The AI Security Engineer Career Masterclass

The cybersecurity industry is entering a new era.

For years, security professionals have focused on protecting applications, infrastructure, identities, and data from attackers. But AI is changing how applications are built, how they interact with information, and increasingly, how they make decisions and execute actions.

Modern AI applications are no longer limited to simple chatbots.

They use Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), external APIs, Model Context Protocol (MCP), and autonomous AI agents to interact with business systems and perform real-world tasks.

An AI assistant might retrieve confidential company documents, access employee records, create support tickets, or execute actions in a cloud environment.

But what happens when an attacker manipulates the information an AI system receives? What if an AI agent invokes an unauthorised tool, exposes sensitive information, or performs actions that were never intended?

This is where AI Security Engineering comes in.

AI Security Engineering is an emerging cybersecurity discipline focused on understanding, assessing, testing, and securing modern AI applications.

It combines traditional cybersecurity principles such as application security, identity, access control, cloud security, and threat modeling with new challenges including prompt injection, RAG poisoning, insecure MCP integrations, excessive agency, and autonomous AI behaviour.

The AI Security Engineer Career Masterclass is a practical, hands-on course designed to help cybersecurity professionals, software developers, cloud engineers, students, and career changers develop the skills needed to assess and secure real-world AI applications.

You will learn how modern AI systems are built, where their security weaknesses exist, how attackers exploit them, and how to design controls that reduce those risks.

Most importantly, this course focuses on practical AI Security Engineering — not endless slides about AI risks and theoretical frameworks.

Throughout the course, you will work with GlobalTech AI, a realistic fictional enterprise AI application that evolves from a simple LLM-powered assistant into a more sophisticated system incorporating RAG, MCP integrations, external tools, and AI agents.

You will examine its architecture, identify trust boundaries, assess vulnerabilities, explore security testing scenarios, and use the Cloud Security Alliance's MAESTRO framework to perform an agentic AI threat assessment.

The goal is simple: help you move beyond knowing about AI security to demonstrating that you can actually perform the work.

What You Will Learn

• Understand modern AI application architecture: Learn how LLMs, prompts, tokens, context windows, APIs, RAG pipelines, MCP servers, and AI agents work together in real-world applications.

• Assess LLM and prompt security: Understand system prompts, instruction hierarchies, prompt injection, sensitive information disclosure, and the limitations of prompt-based security controls.

• Secure Retrieval-Augmented Generation (RAG): Identify risks involving document ingestion, vector databases, retrieval pipelines, data poisoning, access control failures, and malicious retrieved content.

• Understand Model Context Protocol (MCP) security: Explore MCP hosts, clients, servers, and tools, including excessive permissions, missing authorisation, malicious tool descriptions, untrusted tool outputs, and rogue MCP servers.

• Assess agentic AI security risks: Learn how AI agents plan, make decisions, use memory, and execute tools, and how attackers can exploit excessive agency, goal manipulation, action hijacking, memory poisoning, and cascading tool actions.

• Apply AI threat modeling: Learn how to use the Cloud Security Alliance's MAESTRO framework to systematically identify risks across foundation models, data operations, agent frameworks, infrastructure, observability, security controls, and the wider agent ecosystem.

• Build practical AI security experience: Work through hands-on GlobalTech exercises, interactive JupyterLab runbooks, architecture reviews, security assessments, and threat modeling activities.

• Develop your AI security career: Learn which skills employers value, how existing cybersecurity experience transfers into AI security, and how to create portfolio projects that demonstrate your capabilities.

  
  

Who Should Take This Course

This course is designed for professionals who want to understand how AI changes cybersecurity and develop practical skills for securing modern AI applications.

It is particularly suitable for:

• Cybersecurity professionals who want to transition into AI Security Engineering without starting their careers from scratch.

• Application Security Engineers and Penetration Testers who want to expand into LLM security, prompt injection testing, RAG security, and AI red teaming.

• Cloud Security Engineers and Architects responsible for securing AI workloads, cloud-based AI applications, and integrations with external services.

• SOC Analysts and Security Engineers interested in emerging AI threats, agent monitoring, and security controls for autonomous systems.

• Software Developers and DevSecOps Engineers who want to understand how to build and assess secure LLM-powered and agentic applications.

• GRC, Risk, and Security Consultants who want to develop practical AI threat modeling and AI risk assessment skills.

• Students and Career Changers looking to build foundational AI security knowledge, practical experience, and a portfolio of relevant projects.

• Security Leaders and Architects who need to understand the risks of deploying LLM applications, MCP integrations, and AI agents inside enterprise environments.

The skills covered in this course can help you prepare for or develop towards roles such as AI Security Engineer, AI Application Security Engineer, Agentic AI Security Engineer, AI Red Teaming Specialist, AI Security Architect, AI Security Consultant, and AI Risk & Security Specialist.

Learn AI Security Engineering Through a Realistic Enterprise Project

One of the biggest challenges when learning AI security is finding opportunities to apply what you've learned.

You can read about prompt injection, study OWASP guidance, and learn how AI agents work, but how do you demonstrate those skills without already working on an enterprise AI security team?

This course addresses that challenge through GlobalTech AI, a fictional enterprise application that provides a consistent learning environment throughout the course.

Instead of learning disconnected concepts, you will examine how security risks evolve as GlobalTech adds new AI capabilities.

You will start by understanding the architecture of a relatively simple AI application, then explore the security implications of connecting that application to enterprise data, MCP tools, and autonomous agents.

The practical exercises include:

• AI Attack Surface Assessment: Identify assets, entry points, data flows, trust boundaries, and potential attack paths within GlobalTech AI.

• LLM and Prompt Security: Examine how prompts and untrusted inputs influence model behaviour and understand how security controls should be applied.

• RAG Security: Investigate the risks introduced when AI applications retrieve information from documents and external knowledge sources.

• MCP and Agent Security: Explore how tool access, permissions, agent decisions, and external integrations create new attack opportunities.

• MAESTRO Threat Modeling: Map the GlobalTech agentic architecture to the seven MAESTRO layers, identify credible threats, assess cross-layer attack paths, and recommend mitigations.

• Security Assessment Deliverables: Produce architecture assessments, threat models, risk registers, and security recommendations that demonstrate practical security engineering skills.

The course includes guided demonstrations and interactive JupyterLab exercises designed to make important concepts accessible without requiring advanced programming experience.

These exercises also provide a foundation for creating your own AI Security Engineering portfolio.

Prerequisites

• A basic understanding of cybersecurity concepts is recommended.

• Familiarity with application security, cloud security, APIs, or networking will be helpful but is not mandatory.

• No previous experience securing LLMs, RAG pipelines, MCP servers, or AI agents is required.

• Basic programming knowledge is useful, particularly Python, but advanced software development experience is not necessary.

• The course introduces relevant AI architectures and concepts before exploring their security implications.

• Hands-on JupyterLab exercises are designed to guide learners through practical assessments without requiring extensive coding.

• A willingness to experiment, investigate security risks, and understand how modern AI systems work is more important than holding a particular certification.

As organisations adopt LLM applications, RAG pipelines, MCP integrations, and autonomous AI agents, they need security professionals who understand these technologies and can evaluate the risks they introduce.

The principles of cybersecurity haven't disappeared. Authentication, authorisation, least privilege, defence in depth, threat modeling, and monitoring remain just as important.

But the systems we're applying them to are changing.

AI Security Engineering brings these established security principles into a world where software can reason, plan, use tools, and take actions.

The AI Security Engineer Career Masterclass is designed to help you develop the knowledge, practical skills, and portfolio experience needed to participate in that transition.

Whether you're an experienced cybersecurity professional looking to expand into AI security, a developer building AI applications, or someone preparing for a new career, this course gives you a structured path from understanding the technology to assessing and securing it.

Don't just learn how AI works. Learn how to secure it.