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Learning Roadmap

How to Become a AI SOAR Specialist

A step-by-step, phase-based learning path from beginner to job-ready AI SOAR Specialist. Estimated completion: 9 months across 4 phases.

4 Phases
36 Weeks Total
High Entry Barrier
Advanced Difficulty
Your Progress 0 / 4 phases

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  1. Foundations of Security Operations & Automation

    6 weeks
    • Master SOC workflows and incident response fundamentals
    • Learn basic scripting (Python) for task automation
    • Understand core SOAR platform concepts and playbooks
    • SANS SEC501 or SEC511 courses
    • Splunk Free SOAR Training
    • Automate the Boring Stuff with Python
    • MITRE ATT&CK Framework documentation
    Milestone

    Can create simple, conditional playbooks in a SOAR platform using APIs.

  2. AI/ML Fundamentals for Security

    8 weeks
    • Learn core ML concepts (supervised/unsupervised learning)
    • Understand NLP and LLM fundamentals for security text analysis
    • Build basic anomaly detection models on security datasets
    • Fast.ai Practical Deep Learning for Coders
    • Hugging Face NLP Course
    • Kaggle security datasets (e.g., CICIDS2017)
    • Andrew Ng's ML Specialization on Coursera
    Milestone

    Can train and evaluate a basic ML model for classifying security events.

  3. Integrating AI into SOAR Workflows

    10 weeks
    • Learn to use LangChain/OpenAI APIs for alert enrichment
    • Design playbooks with AI decision gates and confidence scoring
    • Build end-to-end pipelines for automated phishing analysis
    • LangChain Documentation & Security Templates
    • API documentation for CrowdStrike/Microsoft Sentinel
    • Project: Build a phishing email triage agent
    Milestone

    Can build a playbook that uses an LLM to analyze suspicious emails and take automated actions based on confidence levels.

  4. Advanced AI-SOAR Architecture & Scale

    12 weeks
    • Architect scalable, fault-tolerant AI-SOAR systems
    • Implement MLOps for security model retraining pipelines
    • Design adversary simulation to test AI playbooks
    • Master ethical considerations and human-in-the-loop design
    • AWS Well-Architected Framework for Security
    • MLOps principles (MLflow, Kubeflow)
    • ATT&CK Evaluations for testing
    • Case studies from major breaches (e.g., SolarWinds)
    Milestone

    Can design and present a comprehensive AI-SOAR architecture for a large enterprise, including fail-safes and human oversight.

Practice Projects

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

AI-Enhanced Phishing Triage Automation

Intermediate

Build a SOAR playbook that uses OpenAI API to analyze suspicious emails, extract URLs/attachments, and automatically classify them as malicious or benign, with integration to an email gateway for quarantine.

~25h
SOAR Playbook DesignLLM IntegrationEmail Security

Anomaly Detection for Cloud IAM Events

Advanced

Develop an ML model (using PyTorch) to analyze CloudTrail logs for unusual IAM activity patterns, integrate it as a REST API, and create a SOAR playbook that triggers investigation and temporary access revocation.

~40h
ML Model DeploymentCloud Security (AWS/IAM)Log Analysis

Threat Intelligence Automation with LangChain

Intermediate

Create a system that uses LangChain to parse unstructured threat reports from PDFs and web pages, extract IOCs, and automatically update firewall block lists and SIEM watchlists via SOAR.

~30h
NLP for SecurityThreat IntelligenceData Extraction

Purple Team Exercise with AI Adversary Simulation

Advanced

Design an AI-powered adversary simulation tool that uses reinforcement learning to probe network defenses, and build corresponding SOAR playbooks that detect and contain the simulated attacks in real-time.

~50h
Adversary SimulationReinforcement Learning BasicsDefensive Playbook Design

Self-Healing Infrastructure with SOAR

Beginner

Build a playbook that monitors for common infrastructure issues (e.g., high CPU, disk space) using cloud APIs and automatically triggers remediation actions (e.g., restart service, clean temp files) with AI-driven root cause suggestion.

~15h
Basic AutomationCloud API UsageMonitoring & Alerting

Ready to Start Your Journey?

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