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

Admin activity logs are always created to log entries for API calls or other actions that modify the configuration or metadata of resources. For example, these logs record when users create VM instances or change Identity and Access Management permissions.

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.

Google Cloud Logging Documentation

Audit Logs Overview

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.

Distroless Images

Best Practices for Building Containers

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