Cox Automotive: Modernizing Security Logging Analytics for an Auto Leader

Cox Automotive standardizes cloud security analytics across 16 subsidiaries, saving $700k

Using log analytics on AWS data lake to exponentially improve operations across business units and help standardize security among subsidiaries amid move to the cloud  

Becoming The Digital Thread Between Auto Industry Stakeholders

Cox Automotive is transforming the way the world buys, sells, owns, and uses vehicles. Working with more than 40,000 auto dealer clients across five continents, the company has created a suite of digital tools that help bridge the gap between consumers, manufacturers, dealers, and lenders at every stage of the automotive experience. Cox Automotive is a proven innovator that makes it possible for clients to compete and modernize their business in the face of ever-increasing consumer demand for more seamless offline-to-online processes. As an industry leader, when a vast digital market expansion ensued, Cox Automotive made the decision to move its products to the cloud from 40+ data centers. Their modernized cloud architecture and organic business expansion resulted in exponential data growth, making real-time operations analytics imperative. Already utilizing Amazon AWS to onboard products quickly, Cox Automotive turned to Upsolver and Amazon Athena for analyzing operations in real-time and at scale.

Improving Operations and Standardizing Security

Data captured by the cloud became a major information hub for analyzing and monitoring applications across organizations. The operations teams use metrics to understand cloud consumption and costs. Security teams rely on the data to detect threats in real-time, calculate network traffic statistics, troubleshoot connectivity issues, and more. New cloud environments also allow Cox Automotive to onboard applications and data even faster than before, though the increasing data volume is not without its challenges:

• Exponential cost with rapid business and data growth: ingesting and storing data in its raw form is difficult due to high costs.
• Scalability with business growth: onboarding new applications is a long process that requires a dedicated team with an experienced coding background.
• Business agility: retention of data long-term is required to build machine learning models or replay data from its raw form when needed.

Using log analytics on AWS data lake to save millions

Upsolver and Amazon Athena are being used together to deliver an end-to-end solution for streaming data analytics that is quick to set up and easy to operate without coding. Upsolver  processes the raw data  to gain powerful visibility into the cloud operations to ensure application uptime and stability while minimizing costs by only ingesting relevant data into Splunk. The architecture also scales elastically using cloud-native computing and storage without manual maintenance. Cox Automotive did just that. Rather than spending hundreds of thousands of dollars to send their raw VPC flow log data directly to a log analytics engine, the company moved to a cloud data lake architecture to store all log data in Amazon S3, query it with Amazon Athena using SQL, and push relevant data into Splunk when it’s needed to do a deeper dive into a security anomaly.