AI Cybersecurity and Data Privacy
Equip practitioners to map AI attack surfaces and data flows, recognise AI-specific threats, apply privacy impact assessment and secure deployment practices, and build threat models, monitoring, incident response and vendor assurance for AI systems.
Upcoming course. LMS delivery and enrolment are not open. Release dates will be confirmed.
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Planned learning outcomes
- Map the components, model access patterns, data flows and trust boundaries of an AI system
- Explain prompt injection, data leakage, poisoning and insecure integrations, and the layered defences against each
- Conduct a privacy impact assessment for an AI use case against the Australian Privacy Principles, with international comparison
- Design access controls and secure deployment settings for AI assistants, retrieval systems and agents
- Build an AI threat model and plan monitoring, incident response and vendor assurance
- Assess the effectiveness of security and privacy controls and rate residual risk