Google Professional-Cloud-Security-Engineer Google Cloud Certified - Professional Cloud Security Engineer Exam Practice Test

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

Challenge:

Prevent common misconfigurations that expose services (e.g., MYSQL) to the public internet.

Hierarchical Firewall Policies:

These policies can be applied at the organization level to enforce consistent network security rules across all projects.

Solution:

Create a hierarchical firewall policy that allows connections only from internal IP ranges.

This policy ensures that services like MySQL are not exposed to 0.0.0.0/0 (the entire internet).

Steps:

Step 1: Define the hierarchical firewall policy at the organization level.

Step 2: Set the rule to allow traffic only from internal IP ranges.

Step 3: Apply the policy to all projects under the organization.

Benefits:

Centralized management of network security.

Prevents accidental exposure of services to the public internet, enhancing security.

Enable Security Command Center (SCC):

SCC provides centralized visibility and control over your cloud resources' security status.

Ensure that SCC is enabled in your Google Cloud environment.

Configure Virtual Machine Threat Detection (VMTD):

VMTD is part of SCC and specializes in detecting threats within VM instances, such as cryptocurrency mining malware.

Navigate to the SCC settings in the Google Cloud Console.

Activate VMTD:

Enable VMTD for the projects or resources where you want to monitor and detect potential threats.

VMTD uses behavioral analysis to identify anomalies indicative of unauthorized mining activities.

Monitor and Respond to Alerts:

VMTD generates alerts when it detects suspicious activities, such as unauthorized cryptocurrency mining.

Set up appropriate response actions, such as notifications, automatic remediation, or manual investigation, to handle these alerts.

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

Cloud NAT Service: Use Cloud NAT (Network Address Translation) to allow VM instances without external IP addresses to access the internet securely.

Configuration: Configure Cloud NAT for the subnets containing your VM instances. This setup allows the VMs to initiate outbound connections to the internet for updates and other necessary communications.

Security Compliance: By using Cloud NAT, you adhere to the security policy of not assigning external IP addresses to VMs while still enabling necessary internet connectivity. Cloud NAT provides a secure method for outbound internet traffic without exposing VMs directly to the public internet. References:

Google Cloud - Cloud NAT Overview

Google Cloud - Configuring Cloud NAT

”This encryption method is reversible, which helps to maintain referential integrity across your database and has no character-set limitations.” https://cloud.google.com/blog/products/identity-security/take-charge-of-your-data-how-tokenization-makes-data-usable-without-sacrificing-privacy

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

FPE provides fewer security guarantees compared to other deterministic encryption methods such as AES-SIV. For these reasons, Google strongly recommends using deterministic encryption with AES-SIV instead of FPE for all security sensitive use cases. Other methods like deterministic encryption using AES-SIV provide these stronger security guarantees and are recommended for tokenization use cases unless length and character set preservation are strict requirements—for example, for backward compatibility with a legacy data system.

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

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

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

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

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

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

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

References

Managing Service Account Keys

Service Account Key Rotation

To meet the requirements for exporting and auditing security logs in near real-time for login activity events and API calls:

Organization-Level Log Sink with Pub/Sub: Create a log sink at the organization level to ensure logs from all projects are captured. Use the includeChildren parameter to include logs from all child resources (projects). Set the destination to a Pub/Sub topic to facilitate near real-time log export to an external SIEM.

Enable Data Access Audit Logs: Enable Data Access audit logs at the organization level to capture and export detailed information about API calls that modify configurations of Google Cloud resources across all projects.

These steps ensure comprehensive logging and real-time export capabilities, which are crucial for security auditing and monitoring.

References

Audit Logs Overview

Exporting with Sinks

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.

Packet Mirroring Setup: Configure Packet Mirroring in your Google Cloud VPC to capture traffic to and from specific VM instances. This allows you to analyze the traffic for security and compliance purposes.

Security Software: Use specialized security software to inspect the mirrored traffic. This software can detect invalid or malicious content in the IP packets.

Mirroring Configuration: Specify the instances, network, and traffic direction (ingress, egress, or both) to be mirrored. Ensure that the mirrored traffic is directed to an appropriate analysis destination.

Traffic Analysis: Continuously monitor and analyze the mirrored traffic for any signs of malicious activity or anomalies. Use the findings to enhance your security posture and respond to potential threats. References:

Google Cloud - Packet Mirroring

Google Cloud - Packet Mirroring Best Practices

"Your team needs to obtain a unified log view of all development cloud projects in your SIEM" - This means we are ONLY interested in development projects. "The development projects are under the NONPROD organization folder with the test and pre-production projects" - We will need to filter out development from others i.e test and pre-prod. "The development projects share the ABC-BILLING billing account with the rest of the organization." - This is unnecessary information.

To minimize the attack surface of the container for an internet-facing application running on Google Kubernetes Engine (GKE), the best practice is to build small containers using small base images. This approach helps in the following ways:

Reduce Vulnerabilities: Smaller base images contain fewer packages and dependencies, which minimizes the potential vulnerabilities that an attacker could exploit.

Improved Security: Using minimal base images such as distroless or Alpine Linux ensures that only the necessary components are included, reducing the attack surface significantly.

Easier Maintenance: Small containers are easier to maintain and update, ensuring that security patches can be applied quickly without dealing with unnecessary components.

Steps to Implement:

Choose a Minimal Base Image:

Use base images like gcr.io/distroless/base or alpine.

FROM gcr.io/distroless/base COPY myapp /myapp CMD ["/myapp"]

Optimize Container Image:

Remove unnecessary tools and libraries.

Use multi-stage builds to keep the final image small.

Regularly Update Base Images:

Keep the base images up-to-date with the latest security patches.

The problem requires granting an external partner team access solely to a database, without extending to other network resources, and following Google-recommended practices.

Workforce Identity Federation: This Google Cloud IAM feature is specifically designed for scenarios where an organization needs to grant Google Cloud access to external identities (like partners, contractors, or customers) who are managed by their own identity provider (IdP). It allows these external users to authenticate using their existing credentials and then gain access to specified Google Cloud resources.

Extract Reference: "Workforce Identity Federation lets you use an external identity provider (IdP) to authenticate and authorize a workforce—a group of users, such as employees, partners, and contractors—using IAM, so that the users can access Google Cloud services." (Google Cloud Documentation: "Workforce Identity Federation | IAM Documentation" - https://cloud.google.com/iam/docs/workforce-identity-federation)

Extract Reference: "Secure access for partners and vendors. Workforce Identity Federation can enable enterprises to selectively federate users from partner or vendor IdPs without requiring IT teams to sync or create a separate identity store to use Google Cloud resources." (Google Cloud Documentation: "Introducing Workforce Identity Federation..." - https://www.azalio.io/introducing-workforce-identity-federation-to-easily-manage-workforce-access-to-google-cloud/)

Least Privilege and Isolation: With Workforce Identity Federation, you create an identity pool and a provider that trusts the partner's IdP. You then grant IAM roles only to the workforce pool (or specific identities within it) on the specific database resource. This ensures fine-grained access control and prevents access to other resources in your network, directly addressing the least privilege and isolation requirements. The partner's identities are never synced into your internal Cloud Identity directory.

Let's evaluate the other options:

A. Add a public IP address... Securely distribute credentials: Adding a public IP address exposes the database to the internet, which is a major security risk and contradicts "restricted solely to the database and can not extend to any other resources within your company's network" as it allows any external network to potentially reach it. Distributing credentials manually is also not a Google-recommended secure practice.

B. Create accounts for the partner team in your corporate identity provider. Synchronize these accounts with Google Cloud Identity: This means you become responsible for managing the partner's identities within your own corporate IdP and syncing them. This is an unnecessary operational burden and blurs the lines of identity management. It also may inadvertently grant them broader network access if your corporate IdP is connected to your internal network resources.

C. Ask the partner team to set up Cloud Identity accounts within their own corporate environment and identity provider. Grant the partner’s Cloud Identity accounts access: While better than B, this implies the partner managing Cloud Identity accounts themselves and you directly granting IAM roles to their Cloud Identity users. Workforce Identity Federation is a more robust and scalable solution for federating any external IdP with Google Cloud IAM, rather than requiring partners to adopt Cloud Identity directly. Workforce Identity Federation is the explicit pattern for cross-organization access using existing external IdPs.

Therefore, Workforce Identity Federation is the most secure, scalable, and Google-recommended solution for granting restricted access to external partner teams.

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

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

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

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

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

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

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

Let's evaluate the other options:

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

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

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

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

"Isolate VMs using service accounts when possible" "even though it is possible to uses tags for target filtering in this manner, we recommend that you use service accounts where possible. Target tags are not access-controlled and can be changed by someone with the instanceAdmin role while VMs are in service. Service accounts are access-controlled, meaning that a specific user must be explicitly authorized to use a service account. There can only be one service account per instance, whereas there can be multiple tags. Also, service accounts assigned to a VM can only be changed when the VM is stopped." https://cloud.google.com/solutions/best-practices-vpc-design#isolate-vms-service-accounts

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.

Google Cloud Storage provides an Object Lifecycle Management feature that can help automate data retention and deletion processes, ensuring compliance with data privacy regulations while minimizing storage costs.

Lifecycle Management: Object Lifecycle Management allows you to define rules that automatically delete objects after a specific period. This ensures that data is only retained for the required amount of time and is deleted once it expires.

Configuration: You can configure lifecycle rules to delete objects based on conditions such as the age of the object, the creation date, or custom metadata. This allows for precise control over the retention period of your data.

Cost Efficiency: By using lifecycle policies to delete data automatically, you can reduce storage costs, as you only pay for the storage you actively use.

References

Cloud Storage Object Lifecycle Management

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 reduce the scope of systems subject to PCI audit standards, segregate the cardholder data environment (CDE) into a separate GCP project. This ensures that only the project containing the CDE will be subject to PCI DSS compliance, reducing the audit scope for other projects.

Create Separate GCP Project:

Go to the Cloud Console, navigate to IAM & Admin > Manage Resources.

Click "Create Project" and set up a new project for the CDE.

Migrate CDE:

Transfer the systems processing, storing, or transmitting cardholder data to the new project.

Apply PCI DSS Controls:

Implement PCI DSS required controls on the new project.

Use appropriate security measures such as firewalls, access controls, and encryption.

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.

To monitor and get notified in case the script causing the Compute Engine instance to crash is executed again, you should create an Alerting Policy in Stackdriver (now known as Google Cloud Monitoring). The Process Health condition can be set to monitor the number of executions of the script and ensure it remains below the desired threshold. By enabling notifications, you will be alerted if this threshold is exceeded.

Step-by-Step:

Log Script Executions: Ensure that the script execution is logged.

Create a User-Defined Metric: Go to Google Cloud Console > Logging > Logs-based Metrics, and create a new user-defined metric that counts the number of times the script executes.

Set Up Alerting Policy:

Navigate to Google Cloud Console > Monitoring > Alerting.

Click on “Create Policy”.

Add a condition and select “Logs-based Metric”.

Configure the condition to trigger when the number of script executions exceeds the threshold.

Configure Notifications: Add notification channels (email, SMS, etc.) to the alerting policy.

Save and Test: Save the policy and test to ensure notifications are received when the script is executed beyond the threshold.

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.

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.

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.

To protect your web application from threats like malware by implementing TLS interception for incoming traffic, configuring a Secure Web Proxy with TLS offloading at the load balancer is an effective approach.​

Option A: By configuring a Secure Web Proxy, you can offload TLS traffic at the load balancer, inspect the decrypted traffic for threats such as malware, and then forward the inspected traffic to your web application. This approach ensures that encrypted traffic is securely analyzed without compromising the security of the data in transit.​

Option B: An internal proxy load balancer is designed for distributing traffic within a private network and may not support TLS interception capabilities required for inspecting incoming traffic from external sources.​

Option C: Hierarchical firewall policies in Google Cloud are used to enforce security rules across your organization but do not provide TLS interception capabilities.​

Option D: VPC firewall rules control traffic to and from VM instances based on specified rules but do not have the capability to perform TLS interception or traffic inspection.​

Therefore, Option A is the most suitable solution, as it allows for TLS interception through a Secure Web Proxy, enabling the inspection of incoming encrypted traffic to detect and mitigate threats like malware before the traffic reaches your web application.​

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.

This approach allows you to control network traffic at the folder level. By attaching external IP addresses to the VMs in scope, you can ensure that the VMs have a unique, routable IP address for outbound connections. Then, by defining and applying a hierarchical firewall policy at the folder level, you can enforce that egress connections are limited to the specified IP range and only from the specified VPC network.

Use the org policy constraint "Google Cloud Platform - Resource Location Restriction" on your Google Cloud organization node: This organizational policy constraint allows you to restrict the locations where your resources can be created. By setting this constraint to allow only Europe regions, you can ensure compliance with GDPR and other regional regulations.

Implementation: To implement this, you need to configure the organization policy with the constraint constraints/gcp.resourceLocations. You can specify allowed regions such as europe-west1 and europe-west4 to ensure resources are only created in these locations.

References

Resource Location Restriction documentation

GDPR compliance on Google Cloud

"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."

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.

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

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.

https://cloud.google.com/logging/docs/export/aggregated_sinks#supported-destinations

You can use aggregated sinks to route logs within or between the same organizations and folders to the following destinations: - Another Cloud Logging bucket: Log entries held in Cloud Logging log buckets.

To address inconsistencies in your project's Identity and Access Management (IAM) configuration and gain comprehensive visibility into IAM policy changes, user activity, service account behavior, and access to sensitive projects, leveraging Google Cloud's auditing capabilities is essential.​

Option A: While Cloud Monitoring's metrics explorer can track certain metrics, it is not designed to provide detailed logs of IAM policy changes or user activities.​

Option B: Cloud Audit Logs offer detailed records of administrative activities, including IAM policy changes and authentications. By creating log export sinks, you can forward these logs to a Security Information and Event Management (SIEM) solution, enabling correlation with other event sources and comprehensive analysis. This approach provides the necessary visibility into IAM configurations and user activities.​

Option C: Triggering Cloud Functions based on IAM policy changes and analyzing them with a policy simulator is a proactive approach. However, it may not provide the depth of historical data and comprehensive analysis capabilities that a SIEM solution offers.​

Option D: Deploying the OS Config Management agent focuses on VM configuration and patch management, which does not directly address IAM policy monitoring or user activity tracking.​

Therefore, Option B is the most effective solution to gain detailed visibility into IAM-related activities and address the identified inconsistencies.​

Enable Firewall Rules Logging on the latest rules that were changed. Use Logs Explorer to analyze whether the rules are working correctly:

Enable Firewall Rules Logging for the specific firewall rules in question through the Google Cloud Console.

Once logging is enabled, use Logs Explorer to filter and review the firewall logs.

Analyze the logs to determine if the rules are allowing or blocking traffic as intended, identifying any misconfigurations or issues.

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.

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

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

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

Let's evaluate the other options:

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

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

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

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

Uniform Bucket-Level Access: Enable uniform bucket-level access for all your Cloud Storage buckets. This feature ensures that access control is applied consistently at the bucket level, simplifying management and improving security.

Domain Restricted Sharing: Enforce domain-restricted sharing through an organization policy. This policy ensures that only users within your organization’s domain can access the data in the buckets, preventing public exposure.

Policy Enforcement: Apply the necessary IAM policies and ensure that no buckets are configured to allow public access. This combination of settings ensures that data in Cloud Storage buckets remains private and accessible only to authorized users within your organization. References:

Google Cloud - Uniform Bucket-Level Access

Google Cloud - Organization Policy Service

If the environment variable GOOGLE_APPLICATION_CREDENTIALS is set, ADC uses the service account key or configuration file that the variable points to. If the environment variable GOOGLE_APPLICATION_CREDENTIALS isn't set, ADC uses the service account that is attached to the resource that is running your code. https://cloud.google.com/docs/authentication/production#passing_the_path_to_the_service_account_key_in_code

Enable Dry Run Mode: Start by enabling the dry run mode for your VPC Service Controls perimeter. This mode allows you to test changes without actually enforcing them, thus preventing any disruption to your current setup.

Add Access Level: Add your new access level to the dry run configuration. This way, you can monitor how the new access level would behave and interact with your existing setup without any real impact.

Vetting Process: Carefully vet the new access level by analyzing logs and monitoring the behavior in the dry run mode. Ensure that the new configuration meets your security and operational requirements.

Update Perimeter: Once you are confident that the new access level will not disrupt existing services and meets all requirements, update the actual perimeter configuration with the new access level. This approach minimizes risk by allowing you to test changes before they take effect, ensuring seamless updates with minimal disruption. References:

Google Cloud - Configuring VPC Service Controls

Google Cloud - Using Dry Run Mode

To grant read access to a third-party disk image stored in an external Google Cloud organization so it can be deployed into a VPC Service Controls perimeter, you need to update the service perimeter to allow egress traffic from your projects to the external project.

Update the Service Perimeter:

Go to the Google Cloud Console, navigate to Security > VPC Service Controls.

Select the appropriate service perimeter that includes your image repository project.

Configure Egress Policy:

Within the perimeter settings, configure the egressTo field to allow traffic to the external project.

Set the identityType to ANY_IDENTITY to permit any principal to access the external project for this specific egress rule.

Specify External Project and Service:

In the egressFrom field, include the external Google Cloud project number as an allowed resource.

Set the serviceName to compute.googleapis.com to specifically allow access to the Compute Engine service in the external project.

This configuration permits your internal projects to read the disk image from the external project while maintaining the security boundaries established by the service perimeter.

A. Create a Cloud Build pipeline that will monitor changes to your container templates in a Cloud Source Repositories repository. Add a step to analyze Container Analysis results before allowing the build to continue:

Use Cloud Build to automate your CI/CD pipeline.

Integrate Container Analysis to scan container images for vulnerabilities during the build process.

If vulnerabilities are found, configure the build to fail, preventing deployment of insecure containers.

E. In your CI/CD pipeline, add an attestation on your container image when no vulnerabilities have been found. Use a Binary Authorization policy to block deployments of containers with no attestation in your cluster:

Use Binary Authorization to enforce deploy-time security policies.

Configure your CI/CD pipeline to generate attestations for container images that pass vulnerability scans.

Binary Authorization will then block deployments of any containers without valid attestations, ensuring only secure images are deployed.

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.

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.

To prevent developers from creating user-managed service account keys and reduce the risk of key mismanagement, you should enable an organization policy that specifically prohibits the creation of these keys.

Enable an organization policy to prevent service account keys from being created (C):

Google Cloud provides the capability to enforce organizational policies that restrict various actions, including the creation of service account keys. By enabling this policy, you ensure that developers cannot create new user-managed service account keys, thus minimizing the risk of key mismanagement and potential security breaches.

References

Service Accounts documentation

Organization Policy Service documentation

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

Step-by-Step:

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

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

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

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

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.

To ensure that Vertex AI Workbench Instances are automatically kept up-to-date and that users cannot alter operating system settings, implementing specific organization policies is essential.​

Option A: Enabling VM Manager and adding Compute Engine instances assists in managing and monitoring VM instances but does not enforce automatic updates or restrict user modifications to the operating system.​

Option B: Enforcing the disableRootAccess organization policy prevents users from gaining root access, thereby restricting unauthorized changes to the operating system. Additionally, the requireAutoUpgradeSchedule policy ensures that instances are automatically updated according to a defined schedule. Together, these policies maintain system integrity and compliance with update requirements.​

Option C: Assigning AI Notebooks Runner and AI Notebooks Viewer roles controls user permissions related to running and viewing notebooks but does not directly influence operating system settings or update mechanisms.​

Option D: Implementing firewall rules to prevent SSH access limits direct access to instances but does not ensure automatic updates or prevent alterations through other means.​

Therefore, Option B is the most appropriate action, as it directly addresses both the enforcement of automatic updates and the prevention of unauthorized operating system modifications.​

When using Google Cloud's Platform-as-a-Service (PaaS) offerings like App Engine, Google manages the infrastructure, including the underlying OS, runtime, and scaling. However, securing the application code itself, such as defending against cross-site scripting (XSS) and SQL injection (SQLi) attacks, remains the responsibility of the user. This involves implementing secure coding practices, validating inputs, and employing appropriate security measures within the application.

To keep track of "who did what, where, and when?" within GCP projects, the administrator should focus on Admin Activity logs and Data Access logs. Here’s a detailed explanation of why these two log streams are essential:

Admin Activity Logs:

These logs capture administrative actions performed in your Google Cloud resources. This includes actions like creating, modifying, or deleting resources.

Admin Activity logs provide detailed information about the user who performed the action, the resource that was affected, the action performed, and the timestamp.

Data Access Logs:

These logs capture read and write operations on data within your Google Cloud services. This includes actions like accessing or modifying data stored in databases, storage buckets, etc.

Data Access logs help track the access patterns of users and services to sensitive data, providing insights into who accessed which data and when.

Steps to Enable and Access Logs:

Navigate to the Google Cloud Console.

Go to Logging in the left-hand menu.

Enable Admin Activity and Data Access logs if not already enabled.

Use Logs Explorer to filter and view specific logs based on your requirements.

By monitoring both Admin Activity and Data Access logs, administrators can gain comprehensive visibility into the actions performed on their GCP resources and data, ensuring robust security and compliance tracking.

To allow Compute Engine instances to access public repositories for security updates while an egress firewall rule is in place to deny all internet traffic, you need to create a more specific egress rule that permits traffic to the CIDR range of the repository. The priority of this rule should be lower (i.e., a higher priority number) than the deny rule.

Steps:

Identify the CIDR Range: Determine the CIDR range of the public repository from which the security updates will be fetched.

Create Egress Firewall Rule: Create a new egress firewall rule allowing traffic to the identified CIDR range with a priority less than 1000.

Apply Firewall Rule: Use the Google Cloud Console or gcloud command-line tool to apply the new firewall rule.

To ensure that a Compute Engine instance does not have access to the internet or to any Google APIs or services, you need to disable the following settings:

Public IP: Disabling the public IP address ensures that the instance does not have a direct connection to the internet. Without a public IP address, the instance cannot be accessed from or communicate with the internet directly.

Private Google Access: Disabling Private Google Access ensures that the instance does not have access to Google APIs and services through the internal Google network. Private Google Access allows instances without a public IP to reach Google APIs and services using private IP addresses, but disabling it will block this path.

Disabling these settings will effectively isolate the instance from both the public internet and Google's internal API services.

References

Google Cloud VPC Documentation - Overview

Configuring Private Google Access

Compute Engine Network Overview

To enable developer teams to deploy new applications without the extensive overhead of network and security reviews, it’s recommended to mandate the use of infrastructure as code (IaC) and enforce policies through static analysis in CI/CD pipelines. This approach ensures that security and compliance policies are checked automatically during the development process.

Step-by-Step:

Adopt IaC: Use tools like Terraform or Google Cloud Deployment Manager to manage infrastructure as code.

CI/CD Pipeline Integration: Integrate static analysis tools such as TFLint or Checkov in the CI/CD pipeline to enforce security policies.

Policy Definition: Define security policies and best practices that need to be adhered to in the code.

Automated Checks: Configure automated checks in the CI/CD pipeline to review code against these policies before deployment.

Monitor and Audit: Continuously monitor and audit deployed applications to ensure ongoing compliance.

Objective: Implement external web application protection and validate policy changes before enforcement.

Solution: Use Google Cloud Armor's preconfigured rules in preview mode.

Steps:

Step 1: Open the Google Cloud Console.

Step 2: Navigate to the Google Cloud Armor section.

Step 3: Create or select a security policy.

Step 4: Apply preconfigured rules to the policy.

Step 5: Enable preview mode to simulate the effects of the rules without enforcing them.

Step 6: Monitor the logs to validate the policy changes.

Google Cloud Armor's preview mode allows you to test and validate the impact of security policies on your application traffic before applying them, ensuring that they work as intended without disrupting the service.

A security key is a physical device that you can use for two-step verification, providing an additional layer of security for your Google Account. Security keys can defend against phishing and man-in-the-middle attacks, making your login process more secure.

To ensure least-privilege access and provide necessary permissions to the DevOps team only during a deployment issue, follow these steps:

Create a Service Account:

In your Google Cloud project, create a new service account specifically for the DevOps team.

Assign Limited Permissions:

Grant the service account permissions with only the necessary list/view roles. For instance, you can create a custom IAM role with compute.instances.list and compute.instances.get permissions.

Grant Service Account User Role:

Assign the Service Account User role to the DevOps team members for the created service account. This allows them to act as the service account and use its permissions.

Access Control During Incidents:

During a deployment issue, the DevOps team can temporarily use the service account to access the resources. This ensures they have the least-privilege access required to investigate and resolve the issue.

Automation and Monitoring:

Implement automation to enable and disable the service account access as needed and monitor the usage to ensure compliance with the least-privilege principle.

Benefits:

Security: Limits access to only what is necessary, reducing the risk of unauthorized changes.

Flexibility: Provides necessary access during incidents without granting permanent elevated permissions.

References

Creating and Managing Service Accounts

Service Account User Role

To ensure that payment information is encrypted between the customer’s browser and Google Cloud Platform during checkout, the company should configure an SSL certificate on an L7 (Layer 7) Load Balancer. Here’s why this is the best solution:

SSL/TLS Termination: An L7 Load Balancer can handle SSL/TLS termination, which means it can decrypt HTTPS traffic, offloading the work from the backend servers. This is essential for handling encrypted connections securely.

HTTPS Configuration: By configuring an SSL certificate, the load balancer ensures that all traffic between the customer’s browser and the application is encrypted using HTTPS.

Security Best Practices: Using an L7 Load Balancer with an SSL certificate aligns with best practices for securing web applications, particularly for e-commerce sites handling sensitive payment information.

Managed Certificates: Google Cloud offers managed SSL certificates, which simplifies the process of obtaining, deploying, and renewing SSL certificates.

Implementation Steps:

Obtain an SSL certificate.

Configure the L7 Load Balancer in the GCP Console.

Associate the SSL certificate with the load balancer.

Ensure that the backend services are configured to handle HTTPS traffic.

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

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

Step-by-Step:

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

Grant Role:

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

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

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

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

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

Since the domain is already being used by G Suite, the best course of action is to minimize disruption by discovering any existing uses of Google-managed services. Collaborate with the existing Super Administrator to align the setup with the company's requirements.

Step-by-Step:

Identify Existing Usage: Have the customer's management identify all current uses of the domain within Google-managed services.

Collaboration: Work closely with the existing Super Administrator of the domain.

Provision Required Accounts: Ask the Super Administrator to provision necessary accounts and permissions for the data science manager or other relevant personnel.

Integrate SAML IdP: Ensure that the existing domain integrates with the company’s SAML 2.0 IdP for user authentication.

Set Up Cloud Identity: Configure Cloud Identity under the guidance of the Super Administrator without disrupting current services.

Enable the compute.restrictXpnProjectLienRemoval organization-level policy constraint:

This constraint prevents users from removing liens from Shared VPC host projects.

By enabling this constraint, you ensure that the Shared VPC host project cannot be accidentally deleted, as liens prevent deletion without proper authorization.

Apply this constraint via the Google Cloud Console or using the gcloud command-line tool.

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.

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:

Objective: Quickly recover a deleted service account used for data transfers.

Solution: Use the undelete command available in the gcloud command-line tool to recover the service account.

Steps:

Step 1: Open the Cloud Shell in the Google Cloud Console.

Step 2: Run the following command to list the deleted service accounts:

gcloud iam service-accounts list --filter="deleted: true"

Step 3: Identify the name and ID of the deleted service account.

Step 4: Use the undelete command to recover the service account:

gcloud iam service-accounts undelete [SERVICE_ACCOUNT_ID]

Step 5: Verify that the service account has been restored and reassign any necessary permissions.

By using the undelete command, you can quickly restore the service account and resume application functionality without compromising security.

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

Steps:

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

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

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

Assured Workloads for Google Cloud allows you to deploy regulated workloads with data residency, access, and support requirements. It helps you configure your environment in a manner that aligns with specific compliance frameworks and standards.

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.

Enable automatic key version rotation on a regular schedule:

Regularly rotating keys reduces the impact of a potentially compromised key by limiting the amount of data encrypted with a single key version.

Set up automatic key rotation in Cloud KMS to ensure keys are rotated without manual intervention.

Limit the number of messages encrypted with each key version:

Reducing the number of messages encrypted with each key version minimizes the potential data exposure in case of a key compromise.

Implement policies to ensure that new key versions are used periodically to limit the usage of each key version.

Access Google Cloud Console:

Log in to the Google Cloud Console with administrative privileges.

Navigate to the "IAM & Admin" section.

Set Session Length Timeout:

Go to the "Settings" page within IAM & Admin.

Locate the "Session control" settings.

Configure the session length timeout to a shorter duration, such as 15 or 30 minutes. This ensures that user sessions expire automatically after the specified time of inactivity.

Apply and Enforce the Policy:

Save the changes and ensure the new session timeout policy is applied across all users and services.

Communicate the new policy to employees, highlighting the importance of session security and the rationale behind the change.

Additional Security Measures:

Consider implementing additional measures such as automatic screen locks and secure session management practices.

Educate employees on the importance of logging out of their sessions and securing their devices when not in use.

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.

By generating a key in your on-premises environment and storing it in an HSM that you manage, you're ensuring that the key material is fully under your control. Using the key as an external key in Cloud KMS allows you to use the key with Google Cloud services without having the key stored on Google Cloud. Activating Key Access Justifications (KAJ) provides a reason every time the key is accessed, and you can configure the external key system to reject unauthorized access attempts.

Objective: Encrypt data using envelope encryption.

Solution: Follow the envelope encryption process.

Steps:

Step 1: Generate a Data Encryption Key (DEK) locally. The DEK is used to encrypt the actual data.

Step 2: Encrypt the data using the DEK.

Step 3: Use a Key Encryption Key (KEK) to wrap the DEK. The KEK is used to encrypt the DEK.

Step 4: Store the encrypted data and the wrapped DEK. This ensures that the data can be securely decrypted in the future using the KEK to unwrap the DEK.

Envelope encryption enhances security by adding an additional layer of encryption to the data encryption key, which is particularly useful for managing large volumes of encrypted data.

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.

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

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

Steps:

Step 1: Open the Google Cloud Console.

Step 2: Navigate to the VPC Service Controls page.

Step 3: Create a new service perimeter.

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

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

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

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

To enhance the security of your microservices architecture on Google Kubernetes Engine (GKE) and ensure that only approved container images are deployed, implementing Binary Authorization is a robust solution.​

Option A: Enforcing Binary Authorization in your GKE clusters ensures that only container images that meet your organization's security policies are deployed. By integrating container image vulnerability scanning into your Continuous Integration/Continuous Deployment (CI/CD) pipeline, you can assess images for known vulnerabilities before they are deployed. Binary Authorization can be configured to use these vulnerability scan results to make policy decisions, effectively preventing the deployment of insecure images. This approach leverages managed services provided by Google Cloud, ensuring scalability and compliance with security standards.​

Option B: Developing custom organization policies to restrict deployments to images within a specific Artifact Registry project helps in controlling the source of images but does not inherently assess the security posture of those images. Without integrated vulnerability scanning and enforcement mechanisms, this approach may not fully mitigate the risk of deploying vulnerable images.​

Option C: Building a system using third-party vulnerability databases and custom scripts requires significant maintenance and may not integrate seamlessly with GKE. This approach can be error-prone and lacks the efficiency of managed services designed for this purpose.​

Option D: Automatically deploying new images upon successful CI/CD builds ensures rapid deployment but does not address the need for security assessments of the images. While setting up firewall rules is good practice, it does not prevent the deployment of potentially vulnerable images.​

Therefore, Option A is the most effective approach, as it utilizes Google Cloud's managed services to enforce security policies and integrate vulnerability assessments directly into the deployment process, ensuring that only approved and secure container images are deployed to your GKE clusters.​

Use Cryptographic Verification If there is a risk of IAP being turned off or bypassed, your app can check to make sure the identity information it receives is valid. This uses a third web request header added by IAP, called X-Goog-IAP-JWT-Assertion. The value of the header is a cryptographically signed object that also contains the user identity data. Your application can verify the digital signature and use the data provided in this object to be certain that it was provided by IAP without alteration.