Detailed Course Outline
Module 1 — AI and Data Concepts for Cybersecurity
- AI concepts and core AI types
- Generative AI and transformers
- Machine learning and deep learning
- Natural language processing
- AI model training approaches
- Prompt engineering fundamentals
- Model security considerations
- AI data types and data security techniques
- RAG (Retrieval Augmented Generation) concepts
- Data integrity and processing controls
Module 2 — Threat Modeling and Securing AI Systems
- AI threat modeling fundamentals
- Threat modeling processes and prerequisites
- AI threat modeling frameworks
- AI security control types
- Model guardrails and prompt templates
- Gateway and interface controls
- Usage quotas and limitation controls
- Security control testing
Module 3 — Access Controls for AI
- AI access control principles and models
- Model and agent access controls
- API and network access security
- AI data security controls
- Encryption and data safety measures
- Monitoring and logging AI systems
- Performance and cost monitoring
- AI auditing and compliance monitoring
Module 4 — AI Threats and Compensating Controls
- AI lifecycle security
- Ethical AI design considerations
- AI attack types and techniques
- Backdoor and trojan model attacks
- Model poisoning and inversion
- Model theft risks
- Compensating control strategies
- Post-incident AI analysis
Module 5 — Leveraging AI in Security Operations
- AI-enabled security tools
- AI use cases in detection and analysis
- AI for vulnerability assessment
- AI-enhanced attack vectors
- AI for social engineering and deception
- AI reconnaissance techniques
- AI-driven automation
- AI in DevSecOps workflows
- AI scripting and summarisation
Module 6 — AI Governance, Risk, and Compliance
- AI governance structures
- AI organisational roles
- Responsible AI principles
- AI risk identification and assessment
- AI regulatory themes
- Compliance frameworks for AI
- Organisational AI policy design
- Compliance reporting