Learn AI Cybersecurity Attack & Defense in Just 3 Days

• Evolution of AI technology from ML and Deep Neural Networks to Agentic AI
• GenAI system architecture and attack points
• Training models with MLOps pipeline, and securing and partitioning datasets
• Transfer learning of Foundation Models and fine-tuning
• NLP mechanics comprising word embeddings, self-attention, and LLM context window
• Connecting to knowledge bases with RAG and context window overflow
• AI agents functions (Perception, Planning, Action, Learning), and enrichment “Loop of death”
• Discriminative vs Generative AI models and multimodal prompting
• Do Nows: Tinker With a Neural Network using TensorFlow Playground, Exploring CNN, Examine Federated Learning, Google Natural Language API Analysis, Building AI Agents with Vertex AI, Google AI Studio
• Demo: Creating a Co-Occurrence Matrix
• LAB: Utilizing a Small Language Model

• Mitigating risk with CIANA+PS pillars and Risk Register
• Tracking AI vulnerabilities using CVE and CWE dictionaries
• Zero Trust Frameworks applied to AI “Quad of IAM”
• Ethics and Autonomy with human in the loop, and risks with PII, Intellectual Property and Bias
• AI Threat Mind Map categorizing threats to/from models, including human risks
• NIST AI RMF core functions(Govern, Map, Measure, and Manage), risks and TEVV processes
• Mitigate Risk With Trustworthy AI and Privacy-Enhanced AI
• Assessing maturity with the AI CMM
• AI Risk Assessment Process with RMF Generative AI Profile
• Mitigating GenAI Risks with grounding, risk signals and DLP safeguards
• OWASP Top 10 ML, LLM and Agentic AI Security Risks
• Do Nows: Known AI Vulnerabilities, Harm to Organizations, NIST AI RMF Playbook, OWASP AI Privacy, Trolley Problem Ethical Dilemma, Risks of “Free Services”, DoD RAI Risk Assessment, Detection with DLP and GenAI, Attacking the OWASP Top Ten ML, LLM and Agentic AI
• LAB: Conducting an AI Risk Assessment
• LAB: Deidentify GenAI Responses

• Integrate security into all phases of AI SDLC Lifecycle
• Adversarial attacks including, GenAI classification, NLP, Dataset poisoning, backdoor Trojan, “Man in the Prompt”
• Secure AI with AI-BOM, sanitization, and security controls
• Secure RAG against, indirect prompt injection, data poisoning, embedding inversion, pirate attack
• Agentic AI kill chain and threat model
• Extending the SAIF Risk Map for AI Agents
• Hacking Agentic AI through rebus, excessive agency, goal hijacking and tool misuse
• Prompt Hacking with injection, jailbreaking and system prompt leaking
• Defensive Guardrails including the Google SAIF, AI Agent Firewalls and Model Armor
• OWASP AI Threat Model
• AI red teaming for proactive defense and interactive testing
• Securing Gen AI with Logging and Monitoring, and Agentic AI with Evaluation Services and AgentOps
• Do Nows: Coercing Misclassification of an ML Model, OWASP Agentic AI Threats and Mitigations, OWASP Agentic AI Top 10: Threats in the Wild, System Prompt Security, Prompt Engineering for Generative AI, SAIF Risk Self Assessment, OWASP AI Security Matrix, OWASP Threat Modeling of an LLM Application, DEFCON GenAI Attack Strategies, OWASP GenAI Red Teaming Strategy, RAI Toolkit, Investigating Adversarial Attacks with ART
• LAB: Penetration Testing an AI System
• LAB: Safeguarding With Gemini AI

• Traditional hacking phases enhanced by AI smart automation, reinforcement learning to evade detection and Out-of-the-Box AI Thinking
• Autonomous hacking in the DARPA DEFCON Cyber Grand Challenge
• Believable AI-Infused Social Engineering and GenAI fraud
• Deepfake technology fabricates target’s video and audio
• AI infused tools including Nmap, Metasploit, and Wireshark enhancements
• Side channel attacks like AI acoustic keyboard monitoring
• The Long Con using AI to build trust and erode resilience over time
• DoNows: Bing Chat as a Social Engineer, Famous Deepfakes, Creating Deepfakes
• LAB: Enhance Hacking With GenAI

• Modern SecOps using Autonomic Security Operations and CD/CR pipelines
• Benefits of AI in Cybersecurity and AI Powering SecOps Functions
• AI Powered detection for intrusions and malware
• AI-Powered IGA, IAM, Security Analytics and Incident Response
• GenAI in SIEM, SOAR, TIM using intelligent data ingestion, automated playbooks and NLP
• The MITRE ATLAS matrix for understanding AI adversarial tactics
• Google AI SecOps leveraging Gemini, SecLM and Mandiant for threat intelligence
• Google Agentic SOC Defense
• Microsoft Security Copilot and GitHub Copilot for malware reverse engineering and policy summarization
• DoNows: Threat Intelligence Platform AV-ATLAS, MITRE ATLAS Navigator
• LAB: Analyze a Codebase With Gemini
• LAB: SecOps Threat Hunting With AI
• LAB: Anatomy of an AI Model Attack
• LAB: Secure Coding With AI

• Global regulations such as UN Ethics of AI and accountability standards
• The EU AI Act risk based framework
• US Executive AI Order
• Pillars of Trustworthy AI comprising responsible, reliable, and resilient systems
• Google’s Responsible AI and the "Agentic" Shift
• EU AIGA Hourglass Model Governance framework
• The OECD AI system lifecycle stages
• Model AI Governance Framework (MGF) for Agentic AI
• Four dimensions of Agentic AI
• DoNow: AIGA AI Governance Lifecycle

The AI & Cybersecurity: Attack & Defend course is a 3-day, instructor-led training program focused on the security of artificial intelligence systems and the use of AI in cybersecurity. Participants explore AI architecture, generative AI, large language models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI, AI risk management, adversarial attacks, AI-powered hacking, AI-powered Security Operations (SecOps), and AI governance.

You’ll learn how modern AI systems are built and operated, where AI applications can be attacked, how to assess and mitigate AI risks, and how AI can be used by both attackers and defenders. The course includes hands-on activities covering AI vulnerabilities, prompt injection, adversarial attacks, RAG security, agentic AI attacks, AI penetration testing, red teaming, AI-powered threat hunting, and defensive AI security controls.

Yes. The course explores Generative AI and LLM architecture, including word embeddings, self-attention, context windows, foundation models, fine-tuning, multimodal prompting, and common security risks affecting generative AI applications and large language models.

Yes. Agentic AI security is a major component of the course. Participants learn how AI agents perceive, plan, act, and learn, while examining threats such as goal hijacking, excessive agency, tool misuse, prompt attacks, and agentic AI kill chains. The course also introduces defensive concepts including agent firewalls and AI security controls.

Yes. The course examines AI-powered hacking and smart automation, including how generative AI can enhance traditional hacking techniques, reconnaissance, exploitation, social engineering, fraud, and other attack activities. Students also explore how AI can be used to automate portions of the attack lifecycle.

The course demonstrates how AI can transform Security Operations (SecOps) through AI-powered threat detection, security analytics, incident response, threat hunting, security information and event management (SIEM), security orchestration and automated response (SOAR), and threat intelligence.