AI Cybersecurity: Attack and Defend
3 DAY COURSE

This course explores the intersection of AI and cybersecurity, starting with a foundational understanding of AI technologies such as machine learning, deep learning, and natural language processing, as well as their applications in various industries. The content delves into mitigating risks associated with AI adoption, including risk management and ethical considerations, and identifying vulnerabilities in AI systems.

The importance of integrating AI into security operations is covered through the use of AI for intrusion detection, threat intelligence, and automated incident response, as well as AI’s potential for transforming hacking techniques while highlighting AI-powered attacks and tools.  The Course also emphasizes the need for aligning AI with common security frameworks and regulatory compliance, as well as exploring future trends such as federated learning, AI-powered cyber deception, quantum computing for AI, explainable AI, and AI-driven security automation. 

AI Cybersecurity: Attack and Defend Benefits

• Training Prerequisites

  Attendees should have foundational knowledge in networking and cybersecurity.

AI Cybersecurity Training Outline

Chapter 1: Architecture and Operation of AI

• What is AI? 
• Evolution of AI technology 
• Machine learning, deep learning, natural language processing 
• GenAI 
• Algorithms, data sets, and models 
• AI as a service (AIaaS) 
• Applying AI in Security 
• Why need Cybersecurity in GenAI projects 
• LAB: Google Vision, DLP

Chapter 2: Risk in Adopting AI Solutions

• Identifying and managing risks of AI implementations 
• Ethical considerations 
• Security controls for AI 
• Protecting from GenAI-aided attacks 
• LAB: Google Gemini and ChatGPT 

Chapter 3: Hacking AI Vulnerabilities

• Typical attack vectors against AI systems 
• Vulnerabilities in AI algorithms and models 
• AI Red teaming 
• Exploiting AI weaknesses for malicious gain 
• Cyberattacks/incidents related to the use of GenAI 
• LAB: OWASP Top 10 Machine Learning Security Risks 

Chapter 4: Exploiting AI to Hack Systems

• Transforming Hacking Techniques with AI 
• New Attack Vectors 
• How GenAI is being used for cybercrime    
• AI-powered hacking tools 
• Case studies of successful AI-based attacks 
• LAB: Set up ChatGPT for Hacking 

Chapter 5: Improving Security Operations with AI

• Integrating AI in security and IT operations 
• AI in intrusion detection and threat intelligence 
• AI-powered security information and event management (SIEM) 
• Using AI for Automated Incident Response 
• Microsoft Security Copilot  
• LAB: Google Chronicle SOAR War Story 

Chapter 6: Common AI Security Frameworks

• Regulatory and compliance issues related to AI 
• Securing AI in cloud environments 
• NIST AI Risk Management Framework 
• ISO/IEC 27050-2 
• AI Incident Taxonomy for Adversarial Events 

Chapter 7: Evolving AI security

• Federated Learning 
• AI-Powered Cyber Deception 
• Quantum Computing for AI 
• Explainable AI 
• AI-Driven Security Automation