: Visibility (Scanning) and Enforcement (Policy). Google Cloud provides Artifact Analysis (integrated with Artifact Registry) and Binary Authorization to solve this.

According to Google Cloud Documentation (Software Supply Chain Security):

"To secure your supply chain, use Artifact Registry with automatic vulnerability scanning to identify risks in your images. Then, use Binary Authorization to define a policy that requires images to be signed by trusted authorities (attestors) before they can be deployed to GKE or Cloud Run. This ensures that only images that have passed your security checks (like vulnerability scans) are allowed to run."

How it works:

Scanning: Every time an image is pushed to Artifact Registry, it is automatically scanned for CVEs.

Attestation: A successful scan (e.g., no 'Critical' vulnerabilities) triggers a CI/CD step to "Sign" the image (create an attestation).

Enforcement: The GKE admission controller (Binary Authorization) checks for this signature. If it's missing or invalid, the deployment is blocked.

Why other options are incorrect:

A is incorrect: Container Threat Detection is for runtime (after it's already running). Supply chain security is about pre-deployment prevention.

B is incorrect: While Grafeas/Kritis are the open-source foundations, Option D represents the managed Google Cloud services which "minimize management overhead."

C is incorrect: Firewalls inspect network traffic, not the integrity or vulnerability status of the container image itself.

To ensure that only trusted container images are deployed on Cloud Run, you can implement Binary Authorization, which is a deploy-time security control that ensures only trusted images are used.

Set Up Binary Authorization:

Navigate to the Google Cloud Console.

Go to Security > Binary Authorization.

Configure the policy to include attestors that verify your trusted images.

Enable Binary Authorization on Cloud Run:

Go to the Cloud Run service.

Enable Binary Authorization on your existing Cloud Run services by selecting the appropriate Binary Authorization policy.

Set Organization Policy:

Go to the Organization Policies page in the Google Cloud Console.

Add a constraint for constraints/run.allowedBinaryAuthorizationPolicies.

Specify the list of allowed Binary Authorization policy names to enforce across your organization.

These steps ensure that any container image deployed on Cloud Run is validated against the specified Binary Authorization policies, preventing untrusted images from being deployed.

Binary Authorization Documentation

Enabling Binary Authorization on Cloud Run

Use Cloud External Key Management (EKM) that integrates with an external Hardware Security Module (HSM) system from supported vendors: Cloud EKM allows you to use encryption keys that are managed externally to Google Cloud. This means you can generate and store your keys in an on-premises HSM or another supported external HSM service, and integrate these keys with various Google Cloud services.

Integration with Google Services: Cloud EKM integrates seamlessly with many Google Cloud services, including BigQuery, Cloud Storage, Compute Engine, and more. This provides you with full control over your encryption keys while still taking advantage of Google Cloud's powerful services.

References

Cloud External Key Management (EKM) documentation

External Key Management overview

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 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

When you use a customer-managed encryption key (CMEK) to secure a Cloud Storage bucket, the key and the bucket must be located in the same region. In this case, the key is in europe-west3 and the bucket is in europe-west1, which is why you’re unable to access the key.

For managing and rotating encryption keys in a Compute Engine-based cluster using Managed Instance Groups (MIGs), Customer-Managed Encryption Keys (CMEK) with Cloud KMS is the appropriate solution.

Set Up Cloud KMS:

Go to the Cloud Console and navigate to Security > Cryptographic Keys.

Create a keyring and a key.

Create and Use CMEK:

While creating or updating a Compute Engine instance, specify the CMEK key.

Example command:

gcloud compute instances create example-instance \ --image-family=debian-9 \ --image-project=debian-cloud \ --boot-disk-kms-key=projects/[PROJECT_ID]/locations/global/keyRings/[KEY_RING]/cryptoKeys/[KEY]

Rotate Keys:

Rotate keys periodically using Cloud KMS by creating new key versions and updating the instances to use the new key versions.

Customer-Managed Encryption Keys (CMEK)

Using Customer-Managed Encryption Keys

The problem is that the on-premises developers cannot resolve the Artifact Registry hostname, and they have no route to the internet. This is a classic DNS resolution problem in a hybrid network using private API access.

Artifact Registry is a Google-managed service, and its hostname (e.g., us-west1-docker.pkg.dev) resolves to a Google API domain. To access Google services privately from an on-premises network without an internet route, the traffic must be directed to Private Google Access IP ranges.

Issue: The on-premises DNS cannot resolve the Google service domain to the required private IP range.

Solution: The on-premises DNS needs a record (or a forwarding rule) to resolve the Google service domain to the dedicated IP ranges used for Private Google Access, specifically restricted.googleapis.com or private.googleapis.com (which provide the IP addresses for private access).

Extracts (Conceptual Basis):

"To direct traffic privately, you must ensure that your on-premises network's DNS is configured to resolve Google API and service domain names to the IP address range for Private Google Access." (Source 1.1)

"The IP addresses for private.googleapis.com are used for Private Google Access. To enable on-premises hosts to access Google APIs and services using this method, you must configure on-premises DNS to resolve requests for Google API domain names to the IP address range for private.googleapis.com." (Source 1.2)

Option B is incorrect because Private Google Access (PGA) is enabled on the VPC subnet, allowing VMs within the VPC to access Google APIs. However, the problem is with the on-premises developers; the on-premises DNS must be configured to resolve the hostname correctly.

To comply with FIPS 140-2 for the messaging app, you need to ensure that both data at rest and data in transit are encrypted according to the standard. Using customer-managed encryption keys (CMEK) ensures that you have control over the encryption keys, and BoringSSL is a library that meets FIPS 140-2 standards for encrypting data in transit.

Steps:

Encrypt Local SSDs: Modify the instance template for the Managed Instance Group (MIG) to use customer-managed encryption keys (CMEK) for encrypting Local SSDs.

Enable BoringSSL: Update the application to use the BoringSSL library for all instance-to-instance communication to ensure that all data in transit is encrypted according to FIPS 140-2 standards.

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.

Objective: Reduce the risk of Google Cloud user accounts being compromised.

Solution: Implement strong password policies and post-SSO 2-Step Verification using security keys.

Steps:

Step 1: In Active Directory, configure a domain password policy with strong settings (e.g., complexity, length, expiration).

Step 2: In the Google Admin console, navigate to the Security settings.

Step 3: Enable 2-Step Verification and configure it to use security keys for post-SSO verification.

Step 4: Ensure all users enroll in the 2-Step Verification with security keys.

Using strong password policies in Active Directory along with security keys for 2-Step Verification post-SSO provides enhanced security against account compromises.

Pseudonymization is a de-identification technique that replaces sensitive data values with cryptographically generated tokens. Pseudonymization is widely used in industries like finance and healthcare to help reduce the risk of data in use, narrow compliance scope, and minimize the exposure of sensitive data to systems while preserving data utility and accuracy.

https://cloud.google.com/dlp/docs/pseudonymization

The requirements necessitate a private, cross-project service-to-service connection with explicit authorization—a capability perfectly addressed by Private Service Connect (PSC).

Internal Load Balancer: Ensures the service is isolated from the internet (Layer 7 Load Balancer for HTTP/S ML endpoint).

Private Service Connect (PSC): Allows a service (the model endpoint, exposed via the internal load balancer) in one VPC/project (producer) to be securely consumed by other VPCs/projects (consumers) using an internal IP address.

Defined List of Projects: PSC enables Explicit authorization, allowing the producer to define the allowed list of consumers that can establish a connection, directly meeting the granular restriction requirement.

Extracts:

"Private Service Connect provides... Explicit authorization. Private Service Connect provides an authorization model that gives consumers and producers granular control." (Source 2.4)

"Private Service Connect backends let Google Cloud load balancers send traffic through Private Service Connect to reach published services... Placing a load balancer in front of a managed service provides the consumer with more visibility and control..." (Source 2.4)

"Publish services by using Private Service Connect... Select the internal load balancer that hosts the service that you want to publish." (Source 2.3)

To organize GCP projects based on different business units and manage IAM permissions, you should create an organization node and assign folders for each business unit. This approach allows you to logically separate projects under folders and apply IAM policies at the folder level.

Step-by-Step:

Create Organization Node: Ensure that your GCP account is linked to an organization.

Create Folders for Business Units:

Navigate to the GCP Console > IAM & Admin > Resource Manager.

Create a folder for each business unit under the organization node.

Move Projects to Folders:

Move existing projects into the respective folders according to the business unit.

Set IAM Policies:

Assign IAM roles and permissions at the folder level to manage access for each business unit independently.

Monitor and Manage: Use Cloud Audit Logs and other GCP tools to monitor the activities and ensure compliance with the organization’s policies.

Creating and Managing Folders

Managing IAM Policies

DNSSEC — use a DNS registrar that supports DNSSEC, and enable it. DNSSEC digitally signs DNS communication, making it more difficult (but not impossible) for hackers to intercept and spoof. Domain Name System Security Extensions (DNSSEC) adds security to the Domain Name System (DNS) protocol by enabling DNS responses to be validated. Having a trustworthy Domain Name System (DNS) that translates a domain name like www.example.com into its associated IP address is an increasingly important building block of today’s web-based applications. Attackers can hijack this process of domain/IP lookup and redirect users to a malicious site through DNS hijacking and man-in-the-middle attacks. DNSSEC helps mitigate the risk of such attacks by cryptographically signing DNS records. As a result, it prevents attackers from issuing fake DNS responses that may misdirect browsers to nefarious websites. https://cloud.google.com/blog/products/gcp/dnssec-now-available-in-cloud-dns

https://cloud.google.com/kms/docs/ekm#how_it_works

- First, you create or use an existing key in a supported external key management partner system. This key has a unique URI or key path.

- Next, you grant your Google Cloud project access to use the key, in the external key management partner system.

- In your Google Cloud project, you create a Cloud EKM key, using the URI or key path for the externally-managed key.

"The service evaluates all changes and remote access attempts to detect runtime attacks in near-real time." : https://cloud.google.com/security-command-center/docs/concepts-container-threat-detection-overview This has nothing to do with KNOWN security Vulns in images

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.

Cloud Data Loss Prevention Documentation

Setting Up DLP Jobs

Cloud Storage Documentation

The action fails because a constraints/iam.allowedPolicyMemberDomains organization policy is in place and only members from the flowlogistic.com organization are allowed. The inheritFromParent: false property on the “Apps” folder means that it does not inherit the organization policy from the organization node. Therefore, only the policy set at the folder level applies, which allows only members from the flowlogistic.com organization. As a result, the attempt to grant access to the user testuser@terramearth.com fails because this user is not a member of the flowlogistic.com organization.

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.

To establish a reliable, cloud-native, and scalable process for updating nodes in your GKE clusters, configuring node auto-upgrades within designated maintenance windows is the most effective approach.​

Option A: Migrating to a self-managed Kubernetes environment would increase operational overhead and complexity, as your team would be responsible for managing the entire infrastructure, including patching and updates. This contradicts the goal of adopting a cloud-first strategy and does not inherently provide a more reliable update process.​

Option B: Developing custom scripts for patch management introduces potential risks and maintenance burdens. Ensuring the reliability, security, and scalability of such scripts can be challenging, and this approach may not align with best practices for managing GKE environments.​

Option C: Scheduling daily reboots does not guarantee that nodes will apply the latest patches or updates. Without a mechanism to manage and apply updates, reboots alone are insufficient to maintain node security and compliance.​

Option D: Configuring node auto-upgrades ensures that GKE automatically keeps your nodes up-to-date with the latest stable versions, reducing the risk of missed critical patches. By setting maintenance windows, you can control when these upgrades occur, minimizing disruptions to your workloads. This approach leverages GKE's managed services to maintain security and compliance efficiently.​

Therefore, Option D is the optimal solution, as it aligns with a cloud-first strategy and leverages GKE's native capabilities to automate and schedule node updates effectively.​

Customer-Managed Encryption Keys (CMEK):

CMEK allows you to manage encryption keys using Cloud Key Management Service (KMS). This gives you control over the lifecycle of the keys, including rotation, destruction, and auditing.

Set up a Cloud KMS key ring and create encryption keys that will be used to protect your data in BigQuery, Cloud SQL, and Cloud Storage.

Configure the services to use CMEK for encrypting data at rest, ensuring compliance with your organization's security policies.

Cloud External Key Manager (EKM):

Cloud EKM allows you to use keys managed by an external key management provider to encrypt data in Google Cloud services.

Integrate your external key management system with Google Cloud using supported protocols and APIs.

Configure your data warehouse services to use the external keys for encryption, ensuring that key management is handled outside of the Google Cloud environment.

Key Access Justifications:

Enable Key Access Justifications to provide visibility into why encryption keys are being accessed. This helps in monitoring and auditing key usage to ensure compliance and security.

Set up policies and logging to capture and review key access requests, providing insights into how and why keys are used.

Access Transparency and Approval:

Implement Access Transparency to gain visibility into Google’s access to your data and encryption keys.

Configure Access Approval to require explicit approval for Google support or engineering access to your data, adding an additional layer of security and control.

The problem requires a publicly accessible Cloud Run service with HTTPS, TLS termination at the edge, threat mitigation, and geo-based access restrictions.

External HTTP(S) Load Balancer: This is the standard Google Cloud component for exposing public-facing web applications, providing a single global IP address, global load balancing, and crucially, TLS termination at the edge of Google's network.

Serverless Network Endpoint Group (NEG): A serverless NEG connects an HTTP(S) Load Balancer to a Cloud Run service (or other serverless backends like Cloud Functions or App Engine), allowing the load balancer to route traffic to the serverless application.Extract Reference: "You can use an external HTTP(S) Load Balancer with a serverless network endpoint group (NEG) to integrate Cloud Run services with advanced load balancing features, such as Cloud CDN, Google Cloud Armor, and Cloud Identity-Aware Proxy." (Google Cloud Documentation: "Connect a Cloud Run service to an HTTP(S) Load Balancer | Cloud Run Documentation" - https://cloud.google.com/run/docs/integrating/load-balancers)

Google-Managed Certificate for TLS Termination: Using Google-managed SSL certificates simplifies the management of HTTPS, as Google handles the provisioning, renewal, and deployment of certificates. The external HTTP(S) Load Balancer terminates TLS using these certificates.Extract Reference: "Google-managed SSL certificates let you use Google Cloud's globally distributed infrastructure to automatically provision and renew your certificates." (Google Cloud Documentation: "Google-managed SSL certificates overview | Load Balancing" - https://cloud.google.com/load-balancing/docs/ssl-certificates/google-managed-certs)

Cloud Armor for Threat Mitigation and Geo-Based Access Control: Cloud Armor is a Web Application Firewall (WAF) service that integrates with HTTP(S) Load Balancers. It provides DDoS protection, allows custom rules for threat mitigation (e.g., SQL injection, XSS), and supports geo-based access control rules to allow or deny traffic based on the source's geographic region.Extract Reference: "Google Cloud Armor helps protect your Google Cloud deployments from various threats, including Distributed Denial of Service (DDoS) attacks and application attacks such as cross-site scripting (XSS) and SQL injection (SQLi)." and "Google Cloud Armor supports geo-based access control, allowing you to filter requests based on geographic region." (Google Cloud Documentation: "Google Cloud Armor overview" - https://cloud.google.com/armor/docs/overview)

Let's evaluate the other options:

A. IAP for authentication and IP-based access control + custom SSL certs: IAP is primarily an authentication layer for users/identities, not a WAF for threat mitigation or geo-blocking for a publicly accessible service before any authentication takes place. While it can apply IP-based access, it's typically for post-authentication controls.

B. Assign custom domain, enable HTTPS, allUsers IAM, firewall rules & VPC SC: While Cloud Run supports custom domains and HTTPS, and allUsers makes it public, direct Cloud Run services do not leverage traditional VPC firewall rules for public ingress. VPC Service Controls are for API access and data exfiltration, not for WAF or geo-blocking of public web traffic.

D. Cloud DNS public zone, static IP, VPC firewall rules, threat signatures: Cloud Run services are managed and do not typically expose a static IP address that can be directly associated with VPC firewall rules for public traffic. VPC firewall rules apply to VMs within a VPC, not to the managed global infrastructure of Cloud Run's public endpoints.

Therefore, deploying an external HTTP(S) Load Balancer with a serverless NEG and integrating it with Google-managed certificates and Cloud Armor is the most comprehensive and Google-recommended solution for meeting all the specified security requirements for a publicly accessible Cloud Run application.

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

Multiple network interfaces. The simplest way to connect multiple VPC networks through a virtual appliance is by using multiple network interfaces, with each interface connecting to one of the VPC networks. Internet and on-premises connectivity is provided over one or two separate network interfaces. With many NGFW products, internet connectivity is connected through an interface marked as untrusted in the NGFW software.

https://cloud.google.com/architecture/best-practices-vpc-design#l7

This architecture has multiple VPC networks that are bridged by an L7 next-generation firewall (NGFW) appliance, which functions as a multi-NIC bridge between VPC networks. An untrusted, outside VPC network is introduced to terminate hybrid interconnects and internet-based connections that terminate on the outside leg of the L7 NGFW for inspection. There are many variations on this design, but the key principle is to filter traffic through the firewall before the traffic reaches trusted VPC networks.

Objective: Ensure that the analytics workload on Compute Engine instances accessing Cloud Storage does not interact with the public internet.

Solution:

Private Google Access: This allows Compute Engine instances that only have internal IP addresses to reach Google APIs and services through a private connection without the need for a public IP address.

No Public IP Addresses: By avoiding public IP addresses for the instances, you ensure that they are not accessible from the internet and do not initiate internet connections.

Steps:

Step 1: Open the Google Cloud Console.

Step 2: Navigate to the VPC Network page and select the subnet where the Compute Engine instances are located.

Step 3: Enable Private Google Access for the subnet.

Step 4: Ensure that when launching the Compute Engine instances, no public IP addresses are assigned to them.

The requirements are runtime threat detection for containers that specifically detects activities like loading additional libraries or executing malicious scripts, with findings visible in Security Command Center (SCC).

Container Threat Detection (CTD) is the specific SCC service component designed to monitor container runtimes for suspicious events like reverse shells, suspicious library loading, and execution of malicious scripts. It is available only with the Security Command Center Premium tier.

Extracts:

"Container Threat Detection (CTD) is a Security Command Center Premium service that provides runtime threat detection for Google Kubernetes Engine (GKE) and Kubernetes clusters." (Source 4.1)

"CTD detects specific runtime events, such as: Execution of malicious scripts... Loading of suspicious libraries... CTD creates high-fidelity Security Command Center findings for these threats." (Source 4.2)

"Security Health Analytics (Option C) identifies misconfigurations and compliance violations, such as overly permissive IAM roles or open firewall ports, but it does not perform runtime threat detection." (Source 4.3)

While using log-based metrics (Option D) is possible, enabling CTD (Option B) is the specific, managed, and authoritative way to generate verified runtime threat findings directly within Security Command Center as required by the prompt.

To use customer-supplied encryption keys (CSEK) for encrypting data on Cloud Storage, follow these steps:

Generate an Encryption Key: Generate a 256-bit AES encryption key. This key should be base64-encoded.

sh

Copy code

openssl rand -base64 32

Upload Object with CSEK: Use the gsutil command-line tool to upload the object to Cloud Storage, specifying the location of the encryption key using the -o option.

gsutil -o "GSUtil:encryption_key=" cp [LOCAL_OBJECT_PATH] gs://[BUCKET_NAME]/

Verify Encryption: After uploading the object, you can verify that it is encrypted using the provided CSEK by checking the object's metadata.

gsutil stat gs://[BUCKET_NAME]/[OBJECT_NAME]

Key Management: Ensure that the encryption key is securely stored and managed. It should not be hard-coded in scripts or applications.

By using the gsutil tool and specifying the encryption key, you ensure that the object is encrypted using the customer-supplied encryption key during the upload process.

Customer-Supplied Encryption Keys (CSEK) Documentation

gsutil Command Line Tool Documentation

Google Cloud customers with additional requirements for encryption of data over WAN can choose to implement further protections for data as it moves from a user to an application, or virtual machine to virtual machine. These protections include IPSec tunnels, Gmail S/MIME, managed SSL certificates, and Istio. https://cloud.google.com/docs/security/encryption-in-transit

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 Standard Tier network only provides regional load balancing, while the Premium Tier supports global load balancing with a single anycast IP address. To distribute requests across multiple regions, you need to use the Premium Tier and update the load balancer configuration accordingly.

Steps:

Upgrade to Premium Tier: Update the load balancer to use the Premium Tier network in the Google Cloud Console.

Add New Instance Group: Add the instance group in the new region (us-east-2) to the backend configuration of the existing load balancer.

Verify Configuration: Ensure that the frontend configuration of the load balancer uses a single external IP address for global distribution.

To ensure that PII data is separated from non-PII data, using Cloud Pub/Sub and Cloud Functions to trigger a scan by the Data Loss Prevention (DLP) API is an effective approach. This method allows for automated detection and handling of PII.

Steps:

Set Up Cloud Pub/Sub: Configure a Cloud Pub/Sub topic to receive notifications whenever a file is uploaded to the shared Cloud Storage bucket.

Deploy Cloud Functions: Create a Cloud Function that is triggered by the Pub/Sub topic. This function will invoke the DLP API to scan the uploaded file for PII.

Move Detected PII Files: If the scan detects PII, the Cloud Function will move the file to a secure Cloud Storage bucket accessible only by the administrator.

Set Permissions: Ensure that appropriate permissions are set on the Cloud Storage buckets to restrict access to files containing PII.

To migrate the current data backup and disaster recovery solutions to GCP while keeping the production environment on-premises, the most scalable and cost-efficient solution is using Google Cloud Storage with scheduled tasks and the gsutil command.

Setup Cloud Storage: Create a Cloud Storage bucket to store the backups.

Go to the Cloud Console and navigate to Cloud Storage.

Click "Create bucket" and follow the prompts to configure the storage bucket.

Install gsutil: Ensure gsutil is installed on the on-premises servers.

gsutil is a command-line tool for interacting with Cloud Storage.

Follow the installation guide here.

Create Backup Script: Write a script to upload data to Cloud Storage using gsutil.

#!/bin/bash gsutil -m cp -r /path/to/local/backup gs://your-bucket-name

Schedule Backup Task: Use a scheduling tool like cron on Linux to run the backup script at regular intervals.

Edit the crontab file with crontab -e and add an entry like:

Cloud Storage Documentation

gsutil Documentation

https://cloud.google.com/vpc/docs/packet-mirroring

Packet Mirroring clones the traffic of specified instances in your Virtual Private Cloud (VPC) network and forwards it for examination. Packet Mirroring captures all traffic and packet data, including payloads and headers.

This question combines two powerful security features: VPC Service Controls (VPC SC) for data exfiltration prevention and Context-Aware Access (CAA) for fine-grained user access based on context.

Contextual Requirement: Requiring a "company-managed device," "specific location," etc., is the function of Context-Aware Access (CAA), implemented via an Access Level.

Combining VPC SC and CAA: CAA policies can be integrated with VPC SC perimeters to enforce the context for access into the perimeter.

Evaluating Impact: To evaluate changes without blocking access, the entire VPC SC perimeter (including the new CAA rule) should be configured in dry run mode.

Extracts:

"Context-Aware Access (CAA) allows you to define and enforce granular access to Google Cloud resources based on user attributes like device security status, IP address (location), and identity." (Source 3.1)

"When implementing a new security policy... it is best practice to initially configure the VPC Service Controls perimeter (including any associated Access Levels/Context-Aware Access) in dry run mode. Dry run mode allows you to test the perimeter's effect on services without blocking any access." (Source 3.2)

"You can use an Access Level (the core component of CAA) to define the conditions for accessing resources protected by a Service Perimeter." (Source 3.3)

The Organization Administrator and Super Admin roles have extensive administrative privileges at the organization level. Restricting these roles is crucial to limit the number of users who have the ability to manage critical resources and configurations within the organization, thereby enhancing security and minimizing potential risks.

Organization Administrator: Has comprehensive permissions to manage all aspects of the Google Cloud organization, including projects, folders, and IAM policies.

Super Admin: In Google Workspace (formerly G Suite), the Super Admin has access to all administrative features and can manage user accounts, services, and settings across the organization.

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

To provide users with SSO-based access to selected cloud apps, Cloud Identity as your IdP supports the OpenID Connect (OIDC) and Security Assertion Markup Language 2.0 (SAML) protocols. https://cloud.google.com/identity/solutions/enable-sso

The critical requirements are:

De-identify PII (protect individual privacy).

Retain original format and consistency (analytical integrity).

Avoid full irreversible deletion (the process must be reversible/re-identifiable).

Sensitive Data Protection (SDP), also known as Cloud DLP, is Google Cloud's specialized service for discovering, classifying, and de-identifying sensitive data. The specific de-identification technique that meets the need to retain the original format and consistency is Format-Preserving Encryption (FPE).

Extracts:

"Sensitive Data Protection supports several types of tokenization, including transformations that can be reversed, or 're-identified.'" (Source 5.3)

"Pseudonymization by replacing with cryptographic format preserving token (CryptoReplaceFfxFpeConfig)... Preserves format... Reversible transformations can be reversed to re-identify the sensitive data using the content.reidentify method." (Source 5.3)

"Format Preserving Encryption (FPE) is an encryption algorithm that preserves the format of the original data set, but it replaces it with tokens that have no inherent meaning or value... FPE ensures the ciphertext maintains the same format (length, number of hyphens, etc.) as the original plaintext." (Source 5.1)

FPE is necessary for analytical integrity when the structure/format (e.g., 9-digit SSN, 16-digit credit card number) is required for processing in downstream systems.

Organization Policy: Use the constraints/compute.skipDefaultNetworkCreation organization policy constraint to disable the creation of default networks in new projects.

Policy Application: Apply this constraint at the organization level to ensure it affects all projects within your organization, preventing the creation of default networks.

Best Practices Compliance: Following this best practice helps maintain a clean and secure network configuration by avoiding the use of default networks, which may not be properly segmented or secured.

Verification: Verify the policy application by creating new projects and ensuring that default networks are not created. References:

Google Cloud - Organization Policy Constraints

Google Cloud - Best Practices for Enterprise Organizations

"To support common use cases like setting a Time to Live (TTL) for objects, retaining noncurrent versions of objects, or "downgrading" storage classes of objects to help manage costs, Cloud Storage offers the Object Lifecycle Management feature. This page describes the feature as well as the options available when using it. To learn how to enable Object Lifecycle Management, and for examples of lifecycle policies, see Managing Lifecycles." https://cloud.google.com/storage/docs/lifecycle

https://cloud.google.com/sql/docs/mysql/cmek

"You might have situations where you want to permanently destroy data encrypted with CMEK. To do this, you destroy the customer-managed encryption key version. You can't destroy the keyring or key, but you can destroy key versions of the key."

https://cloud.google.com/resource-manager/docs/organization-policy/restricting-domains#setting_the_organization_policy

The domain restriction constraint is a type of list constraint. Google Workspace customer IDs can be added and removed from the allowed_values list of a domain restriction constraint. The domain restriction constraint does not support denying values, and an organization policy can't be saved with IDs in the denied_values list. All domains associated with a Google Workspace account listed in the allowed_values will be allowed by the organization policy. All other domains will be denied by the organization policy.

If you want to manage the key encryption keys used for your data at rest, instead of having Google manage the keys, use Cloud Key Management Service to manage your keys. This scenario is known as customer-managed encryption keys (CMEK). https://cloud.google.com/bigquery/docs/encryption-at-rest

Objective: Understand the security characteristics of VPC peering.

Security Characteristics:

Non-transitive Peering: VPC peering connections are non-transitive. This means that peering is strictly between two VPC networks. If VPC A is peered with VPC B, and VPC B is peered with VPC C, VPC A cannot communicate with VPC C unless a direct peering connection is established.

Inter-Organization Peering: VPC peering allows you to connect VPC networks across different Google Cloud Platform organizations, facilitating private communication between distinct organizational units.

These characteristics ensure controlled and secure connectivity between VPC networks while preventing unintended data exposure.

For massive daily data transfers (250 TB) between cloud providers, traditional VPNs or routing through on-premises "hairpinning" are inefficient and costly. Cross-Cloud Interconnect is the architecturally correct solution for high-bandwidth, low-latency, and secure cloud-to-cloud connectivity.

According to Google Cloud Documentation (Cross-Cloud Interconnect Overview):

"Cross-Cloud Interconnect helps you establish high-bandwidth dedicated connectivity between Google Cloud and another cloud service provider (CSP) such as Microsoft Azure... It simplifies multi-cloud setup by providing a direct physical connection between Google's network and the other CSP's network."

Calculation of Requirement:

Data Volume: 250 TB per day.

Time: 86,400 seconds (1 day).

Required Bandwidth: $250 \times 10^{12} \text{ bytes} \times 8 \text{ bits/byte} / 86,400 \text{ seconds} \approx 23.1 \text{ Gbps}$.

A standard VPN (Option B) typically caps out at 3 Gbps per tunnel, making it insufficient.

The existing 20Gbps Interconnect (Option C) is already used for on-premises data and would be overwhelmed by an additional 23.1 Gbps requirement, not to mention the latency of routing Azure traffic through an on-premises data center.

Why other options are incorrect:

B is incorrect: VPN throughput is insufficient for 250 TB/day and lacks the reliability of a dedicated circuit.

C is incorrect: Routing through on-premises (hairpinning) introduces unnecessary latency and would exceed the 20Gbps capacity of the existing Interconnect.

D is incorrect: SFTP over the public internet is neither efficient for petabyte-scale data nor as secure as a private interconnect.

Objective: Store and retrieve sensitive configuration data for an application running on Compute Engine.

Solution: Use Secret Manager to securely store and manage access to sensitive configuration data.

Steps:

Step 1: Open the Google Cloud Console.

Step 2: Navigate to the Secret Manager section.

Step 3: Create a new secret and add the sensitive configuration data.

Step 4: Set appropriate IAM policies to control access to the secret.

Step 5: Update the application to retrieve the secret from Secret Manager using the appropriate client libraries or APIs.

Secret Manager provides a secure and centralized way to manage sensitive information, with fine-grained access control and audit logging capabilities.

https://cloud.google.com/load-balancing/docs/ssl - SSL Proxy Load Balancing is a reverse proxy load balancer that distributes SSL traffic coming from the internet to virtual machine (VM) instances in your Google Cloud VPC network.

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.