AI Security Mastery

90-day learning path from ML fundamentals to production AI security systems

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🎯 What This Is

A comprehensive, hands-on learning path for security professionals entering AI security. Built by a security engineer with 13 years experience, targeting the critical gap between traditional security and AI/ML security.

Not another ML course. This is security-first, implementation-focused, and brutally practical.

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🚀 Quick Start

git clone https://github.com/raghavpoonia/ai-security-mastery.git
cd ai-security-mastery
./setup.sh
jupyter lab

Start with: book/part-1-foundations/chapter-01-ml-fundamentals.md

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📚 Learning Path

Part 1: ML Foundations (Weeks 1-3)

• Chapter 1: Machine Learning Fundamentals (28 sections)
• Chapter 2: Deep Learning Basics
• Chapter 3: LLM Architecture
• Chapter 4: Modern LLM Internals

Deliverables: Spam classifier from scratch, neural network with backprop, mini-transformer, fine-tuned GPT-2

Part 2: AI Security Landscape (Weeks 4-6)

• Chapter 5: AI Threat Landscape
• Chapter 6: Prompt Injection Attacks
• Chapter 7: Jailbreak Techniques
• Chapter 8: Training Data Poisoning
• Chapter 9: Model Extraction & Stealing
• Chapter 10: Adversarial Machine Learning

Deliverables: 50+ documented attack techniques, backdoored model, adversarial examples generator

Part 3: Detection Engineering (Weeks 7-9)

• Chapter 11: Detection Framework Design
• Chapter 12: ML-Based Detection Systems
• Chapter 13: Behavioral Analysis & Monitoring
• Chapter 14: Production Deployment

Deliverables: Detection architecture, ML classifier (95%+ accuracy), production API gateway

Part 4: Implementation (Weeks 10-12)

• Chapter 15: Building Production Detectors
• Chapter 16: SIEM Integration
• Chapter 17: Monitoring & Tuning
• Chapter 18: Real-World Case Studies

Deliverables: Complete detection suite, 3 SIEM integrations, 10K+ API call analysis

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🎓 Philosophy

Build understanding from first principles:

• Implement algorithms from scratch (NumPy) before using libraries
• Understand mathematics and theory deeply
• Know what's abstracted away by high-level APIs

Security-focused throughout:

• Every chapter connects to security implications
• Attack vectors taught alongside defenses
• Production deployment with security in mind

Explicitly acknowledge gaps:

• Clear scope: LLM security focus (95% of job market)
• Out of scope: CV security, RL security, federated learning
• Can add advanced topics later

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🛠️ Tech Stack

Core: Python 3.10+, NumPy, Jupyter Lab ML: PyTorch, scikit-learn, Transformers (HuggingFace) NLP: spaCy, NLTK API: FastAPI, uvicorn Monitoring: MLflow, Weights & Biases

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📈 Success Metrics (90 Days)

Technical:

• ML algorithms implemented from scratch
• Transformer architecture mastered
• OWASP LLM Top 10 complete
• 5+ detection systems built
• Production API gateway deployed

Career:

• GitHub repo with 50+ stars
• 4+ technical blog posts
• Resume line: "Built AI security detection framework"
• Conference talk submitted
• Recruiter interest from AI security roles

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👤 Author

Raghav Dinesh

• Security Intelligence Lead @ IBM
• M.Tech, IIT Kanpur (Quantum Cryptography)
• 13 years security operations experience
• GitHub: @raghavpoonia

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📄 License

MIT License - Learn freely, build openly

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🤝 Contributing

Contributions welcome! See CONTRIBUTING.md

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Start: 2025 Target: 2026 Status: 🚀 Active Development