Google Cloud Certified Professional-Cloud-Security-Engineer Full Course Free

The requirement is to protect data while in use (meaning data in memory or CPU registers, during processing). This is a concept addressed by Confidential Computing using Trusted Execution Environments (TEEs).

Extracts:

"Confidential VMs are an IaaS solution... Confidential VMs offer: Encryption for 'data in use', including the processor state and the virtual machine's memory." (Source 4.1)

"Confidential computing protects data during processing by isolating workloads inside hardware-based trusted execution environments (TEEs), ensuring even cloud operators cannot access them." (Source 4.3)

"Confidential VMs extend the standard virtual machine concept by adding hardware-enforced confidentiality controls... they ensure data remains encrypted not only at rest and in transit, but also while in use." (Source 4.4)

Options A (CMEK) and B (CSEK) protect data at rest (disk encryption). Option D (Shielded VM) protects integrity and prevents rootkit compromise but does not encrypt memory while the data is actively being processed. Only Confidential VM (or TEE) protects data in use.

Scenarios for exporting Cloud Logging data: Splunk This scenario shows how to export selected logs from Cloud Logging to Pub/Sub for ingestion into Splunk. Splunk is a security information and event management (SIEM) solution that supports several ways of ingesting data, such as receiving streaming data out of Google Cloud through Splunk HTTP Event Collector (HEC) or by fetching data from Google Cloud APIs through Splunk Add-on for Google Cloud. Using the Pub/Sub to Splunk Dataflow template, you can natively forward logs and events from a Pub/Sub topic into Splunk HEC. If Splunk HEC is not available in your Splunk deployment, you can use the Add-on to collect the logs and events from the Pub/Sub topic.

The most effective way to address VM sprawl while enforcing consistent security baselines at the VM creation stage (VM lifecycle management) is through the use of immutable, hardened images built via an automated pipeline.

Centralized Image Management and Hardening: A Cloud Build pipeline is the standard way to automate the creation of "golden images." The pipeline can install OS/packages, apply hardening scripts (e.g., CIS benchmarks), run vulnerability scans, and then store only the verified, secure images in a central registry. This centralizes control over the security baseline.

Enforcement: Instance Templates are the mechanism to standardize VM deployment. By configuring the templates to only point to the central registry of approved, hardened images, you ensure that every new VM spun up automatically adheres to the security baseline. This prevents teams from deploying unhardened or insecure images, solving the "VM sprawl" and "consistent security hardening" problem at its source.

Option A (SCC Posture Management) is a detective control that monitors after the VM is deployed; it does not prevent unhardened VMs from being created, which is the goal of lifecycle management.

Option D (VM Manager) is excellent for ongoing patching and updating of existing VMs, but it doesn't solve the initial problem of ensuring a secure, centralized, hardened image is used for creation (which is where the baseline is enforced).

Extracts:

"Golden images that are configured and used to create servers play a key role in allowing companies to scale securely." (Source 1.2)

"Using an automated tool eradicates this issue. When engineers use images produced by [automated tools], the evidence is clear, as everything needed is pre-baked into the image." (Source 1.2)

"An instance template is a convenient way to save a virtual machine (VM) instance's configuration that includes machine type, boot disk image... You can use an instance template to... Create individual VMs." (Source 3.3)

The overall strategy described in Option B—automate hardening, scan, store, and enforce usage via templates—is the best practice for secure and compliant VM deployment at scale.

The core security and privacy requirement is to prevent personal data from being used in the training process, which necessitates de-identification. Cloud Data Loss Prevention (DLP), also referred to as Sensitive Data Protection (SDP), is the specific Google Cloud tool for this purpose. The secondary requirement, restricting access, is handled by IAM.

Extracts:

"Sensitive Data Protection (SDP)... De-identification enables you to transform your data to reduce data risk while retaining data utility." (Source 1.4)

"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." (Source 1.1)

"DLP provides tools to classify and de-identify sensitive elements or unwanted content within your data... Find and remove sensitive elements from your data before model training." (Source 1.4)

IAM policies are the standard mechanism to satisfy the requirement to "restrict access to the dataset to an authorized subset of people only." Option B combines the precise technical solution for privacy (DLP De-identification) with the necessary access control (IAM).