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Project case study · reviewed source

SurfaceLens

A local-first attack-surface workbench for turning common recon output into explainable priorities.

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Role
Designer and developer
Runtime
Python
License
MIT
Reviewed revision
fe7269ce87c4

Problem to outcome · 01

How the problem shaped the design and implementation.

01

Challenge

Reconnaissance tools produce useful but disconnected files. The analyst still has to normalize hosts, explain why one target matters more than another, compare runs, and prepare evidence for review.

02

Design decision

Keep the workflow local and make prioritization inspectable. SurfaceLens stores normalized observations, ranking reasons, snapshot drift, and analyst notes in one workspace instead of hiding the decision inside a score.

03

Contribution

Designed and built the normalization, explainable ranking, snapshot comparison, annotation, and multi-format export flow.

04

Outcome

The MVP turns common reconnaissance output into a repeatable analyst review surface with four export formats and a visible reasoning trail.

Implementation trace · 02

The implementation follows four reviewable stages.

  1. Ingest

    Read common reconnaissance outputs without sending them to a hosted service.

  2. Normalize

    Bring hosts, services, and observations into one inspectable model.

  3. Prioritize

    Rank leads with visible heuristics that an analyst can challenge.

  4. Export

    Carry annotations and reasoning into HTML, Markdown, JSON, and CSV.

Proof and boundaries · 03

Verified behavior and known limits.

Verified in the reviewed source

What the project demonstrates

  • The reviewed source contains four unit tests.
  • Exports are available in HTML, Markdown, JSON, and CSV.
  • Snapshot comparison and analyst annotations remain part of the local workflow.
Operating boundary

What the project does not claim

  • There is no public CI workflow or formal release.
  • The MVP is not presented as production-ready for untrusted input.
  • Ranking is decision support; an analyst still validates every target and conclusion.

CyberKareem/surfacelensThis page references commit fe7269ce87c4460a7c0aef5d7209385584955a40. The repository may move after that point; the pinned revision keeps this review reproducible.

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