ScanKit

Static analysis that inventories every external input to your application, ranks the risks, generates tests, and feeds AI-assisted review.

One managed, explainable dataset for your attack surface

ScanKit is a static-analysis pipeline that scans your application code, inventories every external input, ranks the risks, generates tests for them, and feeds AI-assisted review. Request parameters, headers, cookies, webhooks, environment variables, form fields, and datastore reads all land in a single correlated report - so your team gets one ranked worklist instead of a wall of equally-weighted alerts.

Why ScanKit matters

Security review only works when every number is defensible. ScanKit attaches transparent reasoning to each finding, cross-checks inputs across independent scanners, and confirms risk against a live system - so a "critical" score is demonstrated, not estimated.

What you get

Previews of the reports ScanKit produces - real screenshots to follow

Ranked Inventory Report - every external input, scored and reasoned
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A single correlated report of every external input, ranked by priority.

Version Diff View - risks introduced, removed, or modified
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See exactly what a patch changed in your attack surface.

AI Review Pane - sourced fix guidance per finding
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Focused explanations of why a path is risky and how to fix it.

How it works

A seven-stage pipeline from raw code to a defensible, version-tracked report

1

Scan the latest version of your app

Run a full external-input scan against the current codebase to build a complete inventory of every entry point: request parameters, headers, cookies, webhooks, environment variables, form fields, and datastore reads.

Result: a complete map of every way data enters your application.
2

Inventory external risks, ranked by priority

Every input lands in a single correlated report, tagged with a transparent priority score, risk level, and aggravating factors - such as data reaching the database - with the reasoning attached to each item.

Result: a single ranked worklist where every number is defensible to anyone who asks why.
3

Backed by multiple analyses

Independent scanners cross-check every input, and each finding is tagged with how many confirmed it across multiple reports.

Result: focus effort on high-confidence risks and defend why something ranked where it did, instead of weighing every alert equally.
4

Generate tests and connect to your tools

Each finding becomes an executable test, with web routes resolved from your code, that plugs into your existing security pipeline.

Result: verify risk against a live target and keep findings within the pipeline you already run.
5

Confirm risk against a live system

Run the generated tests against a running target and let confirmed-exploitable results raise the finding's priority.

Result: prioritize remediation backed by proof - a "critical" score means demonstrated, not estimated.
6

Review risky code with AI, guided by explanations

Each finding is a normalized record that carries its code context and risk tags, so you can hand a specific risky path to an AI reviewer and get a focused explanation of why it's dangerous and how to fix it.

Result: steer every code review with concrete, sourced reasoning instead of guessing where to look.
7

Compare two versions to see what a patch changed

Scan two versions of the app and diff the reports to see exactly which external risks were introduced, removed, or modified by a patch.

Result: treat every release as a measurable delta in your attack surface, with a clear answer to what new inputs it introduced and whether they are safe.

Built for defensible security review

Every feature exists to make a finding explainable, verifiable, or measurable

Transparent priority scores

Each input is tagged with a priority score, a risk level, and aggravating factors - with the reasoning attached. No black-box ranking.

Multi-scanner confidence

Independent analyses cross-check every input, and findings are tagged by how many scanners confirmed them, so you weight effort by confidence.

Tests from your code

Findings become executable tests with web routes resolved straight from the codebase, ready to run against a live target.

Confirmed, not estimated

Live-system confirmation lets demonstrated-exploitable findings raise their own priority - proof backs every critical score.

AI review with sources

Hand an AI reviewer a specific risky path and get focused, sourced guidance on why it's dangerous and how to fix it.

Release-as-a-delta

Diff two scans of your app to see exactly which external risks a patch introduced, removed, or modified.

Turn your attack surface into one managed dataset

See ScanKit rank your application's external inputs into a single, defensible, version-tracked report. Contact us to run it against your codebase.

Request a Scan