Cybersecurity in the Age of AI: Risk, Resilience and Reality

Understanding AI-Driven Cyber Risk

- Identify how AI systems create new risk surfaces across data pipelines, model training, and algorithmic decision-making

- Recognise emerging threat types including data poisoning, model inversion, and adversarial attacks

- Understand regulatory and ethical implications of insecure AI systems

- Analyse case studies of AI-related cyber incidents and lessons learned

People, Culture and Ethics: Building Human Resilience

- Examine how organisational culture and diversity influence cybersecurity behaviours in AI projects

- Understand the ethical dimensions of AI security, including bias, transparency, and accountability

- Explore practical methods to embed security awareness and psychological safety across teams

- Learn how leadership communication and inclusive practices can strengthen cyber resilience culture

Breaking Silos: Collaboration Across Functions

- Map the key roles and responsibilities across technical, executive, and regulatory stakeholders

- Develop shared language and communication strategies between cyber, data, and governance teams

- Understand how cross-functional risk ownership improves detection, response, and recovery

- Work through examples of effective governance models for AI and cybersecurity integration

From Insight to Action: Designing Resilient AI Systems

- Apply workshop learnings to develop a draft cyber resilience strategy for an AI initiative

- Conduct a scenario-based exercise assessing risk, resilience, and mitigation pathways

- Identify key performance indicators and governance checkpoints for ongoing maturity

- Commit to next steps for embedding resilient design and ethical practice in AI programs