Course Description

This intensive 5-day course equips cybersecurity professionals with practical knowledge of generative AI and agentic AI systems, focusing on applications, risks, defensive strategies, threat landscapes, and security operations.

Who Should Attend

Cybersecurity professionals with 2+ years of experience, SOC analysts, incident responders, security architects, engineers, penetration testers, threat hunters, managers, team leads, and general-interest learners.

Boot Camp at a Glance

AI fundamentals for cybersecurity; Generative AI applications and risks; Agentic AI and autonomous security systems; Memory forensics and advanced analysis; AI governance and compliance.

Prerequisites

Cybersecurity experience, machine learning fundamentals, Python familiarity, Linux command-line proficiency, Docker experience, and basic AWS familiarity.

Learning Outcomes

Design agentic AI systems for cybersecurity, build autonomous workflows, create AI-powered threat hunting and incident response capabilities, integrate security tools, perform AI red teaming, conduct adversarial AI attacks, execute memory forensics analysis, and apply governance frameworks.

Tools and Infrastructure

Metasploit, Nmap, OpenVAS, Maltego, Burp Suite Community, Wireshark, Scapy, Suricata, Zeek, YARA, radare2, Ghidra, Cuckoo Sandbox, Volatility 3, Rekall, LiME, Autopsy, Sleuth Kit, SANS SIFT Workstation, LangChain, CrewAI, AutoGen, Ollama, Apache Kafka, Redis, Celery, Docker, Kubernetes, Terraform, Git, Grafana, and Prometheus.

Five-Day Schedule

Day 1: AI fundamentals, machine learning, threat landscape analysis.
Day 2: Generative AI applications, prompt engineering, AI content risks.
Day 3: Agentic AI systems, autonomous threat response, prompt injection techniques.
Day 4: Memory forensics, advanced memory threats, attack-surface analysis.
Day 5: Governance, compliance, future trends, and skills verification.

Technical Topics

Machine learning, transformers, RAG, hyperparameter tuning, LLM security, chatbot development, incident reporting, adversarial prompting, training-data poisoning, model extraction, token inference, hallucination exploits, payload splitting, memory forensics, malware detection, timeline analysis, explainability, privacy-preserving AI, and quantum-computing implications.

Lab Deliverables

Hands-on exercises across model development, generative AI, adversarial prompting, AI attack techniques, memory forensics, governance, and cybersecurity analysis.