AI-Security-Learning-Lab

By Olarewaju Adebulu

🧭 Overview

This repository is a structured, research-driven documentation of my journey into:

Artificial Intelligence (AI) Engineering
AI Security & Governance
Cybersecurity (SOC Pathway)
Cloud-based AI Systems (AWS)

It serves as both:

📘 A public learning log
✍🏽 A technical writing portfolio

This is a system-level breakdown of intelligent systems and their security implications.

🎯 Core Focus Areas

🤖 Artificial Intelligence Engineering

AI, ML, and Deep Learning foundations
Generative AI and Foundation Models
Large Language Models (LLMs)
Model training, evaluation, and inference

🔐 AI Security & Risk

Prompt Injection vulnerabilities
Model misuse & adversarial attacks
Data poisoning risks
AI system attack surfaces

🛡️ Cybersecurity (SOC Perspective)

Threat detection fundamentals
Log analysis & monitoring
Incident response workflows
AI-driven attack evolution

☁️ Cloud AI (AWS Focus)

AWS AI services (SageMaker, Bedrock)
Model deployment pipelines
Real-time vs batch inference
Scalable AI architectures

🧱 Repository Structure

aws-ai-security-engineering/
│
├── 01_Foundations
│ ├── AI_Foundamentals.md (Foundations of Artificial Intelligence, Machine Learning, and Generative AI)
│ ├── Generative_Models.md (Generative Models, Training Methods & Learning Paradigms)
│
├── 02_AI_Risks
│ ├── prompt-injection.md
│ ├── ai-threat-modeling.md
│
├── 03_AI_Security|Cybersecurity/
│ ├── soc-foundations.md
│ ├── detection-strategies.md
│
├── 05_Projects
│ ├── (coming soon...)
│ ├── (coming soon...)
│
└── README.md