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Learning Roadmap

How to Become a AI DevSecOps Specialist

A step-by-step, phase-based learning path from beginner to job-ready AI DevSecOps Specialist. Estimated completion: 10 months across 4 phases.

4 Phases
40 Weeks Total
High Entry Barrier
Advanced Difficulty
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  1. Foundations: DevOps, Security & Core ML

    8 weeks
    Goals
    • Solidify understanding of CI/CD principles and common pipeline tools (GitHub Actions, GitLab CI).
    • Learn core cybersecurity concepts (CIA triad, common vulnerabilities like OWASP Top 10).
    • Gain fundamental knowledge of machine learning lifecycle and basic model deployment.
    Resources
    • KodeKloud DevOps Fundamentals course
    • PortSwigger Web Security Academy
    • Andrew Ng's 'Machine Learning Specialization' on Coursera
    • Docker and Kubernetes official documentation tutorials
    Milestone

    You can set up a basic, automated ML pipeline and identify common web application vulnerabilities.

  2. Specialization: AI/ML Security Concepts

    10 weeks
    Goals
    • Study AI-specific threat models: prompt injection, data poisoning, model evasion, model theft.
    • Learn about major AI security frameworks (NIST AI RMF, MITRE ATLAS).
    • Understand security implications of different model architectures (LLMs, CNNs).
    Resources
    • OWASP Top 10 for LLM Applications
    • NIST AI Risk Management Framework documentation
    • MITRE ATLAS (Adversarial Threat Landscape for AI Systems) knowledge base
    • Research papers on adversarial attacks (e.g., 'Explaining and Harnessing Adversarial Examples')
    Milestone

    You can perform a basic threat model for an LLM-powered chatbot application.

  3. Implementation: Secure AI Pipelines & Tooling

    12 weeks
    Goals
    • Practice integrating security scanning tools (Snyk, Trivy) into ML containerization workflows.
    • Implement basic guardrails using OpenAI's Moderation API or Hugging Face's safety models.
    • Design and deploy a secure, observable inference endpoint using Terraform and monitoring stacks.
    Resources
    • Hands-on labs with Snyk Container and IaC scanning
    • AWS/Azure AI security documentation
    • Building a project with LangChain and incorporating safety checks
    • Terraform tutorials for cloud security infrastructure
    Milestone

    You can deploy an LLM application with integrated security scanning, content filtering, and runtime monitoring.

  4. Mastery: Advanced Threats & Leadership

    10 weeks
    Goals
    • Conduct advanced red teaming exercises on AI systems.
    • Develop custom security tooling or scripts for novel AI threats.
    • Master compliance documentation and create secure AI operational frameworks for teams.
    Resources
    • Certified Ethical Hacker (CEH) or Offensive Security Certified Professional (OSCP) materials for mindset
    • Contributing to open-source AI security tools
    • Case studies on AI security incidents and responses
    • Leadership and technical writing courses
    Milestone

    You can lead a red team assessment, author an AI security policy, and mentor engineers on secure AI practices.

Practice Projects

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

Secure LLM Chatbot Pipeline

Intermediate

Build an end-to-end chatbot using LangChain that integrates input guardrails (toxicity filter), output verification, and all infrastructure deployed via Terraform with security scanning in GitHub Actions.

~40h
Secure Pipeline DesignGuardrail ImplementationIaC Security

Adversarial ML Toolkit for Image Classifiers

Advanced

Develop a Python toolkit that can generate adversarial examples (e.g., FGSM, PGD) against a simple CNN model and implement defensive techniques like input preprocessing or adversarial training.

~60h
Adversarial ML Attack/DefenseThreat Modeling for AI

AI Security Monitoring Dashboard

Intermediate

Create a Grafana dashboard that aggregates metrics from an AI service: request latency, input/output toxicity scores (from a classifier), model drift metrics, and authentication failures, with alerting.

~35h
Runtime Security MonitoringAI Data Security

Container Security for Model Serving

Beginner

Take a pre-trained model, package it in a Docker container, implement multi-stage builds, scan with Trivy, fix vulnerabilities, and deploy securely to a local Kubernetes cluster (minikube/kind).

~25h
Container & Kubernetes SecurityIaC Security Basics

Third-Party Model Integration Risk Assessment

Advanced

Choose a model from Hugging Face Hub, perform a security and ethical assessment: scan for vulnerabilities, test for bias, evaluate the license, and write a formal risk assessment report recommending or cautioning against its use.

~50h
AI Ethics & ComplianceThreat Modeling for AIAPI Security for Model Endpoints

Secrets Management in an ML Workflow

Beginner

Set up HashiCorp Vault or AWS Secrets Manager. Modify an existing ML training script to retrieve a database credential from the secret store at runtime, never exposing it in code or logs.

~20h
Secrets Management in AI WorkflowsSecure AI Pipeline Design

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