As AI becomes increasingly integrated into critical infrastructure and industrial systems, it brings with it new layers of complexity, and new avenues for attack. In this topic, you’ll explore how Artificial Intelligence is reshaping the security landscape of Industrial Control Systems (ICS) and Operational Technology (OT), and what this means for defenders working in high-risk, high-impact environments. We begin by examining how AI is applied in ICS and OT, enhancing operational efficiency, automation, and predictive maintenance. But with innovation comes risk: AI introduces novel vulnerabilities, from AI-driven manipulation of cyber-physical systems to emerging attack vectors in critical infrastructure such as energy grids and manufacturing lines. Through real-world case studies, you’ll investigate how adversaries exploit AI in industrial environments and how traditional OpSec and DevSecOps practices must be adapted to secure AI-enabled deployments. You'll also learn how to identify sensitive components within AI pipelines and apply context-specific defences based on sector, whether in military-grade applications, industrial settings, or consumer products. AI is powering the future of industry. Here, you’ll learn how to defend it.

As AI systems transition from experimental models to real-world deployment, their exposure to adversarial threats and misuse increases dramatically. In this topic, we’ll explore how AI is being attacked and exploited in practice, and why securing these systems is now a critical focus for cyber professionals. You’ll dive into the mechanics of AI-specific attack vectors such as model poisoning, information leakage, model stealing, and backdoor exploits. These threats not only compromise the performance of AI models, but also pose serious risks to data privacy, intellectual property, and user safety. We’ll also examine the implications of harmful AI outputs, whether they arise from poorly aligned models, biased training data, or deliberate manipulation. You’ll learn how challenges such as output alignment, ethical censorship, and AI-powered surveillance affect both public trust and legal compliance. By analysing real-world case studies and scenarios, this topic will sharpen your ability to identify vulnerabilities in AI systems and understand the broader societal consequences of insecure deployments. AI is already shaping the world, this topic helps ensure it does so securely and responsibly.

As AI systems become more powerful and integrated into critical operations, defending them against emerging threats is no longer optional—it’s mission-critical. In this topic, you’ll explore the technical controls and testing strategies used to secure AI models and protect them from compromise. You’ll learn how to apply AI-specific defences, from secure algorithm design to privacy-preserving techniques like differential privacy. You’ll also examine how to test and validate the robustness of AI models using red, purple, and blue teaming approaches. With a focus on balancing security, utility, and performance, this topic empowers you to make informed trade-offs in high-stakes environments. Whether you’re building or auditing AI systems, you’ll gain the practical skills needed to implement trusted controls and rigorously test for resilience against real-world threats.

As AI systems grow in influence and complexity, so too does the imperative to ensure they are designed, deployed, and governed responsibly. This topic introduces the foundational principles of Responsible AI—covering fairness, bias mitigation, transparency, and ethical accountability. You’ll explore how AI decisions can impact individuals and communities, and how to navigate trade-offs between user privacy, model performance, and transparency. Key challenges such as data sourcing, labelling, and the ethical implications of large-scale models will be unpacked, alongside practical strategies for enhancing trust in AI systems. We’ll also dive into global frameworks, policies, and governance models that support secure and ethical AI adoption, equipping you with the knowledge to ensure AI systems are not only functional—but fair, transparent, and aligned with regulatory expectations.

AI is evolving rapidly—and with it, the scope and complexity of its security challenges. In this final topic, we turn our attention to the road ahead: examining how emerging applications and architectures will shape the next frontier of AI security. You’ll explore speculative but increasingly plausible uses of AI in sectors like healthcare, autonomous vehicles, and programming—unpacking the unique risks each use case presents. We’ll also introduce Artificial General Intelligence (AGI), examining its transformative potential alongside the profound security and ethical implications it may carry. From lightweight AI models for constrained devices to philosophical perspectives on security trade-offs, this topic encourages you to think critically and proactively. The goal: to equip you with the insight and foresight needed to anticipate future risks, influence responsible innovation, and contribute to the safe evolution of intelligent systems.