AI Cybersecurity: Attack and Defend

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 In this course, you will: •    Understand AI Foundations and Applications in Security.•    Assess Risks and Ethical Considerations in AI Adoption.•    Analyze AI Vulnerabilities and Attack Vectors.•    Leverage AI for Offensive and Defensive Cyber Operations.•    Enhance Security Operations with AI.•    Navigate AI Security Frameworks and Emerging Technologies. Training Prerequisites Attendees should have foundational knowledge in networking and cybersecurity. AI Cybersecurity Training Outline Learning Objectives Chapter 1: Architecture and Operation of AI Evolution of AI technology Applying AI in Security Machine Learning Deep Neural Networks CNN, RNN, RvNN, Transformers NLP, LLM Generative AI LAB: Investigating Discriminative and Generative AI Chapter 2: Risk in Adopting AI Solutions Risk in Security Risks of AI Implementations Ethical Considerations Risks With GenAI Protecting From GenAI aided attacks Mitigating AI Risks LAB: Protecting Sensitive Data With DLP LAB: Conducting an AI Risk Assessment Chapter 3: Hacking AI Vulnerabilities AI Algorithms, Data Sets, Models OWASP AI Security Risks Prompt Engineering AI vulnerabilities Attacks Against Classifiers NIST Adversarial ML Taxonomy Adversarial ML Threat Matrix AI Red Teaming LAB: Penetration Testing an AI System Chapter 4: Exploiting AI to Hack Systems Using AI to Hack GenAISocial Engineering Deepfakes AI infused Hacking Long Con AI LAB: Enhance Hacking With GenAI Chapter 5: Improving Security Operations with AI SecOps AI-Based Security Processes IT Operations and Cloud AI GenAIRed Teaming AI Security Tools Google AI SecOps Cybersecurity Copilot LAB: Defend Security With AI Chapter 6: Common AI Security Frameworks Regulatory Compliance for AI NIST AI Risk Management Framework OWASP Security & Governance Checklist Responsible AI Google Secure AI Framework Federated Learning Zero Trust Generative AI GenAI Governance Framework