Case Study

Raposa

Reframing cloud security visibility as an AI-assisted analysis problem, with architecture built around AWS, retrieval workflows, and operator trust.

ProblemCloud security teams had too much raw signal and not enough usable analysis.

SolutionAn AI-driven analysis platform designed to surface risk patterns from infrastructure data.

StackAWS, LLM workflows, retrieval patterns, structured security data, production architecture thinking.

OutcomeA clearer path toward explainable security analysis rather than another opaque alert stream.

The Problem

Security visibility is rarely limited by the absence of data. The real problem is that teams collect telemetry faster than they can interpret it. That creates a familiar trap: lots of dashboards, weak prioritization, and very little confidence about what matters.

The Architecture Thinking

The interesting design question was not simply where to place an LLM. It was how to build a system that could retrieve the right infrastructure context, preserve traceability, and present findings in a way a security operator could trust. That pushed the architecture toward structured data flows, retrieval layers, explicit context boundaries, and a bias toward explainability over novelty.

Why It Matters

A lot of AI security products fail because they look impressive in a demo but collapse when a team asks basic questions about provenance, false positives, or operating cost. The value here was treating cloud security analysis as an operational workflow, not a prompt wrapper.