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Learning Roadmap

How to Become a AI Purple Team Specialist

A step-by-step, phase-based learning path from beginner to job-ready AI Purple Team Specialist. Estimated completion: 10 months across 5 phases.

5 Phases
42 Weeks Total
High Entry Barrier
Advanced Difficulty
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  1. Foundations: Cybersecurity + Machine Learning Basics

    8 weeks
    Goals
    • Understand core cybersecurity concepts (CIA triad, OWASP Top 10, threat modeling)
    • Learn Python programming with focus on scripting, APIs, and data manipulation
    • Grasp fundamental ML concepts: supervised learning, neural networks, overfitting, and evaluation metrics
    Resources
    • Google Cybersecurity Professional Certificate (Coursera)
    • fast.ai Practical Deep Learning for Coders
    • OWASP Top 10 for LLM Applications (official documentation)
    • Python Crash Course by Eric Matthes
    Milestone

    You can articulate how traditional cybersecurity threats map onto ML systems and write basic Python scripts for data processing.

  2. Adversarial ML & LLM Security Fundamentals

    10 weeks
    Goals
    • Study adversarial ML attack taxonomy: evasion, poisoning, model extraction, model inversion
    • Master prompt injection types (direct, indirect, system prompt leakage) and jailbreak techniques
    • Get hands-on with LLM red-teaming tools: Garak, PyRIT, Promptfoo
    Resources
    • MITRE ATLAS (Adversarial Threat Landscape for AI Systems) website and case studies
    • Microsoft PyRIT GitHub repository and tutorials
    • NVIDIA Garak documentation and walkthrough
    • Prompt Injection Attacks on LLMs - OWASP guide
    • Adversarial Machine Learning by Goodfellow, Papernot et al. (papers)
    Milestone

    You can independently conduct a structured red-team assessment of an LLM API endpoint and document findings.

  3. Blue-Team Defenses & Secure ML Pipelines

    10 weeks
    Goals
    • Learn to build input/output guardrails using commercial and open-source tools
    • Understand secure MLOps: data provenance, model signing, inference monitoring, access control
    • Implement adversarial robustness techniques: adversarial training, certified defenses, content classifiers
    Resources
    • AWS Bedrock Guardrails documentation
    • Lakera Guard and Robust Intelligence product docs
    • Protect AI MLSecOps community resources
    • Certified Adversarial Robustness (Cohen et al.) - selected papers
    • MLflow + GitHub Actions for secure deployment pipelines
    Milestone

    You can design a secure ML deployment pipeline with integrated guardrails and automated adversarial regression tests.

  4. Purple Team Operations & Threat Intelligence

    8 weeks
    Goals
    • Design and execute end-to-end purple-team exercises combining attack and defense
    • Build a continuous adversarial evaluation framework integrated into CI/CD
    • Develop executive communication skills for AI risk reporting
    Resources
    • NIST AI Risk Management Framework (AI RMF 1.0)
    • MITRE ATLAS Navigator for attack path visualization
    • Real-world case studies: Samsung ChatGPT data leak, Bing Chat jailbreaks, adversarial attacks on autonomous vehicles
    • Technical writing courses (Google Technical Writing)
    Milestone

    You can lead a full purple-team engagement end-to-end, from threat modeling to attack execution to defense implementation and executive reporting.

  5. Specialization & Industry Authority

    6 weeks
    Goals
    • Choose a vertical specialization (financial AI, healthcare AI, autonomous systems, government/defense)
    • Contribute to open-source AI security tools or publish research
    • Build a portfolio of red-team reports and secure pipeline architectures
    Resources
    • Industry-specific compliance frameworks (HIPAA for healthcare, PCI DSS for finance)
    • Conference submissions: DEF CON AI Village, Black Hat, IEEE S&P, NeurIPS Safety workshops
    • Open-source contributions to Garak, PyRIT, or Promptfoo
    Milestone

    You are recognized as a subject-matter expert in AI purple teaming with published work, a strong portfolio, and readiness for senior or lead roles.

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

LLM Jailbreak Catalog & Automated Scanner

Intermediate

Build a comprehensive catalog of known jailbreak techniques (DAN, role-play, multi-turn escalation, encoding bypasses) and an automated scanner that tests any LLM endpoint against all techniques, generating a vulnerability heatmap.

~30h
Prompt injection techniquesPython scriptingAPI testing

RAG Security Auditor

Advanced

Create a tool that audits RAG pipelines for indirect prompt injection, document-level access control failures, and context leakage by injecting adversarial content into the retrieval corpus and measuring model behavior changes.

~45h
RAG architecture understandingIndirect prompt injectionData poisoning simulation

Purple Team CI/CD Security Pipeline

Intermediate

Design and implement a GitHub Actions pipeline that automatically runs adversarial test suites against an LLM application on every deployment, with configurable pass/fail thresholds and Slack alerting.

~25h
CI/CD integrationPromptfoo configurationAutomated testing

AI Agent Red-Team Playground

Advanced

Build a sandboxed LangChain agent environment with multiple tools (code execution, web browsing, file system access) and create a red-team framework that tests for unauthorized tool invocation, privilege escalation, and data exfiltration chains.

~50h
Agent securityTool-calling attack vectorsLangChain internals

Adversarial Image Attack Toolkit for Vision Models

Advanced

Implement FGSM, PGD, and C&W attacks against common vision classifiers, then develop a user-friendly toolkit that allows non-experts to test their image classification models for adversarial robustness.

~40h
Adversarial ML techniquesComputer vision securityIBM ART usage

MITRE ATLAS Threat Model Mapper for LLM Applications

Beginner

Create a web application that guides users through threat modeling an LLM application by mapping features and architecture components to MITRE ATLAS techniques, producing a visual threat map and prioritized mitigation list.

~20h
Threat modelingMITRE ATLAS frameworkWeb development basics

Prompt Injection Honeypot

Intermediate

Deploy a deliberately vulnerable LLM chatbot as a honeypot, instrument it with logging, and build an analytics dashboard that captures, categorizes, and visualizes real-world prompt injection attempts from external attackers.

~35h
Honeypot designLog analysisAttack pattern recognition

LLM Safety Evaluation Benchmark Suite

Intermediate

Build a modular benchmark that evaluates LLM safety across categories (harmful content, PII leakage, bias, jailbreak resistance) using standardized test cases, scoring rubrics, and comparison dashboards across multiple models.

~35h
Evaluation methodologyBenchmark designStatistical analysis

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