Author: Rithik Jaswal Duration: 12 Months Level: Intermediate → Advanced Focus Areas: Cybersecurity · Machine Learning · Generative AI · Threat Modeling
This roadmap is designed to guide a Cyber Security Engineer through a structured 12-month transformation into an AI/ML Security Architect. By the end of the course, you will have mastered:
- Data Engineering for Security Analytics
- Applied Machine Learning for Threat Detection
- Generative AI Integrations for SOC Automation
- Adversarial AI & Governance Frameworks
Goal: Reinforce Python fundamentals for security automation and data manipulation.
- Core Data Structures (Lists, Dicts, Sets)
- String Manipulation & Formatting
- Functions & Pythonic Logic
Sample Tasks
- Create a dictionary for user roles and evaluate risk.
- Parse syslog strings to extract attacker IPs.
- Build a function
is_rfc1918(ip)to identify private addresses.
Goal: Learn to move, transform, and standardize data across security tools.
| Week | Focus | Highlights |
|---|---|---|
| 1 | File Handling & JSON | CSVs, I/O, JSON parsing |
| 2 | APIs & Threat Intel | REST APIs, requests library, VirusTotal integrations |
| 3 | DataFrames | Pandas, filtering, merging datasets |
| 4 | Defensive Programming | Error handling, logging, robust automation |
Capstone: Build a security automation script that pulls, cleans, and logs threat data from multiple APIs.
Goal: Adopt software engineering discipline in detection logic development.
- Write Sigma rules for common Windows attacks.
- Manage versions with Git & GitHub (branches, PRs).
- Implement CI/CD pipelines using GitHub Actions.
- Automate deployment into your SIEM with sigma-cli + REST API.
Goal: Transition from static rules to predictive, ML-based detections.
| Month | Focus | Learning Outcome |
|---|---|---|
| 4 | Supervised Learning | Train classifiers on phishing datasets |
| 5 | Unsupervised Learning | Detect anomalies using K-Means & Isolation Forests |
| 6 | MLOps for SecOps | Deploy ML models via FastAPI & Docker |
Capstone: Integrate a trained ML model into your SOAR workflow to score real-time security events.
Goal: Automate Tier-1 SOC operations using LLMs and AI agents.
| Month | Focus | Highlights |
|---|---|---|
| 7 | LLM APIs & Prompt Engineering | Use OpenAI API to analyze event logs |
| 8 | Retrieval-Augmented Generation (RAG) | Implement ChromaDB & LangChain |
| 9 | Agentic Security | Build autonomous agents with ReAct & tool-calling |
Capstone: Deploy an interactive, SOC-ready chatbot that assists with triage, enrichment, and alert investigation.
Goal: Architect and secure AI systems within enterprise SOCs.
| Month | Focus | Key Skills |
|---|---|---|
| 10 | Adversarial ML | Evasion, poisoning, and defense techniques |
| 11 | Securing GenAI | OWASP LLM Top 10, prompt injection defense, guardrails |
| 12 | Governance & Frameworks | MITRE ATLAS, NIST AI RMF, policy design |
Final Capstone: Design a secure, AI-driven SOC architecture integrating:
- Data pipelines (Phase 1)
- Custom ML models (Phase 2)
- Isolated RAG setup (Phase 3)
- Input/output guardrails (Phase 4)
- Strong foundation in Cybersecurity concepts
- Intermediate Python programming
- Basic familiarity with Git, APIs, and JSON
Languages: Python, YAML Libraries: pandas, requests, scikit-learn, joblib, FastAPI DevOps: GitHub Actions, Docker, sigma-cli AI/ML: LangChain, ChromaDB, OpenAI API, IsolationForest, RandomForest
📍 Repository: AI-ML-Security-Roadmap