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

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.

To provide temporary write access to a Cloud Storage bucket with the minimum permissions necessary, you should:

Identify the Compute Engine instance’s default service account: Each Compute Engine instance has a default service account that is used to interact with other Google Cloud services.

Assign the storage.objectCreator role: This predefined IAM role grants permissions to create objects in a Cloud Storage bucket, which is sufficient for temporary write access. It does not grant permissions to read or delete objects, thus adhering to the principle of least privilege.

Avoid using full permissions or long-lived keys: Options A and C suggest using broader permissions than necessary or embedding long-lived keys, which could pose a security risk if compromised.

Service account impersonation (Option D) is not necessary for this task and would be more appropriate for scenarios where you need to assume a different identity with different 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.​

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.

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.​

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 each team's service account has the necessary permissions to manage resources within their assigned folders while adhering to the principle of least privilege, the following considerations apply:​

Folder Administrator Role: Granting each service account the Folder Administrator role on its respective folder provides comprehensive permissions to manage resources within that folder, including creating, updating, and deleting projects and resources. This approach ensures that teams have the necessary control over their environments without extending permissions beyond their assigned scope.​

Principle of Least Privilege: By assigning permissions at the folder level, you limit the service account's access to only the resources within its designated folder, aligning with the principle of least privilege and reducing the risk of unauthorized access to other parts of the organization.​

Therefore, Option A is the most effective approach, as it provides the necessary permissions for teams to manage their resources within their assigned folders while adhering to security best practices.​

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.​

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.