Learning Outcomes - What You Will Learn

Core skills and competencies gained across our AI Security training programmes.

✓ Understand the capabilities, architecture, and security landscape of AI and agentic AI systems

✓ Identify security threats and vulnerabilities specific to AI agents and autonomous AI workflows

✓ Apply practical threat modelling techniques to AI and agentic AI architectures

✓ Use Rapid Threat Model Prototyping to identify potential attack paths and security weaknesses

✓ Analyse threats across AI agents, tools, integrations, data flows, and system interactions

✓ Assess AI security risks based on likelihood, impact, and potential consequences

✓ Apply recognised AI security and cybersecurity frameworks to real-world AI systems

✓ Develop practical mitigation strategies, security controls, and guardrails for AI applications

✓ Improve the safety, resilience, and security of AI systems throughout their development lifecycle

AI Security Training for Every Role

Courses matched to your technical background, responsibilities, and AI security objectives.

🧑‍💻 AI & Software Engineers

Understand the security implications of building AI applications and autonomous agents. Learn how to identify attack paths, model AI-specific threats, and incorporate security controls into AI architectures and development workflows.

🏗️ Security & Solutions Architects

Assess the security of AI-enabled architectures, including agent interactions, tools, integrations, trust boundaries, and data flows. Apply threat modelling techniques to identify and address risks before systems reach production.

🔒 Cyber Security Professionals

Develop practical skills for assessing emerging AI security threats. Learn how to analyse agentic attack scenarios, identify weaknesses, assess risk, and develop appropriate mitigations using recognised security frameworks.

🎯 Threat Modellers & Security Researchers

Extend existing threat modelling expertise into the rapidly evolving world of AI. Apply Rapid Threat Model Prototyping to complex AI-agent systems and investigate attack paths and threat scenarios unique to autonomous AI.

🛡️ DevSecOps & Application Security Teams

Understand how AI security can be incorporated into existing security and development processes. Identify AI-specific risks and translate threat modelling findings into actionable security controls and mitigations.

👔 Technology, Risk & AI Leaders

Build a practical understanding of the security implications of increasingly autonomous AI systems. Learn how to identify major risks, challenge technical assumptions, and support informed decisions around AI security, governance, and deployment.

Whether you're developing AI applications, securing enterprise systems, or responsible for understanding emerging AI risks, this course group provides a practical learning pathway for identifying and mitigating threats across modern AI and agentic AI systems.

What Our AI Security Training Covers

🤖 Agentic AI Security

Understand the capabilities and risks associated with autonomous AI agents, including agent decision-making, tool use, integrations, and interactions with other systems.

🧩 AI Threat Modelling

Learn how to model AI systems, identify potential attack paths, analyse threat scenarios, and understand weaknesses across complex AI-agent architectures.

🔍 Rapid Threat Model Prototyping

Apply a practical approach to rapidly develop and refine threat models for AI systems. Use modelling techniques to identify security issues early and turn findings into actionable security improvements.

⚠️ Threat Analysis & Risk

Assess emerging threats based on likelihood, impact, and potential consequences. Prioritise security risks and focus mitigation efforts on the areas that could have the greatest effect on an AI system.

🛡️ AI Security Frameworks

Explore recognised AI and cybersecurity frameworks and understand how they can be applied to real-world AI systems. Connect threat modelling activities with practical security controls.

🔧 Threat Mitigation & Guardrails

Translate identified threats into practical security measures. Develop controls and guardrails designed to reduce vulnerabilities, manage AI-agent risks, and improve the resilience of AI systems.