Mastering AI & Cybersecurity: From Foundations to Pro

About This Course

Bridge the Gap Between AI & Cybersecurity

Mastering AI & Cybersecurity: From Foundations to Pro is an industry-first, end-to-end professional programme that bridges the gap between cutting-edge Artificial Intelligence and modern cybersecurity practice.

Designed by two seasoned security practitioners with a combined 20+ years of hands-on experience, this course takes you from the foundations of AI through to building, securing, and weaponising large language models in real-world offensive and defensive security scenarios.

Whether you are a security engineer, penetration tester, SOC analyst, DevSecOps professional, or an AI enthusiast looking to pivot into security — this course delivers the skills, tools, and mindset to stay ahead in 2025 and beyond.

Prerequisites

Basic understanding of networking and operating systems
Familiarity with any scripting/programming language
Curiosity and a hacker mindset

No prior AI experience required.

Complete Course Curriculum

9 Modules of Intensive Training

AI Landscape — Foundations

Human Intelligence vs. Artificial Intelligence
Machine Learning (ML) & Supervised / Unsupervised Learning
Neural Networks & Deep Learning Concepts
Generative AI — Labs: Text, Image & Music Generation
Agentic AI & AI Agents — Architecture & Use Cases
Retrieval-Augmented Generation (RAG)
Model Context Protocol (MCP) — Introduction
Running LLMs Locally — Ollama, LM Studio & llama.cpp Setup
Choosing the Right LLM — Parameters, Quantisation & Hardware Requirements

Building & Running LLM Models

What is a Large Language Model (LLM)?
Dataset Curation & Preparation
Tokenisation — BPE, WordPiece & Byte-Level
Pre-Training, Fine-Tuning & RLHF
Running LLMs on GPU vs. CPU — Performance Benchmarking
Deploying LLMs via REST API — Ollama, vLLM & HuggingFace Inference
Quantisation Formats — GGUF, GPTQ, AWQ Explained
Building an Intrusion Detection System with LLMs
Survey of Leading LLM & SLM Models (GPT-4, Gemini, Llama, Qwen, Mistral)

Prompt Engineering

Anatomy of a Prompt — Components & Best Practices
System Prompts vs. User Prompts
Few-Shot, Chain-of-Thought & Tree-of-Thought Prompting
Function Calling & Tool Use in LLMs
Running LLMs Interactively — CLI, Web UI (Open WebUI) & API Clients
Guardrails — Input & Output Validation

Vibe Coding — AI-Assisted Development

Introduction to AI-Powered Code Generation
Building Full Applications with AI Assistants
Analysing & Auditing AI-Generated Code
Writing Secure Prompts for Code Generation
Live Lab: Build & Break an AI-Generated App

AI for Security Operations

Running Local LLMs for Offline Security Analysis (Air-Gapped Environments)
Threat Modelling with AI
Secrets Scanning — AI-Enhanced Detection
SAST (Static Analysis) & SCA with AI
DAST (Dynamic Analysis) — AI-Powered Fuzzing & Testing
DevSecOps Integration — AI in CI/CD Pipelines
Secure Application Development Lifecycle
Cloud Security — AI Monitoring & Anomaly Detection
Mobile Application Security with AI Tooling
Security Operations Centre (SOC) — AI Automation & Triage

Risks with AI

Deepfakes — Detection, Creation & Countermeasures
Shadow AI — Risks of Unauthorised AI Tool Usage
AI Supply Chain Risks & Model Provenance
Regulatory Landscape — EU AI Act, NIST AI RMF

AI Security — Attacking LLMs

OWASP Top 10 for LLMs

Prompt Injection — Direct & Indirect Attack Techniques
Bias & Fairness Vulnerabilities
Hallucination — Exploitation & Mitigation
Jailbreaking Techniques (DAN, Many-Shot, Adversarial Suffixes)
System Prompt Leakage & Exfiltration
Insecure Output Handling
Training Data Poisoning
Model Denial of Service (DoS/Rate-Limit Abuse)
Supply Chain Vulnerabilities & Malicious Model Weights
AI Software Bill of Materials (AI-SBOM)
Sensitive Information Disclosure
Insecure Plugin / Tool Design
Excessive Agency & Privilege
Overreliance on AI Output
Model Theft & Extraction Attacks
OWASP Top 10 for LLMs — Full Walkthrough

Agentic AI Security — Attacking AI Agents

AI Agent Architecture & Threat Surface
Agentic AI — Goals, Memory & Tool Chains
Hands-On Examples with Real Agent Frameworks
Agent Goal Hijack Attacks
Tool Misuse & Exploitation
Memory & Context Poisoning
Insecure Inter-Agent Communication
Rogue Agent Scenarios & Kill-Switch Design
OWASP Top 10 for Agentic AI

MCP Security & Securing AI Systems

MCP Introduction — Architecture & Design Patterns
Building Custom MCP Tools (Hands-On Lab)
MCP in Application Security Workflows
MCP Vulnerabilities: Secret Exposure, Tool Poisoning, Context Injection
Privilege Escalation via Scope Creep
Shadow MCP Servers & Discovery
Insufficient Authentication & Authorisation in MCP
Guardrails — Implementing Input/Output Controls
AI Security Posture Management (AI-SPM)
Data Masking & DLP for AI Pipelines
Securing Agentic Workflows End-to-End

Mastering AI &
Cybersecurity

Bridge the Gap Between AI & Cybersecurity

Prerequisites

Built for Security Professionals

Everything You Need to Get Started

Start Date

Schedule

Duration

Mode

Timing

Certification

9 Modules of Intensive Training

AI Landscape — Foundations

Building & Running LLM Models

Prompt Engineering

Vibe Coding — AI-Assisted Development

AI for Security Operations

Risks with AI

AI Security — Attacking LLMs

Agentic AI Security — Attacking AI Agents

MCP Security & Securing AI Systems

Skills That Set You Apart

Hands-on Practical Exercises

MCP Tool Development Proficiency

Real-World Red Team Techniques

AI-Augmented DAST / SAST Skills

LLM Fine-Tuning & Model Building Skills

Industry-Recognised Completion Certificate

OWASP LLM & Agentic AI Expertise

50 Hours of Live Instructor-Led Training

Ready to Master AI Security?