Phase 9: Career & Portfolio · 55 min · Python · Markdown · Git

Building an AI Security Portfolio

A certificate says you sat through it. A red team report says you can do it.

Hiring signal: Hiring managers for AI Security Engineer and AI Red Team Engineer roles say the same thing in almost every job posting: show us artifacts, not credentials. A candidate who can hand over a redacted threat model, a red team report with reproduction steps, and a working guardrail implementation gets a callback before a candidate with only a certificate.

What you will learn

Select which of your Phase 2, 4, 6, and 8 project artifacts to feature and in what order
Rewrite a lab report as a case study a hiring manager can read in under three minutes
Apply a redaction checklist so a real engagement writeup is safe to publish
Generate a structured portfolio index from a directory of your own project artifacts

The Problem

You have spent eight phases building real artifacts: a multi-layer prompt injection detector, a full agent security audit with a threat model, a red team engagement report against a RAG system using Garak and PyRIT, and a production security architecture with a monitoring dashboard and an incident response playbook. Almost every AI security candidate applying for the same roles has a resume that says "familiar with prompt injection" and a LinkedIn certificate badge. Almost none of them have a document a hiring manager can actually read.

That gap is the opportunity. AI security is young enough that there is no accepted credential that proves competence the way a CPA license proves accounting competence. Hiring managers know this, so they fall back on the one signal they trust: work product. A threat model you wrote for a real (or realistic) multi-agent system tells them more in five minutes than a resume tells them in five minutes plus a 30-minute phone screen. This is the same reason security consultancies hire based on sample deliverables, not just interviews — the deliverable is the job.

The mistake most candidates make is treating their course projects as homework to check off, then writing a generic "completed AI security training" line on their resume. The fix is to treat every project you built in Phases 2, 4, 6, and 8 as a work sample from day one — which means writing it the way you'd write it for a client, not a grader.

Redact, don't delete
The instinct when preparing a real engagement writeup for public consumption is to delete anything sensitive. Don't delete — redact and generalize. "NimbusDesk Support Platform" becomes "a Series B fintech support platform." A real CVSS score, attack chain, and remediation timeline stay intact. The value of the case study is the methodology and the result; the identifying details are the only thing you strip out.

What Makes an Artifact a Portfolio Piece, Not Homework

Four things separate a case study a hiring manager will read from one they'll skim past:

Homework version	Portfolio version
"Implemented a prompt injection detector as part of Phase 2"	"Built a 3-layer detector achieving 94% detection rate at 3% false-positive rate against a 240-example adversarial test set, cutting successful bypasses from 17/20 known techniques to 2/20"
Full code dump, no narrative	Executive summary first, then methodology, then the code as supporting evidence
"Found some vulnerabilities in a sample agent"	Severity-ranked findings with CVSS-style scoring, reproduction steps, and a specific remediation for each
Screenshot of a passing test suite	A before/after re-test table showing the exact attack chain that used to succeed and now doesn't

Your four course projects map directly onto the four artifact types AI security hiring managers ask for by name in job postings and take-home exercises:

Phase 2 — Injection Detector + Jailbreak Library → a working detector repo plus a documented library of 20+ jailbreak techniques with measured bypass rates. This is the artifact that answers "show me you understand OWASP LLM01 beyond the definition."
Phase 4 — Agent Security Audit → a threat model plus a severity-ranked vulnerability report with implemented fixes. This is the artifact that answers the "review this agent codebase" interview test almost every AI security team runs.
Phase 6 — Full Red Team Engagement Report → a Garak + PyRIT engagement with methodology, findings, attack chains, and re-test results. This is the closest thing to a real consulting deliverable you can put in front of a hiring manager.
Phase 8 — Production AI Security System → an end-to-end architecture with a monitoring dashboard, CI/CD security gates, and an incident response playbook. This is the artifact that proves you can operate security, not just find vulnerabilities once.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Structuring a Case Study, What to Redact vs. What to Show, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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