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Learning Roadmap

How to Become a AI Security Operations Automation Engineer

A step-by-step, phase-based learning path from beginner to job-ready AI Security Operations Automation Engineer. Estimated completion: 9 months across 5 phases.

5 Phases
36 Weeks Total
High Entry Barrier
Advanced Difficulty
← AI Security Operations Automation Engineer Overview Interview Prep →
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  1. Foundations: Networking, Security Fundamentals, and Python Automation

    6 weeks
    Goals
    • Understand core networking concepts (TCP/IP, DNS, HTTP, TLS) and common attack vectors (phishing, SQLi, XSS, privilege escalation)
    • Build proficiency in Python scripting for security tasks - parsing logs, calling APIs, automating repetitive triage steps
    • Learn the structure of a Security Operations Center: alert lifecycle, escalation tiers, incident response frameworks (NIST, SANS)
    Resources
    • Book: 'The Web Application Hacker's Handbook' by Stuttard & Pinto
    • Course: Google Cybersecurity Professional Certificate (Coursera)
    • Practice: TryHackMe 'SOC Level 1' learning path
    • GitHub: python-security/pyt for understanding security tooling patterns
    Milestone

    You can write Python scripts that ingest log data, parse it, and flag indicators of compromise using regex and simple heuristics.

  2. SIEM, SOAR, and Cloud Security Automation

    6 weeks
    Goals
    • Gain hands-on experience with at least one SIEM (Splunk or Elastic) and one SOAR platform (Tines or XSOAR)
    • Build automated playbooks that enrich alerts, query threat intel APIs, and auto-close false positives
    • Understand AWS/Azure/GCP security services and how to collect telemetry from cloud control planes
    Resources
    • Splunk Fundamentals 1 & 2 (free Splunk Education courses)
    • Tines Community Edition (free) for building security automation workflows
    • AWS Security Learning Plan on AWS Skill Builder
    • Tutorial: 'Building a SOC Automation Lab' by MyDFIR on YouTube
    Milestone

    You can build an end-to-end alert pipeline: SIEM detects an event, SOAR enriches it with threat intel, and an automated ticket is created with a recommended action.

  3. ML for Security: Anomaly Detection and Classification

    8 weeks
    Goals
    • Learn fundamental ML concepts relevant to security - supervised classification for malware/phishing detection, unsupervised anomaly detection for network traffic
    • Train and evaluate a security classifier (e.g., detecting malicious URLs or anomalous login patterns) using scikit-learn or PyTorch
    • Understand model evaluation in security contexts: precision/recall trade-offs when false negatives are costly, adversarial robustness considerations
    Resources
    • Course: Andrew Ng's Machine Learning Specialization (Coursera) - focus on classification modules
    • Paper: 'Adversarial Machine Learning' by Biggio & Roli (survey)
    • Dataset: CICIDS2017 network intrusion detection dataset for hands-on practice
    • Book: 'Machine Learning and Security' by Clarence Chio & David Freeman (O'Reilly)
    Milestone

    You can train an ML model on network traffic data to detect anomalies, evaluate it with security-relevant metrics, and serve it as a simple API endpoint.

  4. LLM-Powered Security Automation and Adversarial AI

    8 weeks
    Goals
    • Build an LLM-powered security triage agent using LangChain that summarizes alerts, maps them to MITRE ATT&CK techniques, and recommends response actions
    • Learn adversarial attack techniques against LLMs - prompt injection, jailbreaking, data exfiltration via output manipulation - and implement defenses (input validation, output filtering, guardrails frameworks)
    • Integrate LLM components into existing SOAR playbooks to augment human decision-making with natural-language reasoning
    Resources
    • LangChain documentation and security-specific tutorials (LangChain LCEL for chaining guardrails)
    • OWASP Top 10 for LLM Applications (2025 edition)
    • Course: DeepLearning.AI 'Building Systems with the ChatGPT API'
    • GitHub: Lakera Guard / Guardrails AI for LLM input/output validation patterns
    • Tool: Garak - LLM vulnerability scanner for red-teaming your own models
    Milestone

    You can build a production-ready LLM security copilot that ingests SIEM alerts, provides natural-language investigation summaries, and enforces guardrails to prevent the LLM itself from being exploited.

  5. Production Deployment, MLOps for Security, and Continuous Improvement

    8 weeks
    Goals
    • Deploy security automation systems to production with proper CI/CD, monitoring, versioning, and rollback capabilities
    • Implement feedback loops - analyst corrections feed back into model retraining, playbook refinement, and detection rule updates
    • Build comprehensive observability: dashboards for MTTD/MTTR, automation coverage percentage, false-positive rate trends, and model drift indicators
    • Prepare for interviews by practicing scenario-based incident response with AI-augmented tooling
    Resources
    • MLOps Specialization by Andrew Ng (Coursera) - focus on deployment and monitoring modules
    • Weights & Biases documentation for experiment tracking in security model development
    • AWS Well-Architected Framework - Security Pillow and Machine Learning Lens
    • Project: Build a complete 'AI-Augmented SOC-in-a-Box' open-source project on GitHub
    Milestone

    You can deploy, monitor, and iteratively improve a full AI security operations automation stack - from data ingestion through autonomous response - in a cloud-native production environment.

Practice Projects

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

AI-Powered Alert Triage Assistant

Beginner

Build a Python application that ingests security alerts from a sample SIEM dataset, uses a pre-trained LLM (via OpenAI API) to summarize each alert in plain English, and classifies alerts by severity using simple heuristics and LLM reasoning. Deploy as a FastAPI service with a simple web dashboard.

~25h
Python automationAPI integrationLLM prompt engineering

SOAR Playbook Library with Tines Community Edition

Beginner

Build 5 automated security playbooks using Tines (free tier) that handle common SOC scenarios: phishing email triage, suspicious login investigation, malware hash lookup, vulnerability scan result processing, and user access review automation. Document each playbook with decision logic and error handling.

~30h
SOAR workflow designAPI integrationsecurity operations logic

ML-Based Network Anomaly Detector

Intermediate

Using the CICIDS2017 or NSL-KDD dataset, train an anomaly detection model (Isolation Forest, Autoencoder, or XGBoost classifier) that identifies malicious network traffic. Serve the model as a REST API, build a pipeline that processes pcap-to-features in real-time, and create a Grafana dashboard visualizing detected anomalies.

~40h
ML model trainingfeature engineering for security datamodel serving

LLM Security Red-Team Toolkit

Intermediate

Build a Python toolkit that systematically tests LLM applications for vulnerabilities: prompt injection, jailbreaking, PII extraction, and harmful content generation. Implement a library of attack prompts, automated scoring of attack success, and generate a vulnerability report. Test against OpenAI API and a locally hosted model.

~35h
adversarial AI testingLLM securityautomated security assessment

Detection-as-Code Pipeline with MITRE ATT&CK Mapping

Intermediate

Build a complete detection-as-code workflow: write 20+ detection rules in Sigma format, map each to MITRE ATT&CK techniques, create a CI/CD pipeline (GitHub Actions) that converts Sigma rules to Splunk SPL and Elastic KQL, tests each rule against sample logs, and generates a MITRE ATT&CK coverage heat map.

~35h
detection engineeringSigma rulesCI/CD for security

LangChain Security Investigation Agent

Advanced

Build a LangChain-based agent that accepts a security alert as input and autonomously investigates it by querying multiple tools: a log search tool (using sample Elastic data), an IOC enrichment tool (VirusTotal API), an asset inventory tool, and a threat intelligence knowledge base (RAG with Chroma). The agent produces a structured investigation report with evidence chain and recommended actions.

~50h
LangChain agent architectureRAG for securitymulti-tool orchestration

AI-Augmented SOC-in-a-Box

Advanced

Build a complete open-source AI-augmented SOC lab using Docker Compose: Wazuh (SIEM/XDR) + Shuffle (SOAR) + a custom ML detection microservice + an LLM triage copilot. Ingest telemetry from simulated endpoints, create automated playbooks for 10+ alert types, and demonstrate end-to-end autonomous incident response from detection to containment with audit logging.

~80h
full-stack security architectureDocker/Kubernetes orchestrationSIEM-SOAR integration

ML Model Supply Chain Security Scanner

Advanced

Build a tool that scans ML model artifacts (from HuggingFace Hub or local registries) for security risks: malicious pickle deserialization, embedded executable code, unexpected network calls, data poisoning indicators, and license compliance issues. Integrate the scanner into a CI/CD pipeline that gates model deployment.

~45h
ML supply chain securitystatic analysissecure model deployment

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