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DASH NYC, June 9-10 | AI + Observability

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From Legacy to AI-Ops: Securing and Scaling Systems for 20M Device Requests with Datadog

About this Session

Modernizing a legacy system serving 20 million devices without users noticing is like replacing a jet engine mid-flight.

 

In this session, YoungJin Jung and Donggen Hong from LG U+ share their 18-month journey transforming a Telco-scale API Gateway from a rigid, proprietary solution into a high-performance, open-source architecture on AWS, and the operational challenges they solved along the way.

 

The first problem was visibility. Migrating to a hybrid environment spanning AWS, third-party providers, and on-premises infrastructure meant the team was flying blind. They needed end-to-end observability across provider boundaries to ensure stability at massive scale, and what started as a gap quickly became a core foundation supporting both operations and engineering.

 

With that foundation in place, the team turned to security and developer velocity. They adopted a shift-left approach, embedding static analysis and dependency scanning into CI/CD pipelines to catch vulnerabilities before production. They integrated AI to simplify alert generation, enabling developers to create and manage alerts for complex abnormal scenarios without slowing down delivery.

 

Finally, by correlating deployment history with CI/CD metrics, they established traceability between changes and system behavior, closing the loop between shipping code and understanding its impact.

 

This session will give you a practical roadmap for modernizing legacy systems at scale, with lessons learned on building security, observability, and automation into the migration from day one.

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