How we run a security audit from the traffic you already captured
Inside our three-phase API security audit: analyze captured traffic, optionally verify live, then track findings by category, severity, and lifecycle.
Every time Validate.QA explores an application, the browser records the network traffic that flows underneath the UI: the API calls a page fires, the responses they return, the headers, the auth state, and the test phase each request belonged to. That captured traffic is already sitting there after a discovery run. The engineering question we wanted to answer was simple: could we turn traffic we already have into a useful, repeatable security signal without asking anyone to run a separate scan?
The answer is a focused three-phase pipeline. It analyzes the captured traffic, optionally confirms the most interesting findings against the live application, and then persists them as tracked records that open, update, and resolve across runs. It is deliberately not a replacement for a manual penetration test. It is a fast, always-on second pass over evidence the platform collects anyway, and it complements the deeper, human-driven work rather than standing in for it.
Phase 1: Analyze The Traffic You Already Captured
The first phase reasons over the captured traffic, grouped by the page where each request was observed. Grouping by page matters because context changes everything in security analysis. An authentication call on a sign-in page is completely normal. The same call appearing on a public marketing page is suspicious. By keeping the page association intact, the analysis can judge each request against what the page it lives on is actually supposed to do.
Before analysis, near-identical observations of the same endpoint are collapsed into one representative entry with an occurrence count. This is partly about efficiency, but mostly about clarity: the analysis sees one clear picture of how an endpoint behaves rather than dozens of scattered repeats. The output of this phase is a set of candidate findings, each tagged with a category, a severity, the endpoint and page involved, and a short evidence summary.
The Category Families
Findings fall into roughly ten high-level categories that describe the kind of problem rather than any specific exploit. At a high level they cluster into a few families:
Topics: Engineering, Deep Dive, Architecture, Security.
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