Agentic Security: Attack and Defend AI Agents is a three-day, hands-on course for cybersecurity professionals who need to understand, attack, and defend the autonomous AI systems now operating inside enterprise environments. Every agentic system that perceives, reasons, plans, and acts is a new attack surface. This course teaches you to exploit it and protect it. Agentic Security Benefits In this course you will: Understand Trace the AI architecture stack — ML, DNNs, transformers, LLMs, GenAI models, agentic systems — and identify the attack surface at each layer Master agentic AI design patterns: Cognitive Loop, Planner-Executor-Verifier, multi-agent orchestration, and tool/API integration via MCP Map the threat landscape: OWASP ML Top 10, OWASP LLM Top 10, NIST Adversarial ML Taxonomy, and MITRE ATLAS Build Construct anomaly detection and deep learning malware classification models on real cybersecurity datasets Deploy RAG pipelines integrating AlienVault OTX threat intelligence with chunk provenance validation Implement multi-agent SecOps workflows using LangChain, CrewAI, or AutoGen with Apache Kafka for agent communication Attack Execute all five prompt injection variants: direct, indirect, chained, multi-language, and refusal suppression Conduct training data poisoning, model extraction, token inference side-channel attacks, hallucination exploits, and payload splitting Perform AI-assisted memory forensic analysis using Volatility 3 to detect process hollowing, DLL injection, and advanced persistence Defend & Govern Build autonomous threat detection and response workflows with human-on-the-loop oversight checkpoints Apply NIST AI RMF AI 600-1, OWASP LLM Governance Checklist, and Zero Trust principles to agentic AI deployments Prerequisites 2+ years cybersecurity experience; basic Python; Docker familiarity; comfort with Linux command line; understanding of common attack vectors and defensive frameworks. Who Should Attend: Security Operations & Defensive Roles Security Architecture & Engineering AI / ML & Emerging Tech Roles DevOps, Platform & Automation Roles Governance, Risk & Compliance (GRC) Leadership & Strategy Roles Red Team & Offensive Security Agentic Security AI Training Outline Learning Objectives Module 1: AI Architecture & Agentic Foundations Trace the development of AI from Turing's test to modern agentic systems Demystify ML, deep neural networks, transformers, and LLMs Master agentic AI design patterns: Cognitive Loop, Planner-Executor-Verifier, multi-agent orchestration Identify the AI Security Ecosystem attack surface across compute, data, model, and agent pipeline layers Module 2: Generative AI for SecOps and Risk Management Deploy RAG pipelines integrating live threat intelligence with chunk provenance validation Build AI-powered security operations workflows including incident reporting chatbots Establish a strong foundation in AI security risk management (CIA Triad, CVE, GenAI-specific risks, DLP) Apply adaptive authentication and data protection patterns to AI system deployments Module 3: Hacking AI Agents – Adversarial Techniques Identify OWASP ML Security Top Ten and OWASP LLM Top Ten risks Execute the full prompt injection taxonomy: direct, indirect, chained, multi-language, refusal suppression Master jailbreaking (DAN), prompt leaking, and agent hijacking via crafted inputs Apply MITRE ATLAS and NIST AML taxonomy; execute AI Red Teaming methodology Understand GenAI social engineering, deepfake attacks, and the AI offensive toolkit Module 4: Exploiting the AI Attack Surface Conduct training data poisoning, model extraction, and membership inference attacks Execute token inference side-channel attacks, hallucination exploits, and payload splitting Perform AI-assisted memory forensics using Volatility 3 to detect advanced threats Map all attacks to the NIST AI 100-2 taxonomy and MITRE ATLAS matrix Module 5: Defending with Agents-Autonomous SecOps Build autonomous multi-agent threat detection and response workflows with human-on-the-loop oversight Integrate AI-based IDS, SOAR playbooks, and threat intelligence into agentic SecOps pipelines Deploy multi-agent systems using LangChain/CrewAI with Kafka and Redis/Celery for agent infrastructure Augment SIEM and SOAR with GenAI: NLP threat queries, playbook generation, AI-assisted triage Module 6: AI Governance & Zero Trust for Agents Apply NIST AI RMF AI 600-1, OWASP LLM Governance Checklist, and regulatory frameworks to AI agent deployments Implement Zero Trust patterns for generative AI and agentic systems Deploy a role-aligned AI security agent with signed audit logging as the Zero Trust exit criterion Understand quantum computing implications and advanced persistent AI threats for future readiness
