How great would it be to have a dashboard with a holistic view of threats, malicious server activity, vulnerabilities, sensitive data access levels and a daily scan of resources across all of your applications and services? Now you can.

Cloudflare is thrilled to announce its integration with Cloud Security Command Center (Cloud SCC) for Google Cloud Platform: A security and data risk platform helping enterprises gather data, identify threats, and act on them before they result in business damage or loss.

The advantage of the Cloud SCC solution is that it surfaces insights from both the Google Cloud Platform, as well as Cloudflare’s edge, in a unified dashboard.

Through Cloudflare’s API endpoints, data is pushed to Google’s Cloud SCC dashboard and domain name information mapped to the appropriate Google Cloud asset. Cloudflare’s branded card in the Cloud SCC dashboard is automatically populated with a summary of top theat origins, top types of threats, and latest Web Application Firewall (WAF) events.

To view a full list of Cloudflare events, click on the Cloudflare card in Cloud SCC and it will take you to a “Cloudflare Findings” page. From there, you can select a time period of visible events. The “Findings” table shows what type of event, when it happened, and which asset (website, application, or API) was involved.

Every Cloudflare finding offers detailed information, including: country source, IP source of original request, requested Hostname, requested URI, User Agent, protocol type, method (GET/POST), action taken, rule triggered, and more.

Cloudflare’s findings in the Cloud SCC dashboard highlight which requests were blocked or challenged, and why. To take action on this data, you’ll need to login to your Cloudflare dashboard (link available directly from within Cloud SCC) or configure changes through the Cloudflare API.

One of the great things about container technology is that it delivers the same experience and functionality across different platforms. This frees you as a developer from having to rewrite or update your application to deploy it on a new cloud provider—or lets you run it across multiple cloud providers. With a containerized application running on multiple clouds, you can avoid lock-in, run your application on the cloud for which it’s best suited, and lower your overall costs.

If you’re using Kubernetes, you probably manage traffic to clusters and services across multiple nodes using internal load-balancing services, which is the most common and practical approach. But if you’re running an application on multiple clouds, it can be hard to distribute traffic intelligently among them. In this blog post, we show you how to use Cloudflare Load Balancer in conjunction with Kubernetes so you can start to achieve the benefits of a multi-cloud configuration.

To continue reading follow the Google Cloud blog here or if you are ready to get started we created a guide on how to deploy an application using Kubernetes on GCP and AWS along with our Cloudflare Load Balancer.

Cloudflare logs provide real time insight into traffic, malicious activity, attack incidents, and infrastructure health checks. The output is used to help customers adjust their settings, manage costs and resources, and plan for expansion.

Working with Google, we created an end-to-end solution that allows customers to retrieve Cloudflare access logs, store and process data in a simple way. GCP components such as Google Storage, Cloud Function, BigQuery and Data Studio come together to make this possible.

One of the biggest challenges of data analysis is to store and process large volume of data within a short time period while avoiding high costs. GCP Storage and BigQuery easily address these challenges.

Cloudflare customers can decide if they wish to obtain and process data from Cloudflare access logs on demand or on a regular basis. The full solution is described in this Knowledge Base article. Initial setup takes no more than 30 minutes to an hour. Moreover, customers can still replace any part of the process with their own tool or solution.

Below is a simple visualization of the data flow:

Cloudflare logs are obtained via a REST API. Usually this service can be run on your local workstation or Virtual Machine. The illustrated solution uses GCP Compute micro-instance.

For storing and managing log files we used GCP Storage bucket. All logs are stored in JSON format. Google Cloud Storage allows you to adjust the storage capacity when needed and set the retention policy.

Analyzing large data sets can be challenging. Google BigQuery makes it straightforward. When there is a new log file uploaded to the GCP Storage bucket, GCP Cloud Function triggers the process to import data from the new log file into BigQuery. BigQuery allows you to access your data almost immediately by running a simple query. As illustrated below you can, for example, pull top requested URIs with status code 404.

Based on feedback from our customers about which data they are interested in, we used GCP Data Studio to create visual reports. Data Studio Cloudflare logs analysis template can be found here. The following reports can be created in Data Studio using BigQuery as an input: top client IP address requests, requests by URL, error types, cached or uncached URLs, top triggered WAF rules, traffic types by device or location and many more.

Google Cloud is offering a $500 credit towards a new Google Cloud account to help you get started. In order to receive a credit, please follow these instructions.

Costs depend on several factors including the number of requests, storage, retention policy and number of queries in BigQuery, among others. For more pricing details, please use the GCP Pricing Calculator.

Please reach out to your Cloudflare Enterprise Solution Engineer or Customer Success Manager for more information.