Google Updated Professional-Cloud-Security-Engineer Exam Questions and Answers by mabli

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.

De-identifying sensitive data Cloud Data Loss Prevention (DLP) can de-identify sensitive data in text content, including text stored in container structures such as tables. De-identification is the process of removing identifying information from data. The API detects sensitive data such as personally identifiable information (PII), and then uses a de-identification transformation to mask, delete, or otherwise obscure the data. For example, de-identification techniques can include any of the following: Masking sensitive data by partially or fully replacing characters with a symbol, such as an asterisk (*) or hash (#). Replacing each instance of sensitive data with a token, or surrogate, string. Encrypting and replacing sensitive data using a randomly generated or pre-determined key. When you de-identify data using the CryptoReplaceFfxFpeConfig or CryptoDeterministicConfig infoType transformations, you can re-identify that data, as long as you have the CryptoKey used to originally de-identify the data. https://cloud.google.com/dlp/docs/deidentify-sensitive-data

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.

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

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

Extracts:

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

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

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

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