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New to Plaintext? This page is the 5-minute orientation: how the curriculum works, what order to take it in, and how to prove what you've learned. Read it once, then dive into Foundations.

What Plaintext is (and isn't)

Plaintext is a hands-on, job-ready security curriculum — free, open, and AI-native. It is not a pile of links and not a textbook you passively read. Every module follows the same shape:

  • The core idea — original prose that builds the mental model and the practitioner translation (the "bridge"). This is what we write; it's the part no single link gives you.
  • Learn — a curated, time-boxed path to the best existing explanations of the mechanics. We curate these rather than re-teach them.
  • The lab — a one-command environment where you do the thing and commit an artifact you own.

So the rhythm is: read the bridge → follow the Learn path for the details → do the lab → keep the artifact. The depth comes from the bridge + the lab + the automation you build, not from re-reading fundamentals.

What order to take it in

  • Everyone starts with Foundations (Track 00). It's the bedrock the rest assumes — networking, Linux, scripting, crypto, web, threat modeling.
  • Then pick a specialization. Tracks are standalone and depend only on Foundations, never on each other — so go where your goal points: offense (01), defense (02), forensics, malware, cloud, AD, hardening, crypto, Python, automation, ZTNA, or AI-augmented ops. Cross-references enrich but never gate.
  • Lean into the offense ↔ defense interplay. Many labs have a matched pair (attack here, detect it there). Doing both sides is where the understanding compounds.

Prerequisites

A computer that runs Docker (most labs are git clone + make up), comfort in a terminal, and a GitHub account (your committed artifacts are your portfolio). A few labs need a VM or a cloud free-tier account where the domain demands it (Active Directory, cloud) — each says so.

How the labs work

The curriculum prose lives in this repo; the runnable labs live in plaintext-labs. For a reference lab:

git clone https://github.com/plaintext-security/plaintext-labs
cd plaintext-labs/<track>/<module>
make up      # start the environment
make demo    # watch the worked example
make down    # stop it

Every lab ends in an Automate & own it step: turn the manual work into a small reusable script. The standing posture for AI is AI authors → you review → you own it — automation is assumed; the skill is directing and rigorously reviewing it. Never submit something you can't reproduce by hand.

How to prove what you learned

There's no paywalled exam — your portfolio is the credential:

  • Each lab's committed artifact (writeup, script, detection rule, report) is evidence.
  • Self-check your work against the lab's Success criteria and commit the deliverable — this is an honor system; no tool grades you, the proof is the committed work itself.
  • A track's capstone integrates the whole track into one portfolio-worthy build.

Get help

Stuck on a lab or a concept? Join the Discord — there's a help forum per track plus #labs-and-docker. Per-page comments (via GitHub Discussions) live at the bottom of each module page. New to a term? Check the Glossary.

Comments

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