Welcome: AI, Security, and Responsible Use

Why this matters

AI is changing both the defensive and offensive sides of cybersecurity. This section establishes a practical vocabulary for discussing models, data, automation, threat actors, and responsible use—without treating AI as a magic security control.

Learning outcomes

• Explain where AI can assist security teams and where human judgment remains essential.
• Recognise the AI-specific risks introduced by models, prompts, data, and connected tools.
• Use recognised frameworks to discuss trustworthy and responsible AI.

Watch: public YouTube briefings

• — a concise look at how AI can help find and fix real software vulnerabilities.
• — context on the challenge and the role of AI in cyber defence.

Read: trusted field guides

• NIST AI Risk Management Framework (AI RMF) — the core U.S. framework for managing AI risks.
• MITRE ATLAS — tactics and techniques for adversarial threats to AI-enabled systems.
• OWASP GenAI Security Project — community guidance on risks in LLM and generative-AI applications.

Book shelf

• Machine Learning and Security, Clarence Chio and David Freeman (O’Reilly) — an accessible introduction to the intersection of ML and security.
• Threat Modeling: A Practical Guide for Development Teams, Adam Shostack — a durable foundation for the next section.