Pass the Google Cloud Certified Professional-Cloud-Security-Engineer Questions and answers with ExamsMirror

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

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

Objective: Ensure VMs can access Cloud Storage without reaching the public internet.

Solution: Enable Private Google Access on the VPC network, allowing VMs with only internal IP addresses to access Google APIs and services privately.

Steps:

Step 1: Open the Google Cloud Console.

Step 2: Go to the VPC Network section.

Step 3: Select the relevant VPC network and subnet.

Step 4: Enable Private Google Access for the subnet.

Private Google Access ensures that instances can access Google APIs and services (such as Cloud Storage) over a private network connection, without requiring a public IP address.

To mitigate the risk posed by long-running Google Cloud CLI sessions, it is essential to enforce a reauthentication frequency. This ensures that users must periodically reauthenticate, reducing the window of opportunity for an attacker to exploit an open session. Setting the reauthentication frequency to one hour forces users to reauthenticate after this period, thereby limiting the duration an attacker can use a compromised session.

Access Google Cloud Console: Log in to your Google Cloud Console using your admin credentials.

Navigate to Security Settings: Go to the "Security" section of the Cloud Console.

Set Session Control: Under the session management settings, locate the "Reauthentication frequency" setting. This controls how often users must reauthenticate.

Configure Reauthentication Frequency: Set the reauthentication frequency to "1 hour". This configuration will force users to reauthenticate every hour, thus limiting the duration of each session.

Save Changes: Confirm and save your changes. This setting will now apply to all users, ensuring that open sessions are minimized to a duration of one hour.

SSO/SAML Integration: Implement SSO (Single Sign-On) with SAML integration through Cloud Identity to streamline user authentication and lifecycle management. This ensures centralized management of user identities and access.

Predefined Roles: Use predefined roles to provide granular access control. These roles are designed to follow the principle of least privilege, ensuring that users have the minimum necessary permissions to perform their tasks.

User Management: By leveraging SSO/SAML, user provisioning and de-provisioning become more efficient and secure. This integration helps maintain consistent access policies across your organization.

Access Control: Predefined roles reduce the risk of over-permission by offering well-defined access levels, enhancing security and compliance. References:

Google Cloud - SSO with SAML

Google Cloud - IAM Best Practices

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

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

nodeSelector is the simplest recommended form of node selection constraint. You can add the nodeSelector field to your Pod specification and specify the node labels you want the target node to have. Kubernetes only schedules the Pod onto nodes that have each of the labels you specify. => https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector Tolerations are applied to pods. Tolerations allow the scheduler to schedule pods with matching taints. Tolerations allow scheduling but don 't guarantee scheduling: the scheduler also evaluates other parameters as part of its function. => https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/

Objective: Optimize the usage of Cloud Data Loss Prevention (DLP) API to reduce costs.

Solution:

rowsLimit and bytesLimitPerFile: These parameters help in sampling data instead of scanning the entire dataset, thereby reducing the amount of data processed.

CloudStorageRegexFileSet: This feature allows you to specify a subset of files to be scanned using regular expressions, limiting the scope and volume of data scanned.

Steps:

Step 1: Set appropriate rowsLimit values for BigQuery data scans to sample rows instead of scanning entire tables.

Step 2: Set bytesLimitPerFile values for Cloud Storage buckets to limit the number of bytes scanned per file.

Step 3: Use CloudStorageRegexFileSet to specify the subset of files to be scanned based on patterns that match the filenames.

By combining these strategies, you effectively reduce the scope and volume of data processed by the DLP API, leading to cost savings.

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

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

https://cloud.google.com/bigquery/docs/scan-with-dlp

Cloud Data Loss Prevention API allows to detect and redact or remove sensitive data before the comments or reviews are published. Cloud DLP will read information from BigQuery, Cloud Storage or Datastore and scan it for sensitive data.