Comprehensive Cloud Infrastructure Lab

This project is a two-hour, end-to-end demonstration of building and securing a cloud infrastructure environment in Microsoft Azure — and it's the clearest signal in my portfolio that I think in systems, not silos.

The work involved architecting multiple virtual networks with distinct roles, orchestrating over a dozen Azure services (Sentinel, Log Analytics, Entra ID, Key Vault, Blob Storage, Defender for Cloud, Network Watcher), designing log ingestion pipelines that route data from VMs, network flows, and identity services into a centralized analytics workspace, and writing KQL queries to surface patterns in that data. I ran the environment through two controlled 24-hour exposure windows — once unsecured, once hardened — and measured the difference.

The security domain is the context, but the underlying skills are infrastructure and data engineering: standing up complex multi-service environments, designing pipelines that move data where it needs to go, writing queries to extract signal from noise, and iterating on system configuration based on measured outcomes.

These are the same capabilities that underpin AI/ML infrastructure — model serving pipelines, observability stacks, data ingestion workflows, and the orchestration layer that ties everything together. The ability to design, instrument, and reason about complex technical environments doesn't belong to cybersecurity. It belongs to anyone who builds serious systems. This is me building a serious system.