AI-Powered Cybersecurity Training

Attackers are using AI to write convincing phishing, clone voices and speed up reconnaissance, while defenders face more alerts than any team can read. This course shows how AI helps on the defensive side and how to secure AI systems your organisation is starting to deploy.

You begin with the machine learning concepts a security practitioner actually needs: supervised and unsupervised learning, feature engineering from logs, model evaluation, and why false positives matter. You then apply them to detection tasks such as user and entity behaviour analytics, phishing and URL classification, malware triage and network anomaly detection. A major block covers large language models in security operations: using them to summarise alerts, enrich indicators, draft incident reports and assist hunts, while keeping humans in control and protecting sensitive data.

The final part flips the question and looks at attacks on AI itself, including prompt injection, jailbreaks, training-data poisoning, model theft and insecure agent tools, together with governance frameworks that organisations are adopting. Labs use accessible tools and notebooks, and a short Python familiarity is helpful but not essential.