Cybersecurity Engineers
AI for threat detection, incident response, and security ops
Viewing the Cybersecurity Engineers track. 15 tracks available for different roles.
Course Overview
Week 1: Foundations
. .
9:00 - 9:15 | Welcome & Orientation
- • Welcome & instructor introductions
- • Course overview & objectives
- • Logistics (breaks, lunch, bathrooms, WiFi)
- • Hybrid format expectations
- • In-person: Participation guidelines
- • Virtual: Camera on/off policy, chat usage
- • Icebreaker: Quick poll
- • What's your security focus? (SOC, pentesting, GRC, AppSec, etc.)
- • AI experience level (1-10)
- • One AI concern you have about security
9:15 - 9:45 | What is AI?
- • What is AI, really? (demystified)
- • AI is not magic (it's pattern matching)
- • AI vs ML vs LLM (hierarchy)
- • Three types of AI (overview)
- • Predictive
- • Generative
- • Agentic
- • Common misconceptions debunked
- • When AI helps vs. when it doesn't
9:45 - 10:15 | Predictive AI Deep Dive
- • Predictive AI explained (weather forecasting analogy)
- • How it works (simplified - no math!)
- • Real-world examples:
- • Netflix recommendations
- • Spam detection
- • Credit card fraud detection
- • **For YOUR work:**
- • Anomaly detection in network traffic
- • Malware classification and family detection
- • Insider threat scoring and user behavior analytics (UEBA)
- • Phishing email classification
- • Vulnerability exploitation prediction (EPSS scores)
10:15 - 10:45 | Generative AI Deep Dive
- • Generative AI explained (creative AI)
- • How LLMs work (simplified)
- • Training on massive text
- • Pattern recognition
- • Next-word prediction
- • Real-world examples:
- • ChatGPT
- • GitHub Copilot
- • AI writing assistants
10:45 - 11:00 | Setup Verification & Break Prep
Break: 11:00 AM - 11:30 AM
11:30 - 11:40 | Lab 1 Introduction
- • Lab 1 overview: "Your First AI Conversation - Security Edition"
- • Learning objectives:
- • Chat with AI model about security topics
- • Write effective security-focused prompts
- • Understand parameters
- • Recognize hallucinations in CVE/threat data
- • Demo: Instructor walkthrough (5 min)
- • Q&A (3 min)
- • Get started!
11:40 - 12:10 | Lab 1: Your First AI Conversation
12:10 - 12:25 | Lab 1 Debrief & Discussion
12:25 - 12:55 | Agentic AI & Introduction to Agents
- • Agentic AI explained
- • Chatbot (passive) vs Agent (active)
- • Example: Security analyst reading alerts vs SOAR playbook executing automatically
- • Components of an agent:
- • Goal
- • Reasoning
- • Tools
- • Action
- • Result
- • Modern tooling: MCP (Model Context Protocol) for standardized tool integration
12:55 - 1:20 | Prompt Engineering Workshop
1:20 - 1:30 | Week 1 Wrap-Up & Homework
- • Recap: What we learned today
- • Three types of AI
- • How to use Generative AI for security tasks
- • Prompt engineering basics
- • Preview: Next Saturday (tease exciting content)
- • Build a threat-intel RAG system with YOUR playbooks
- • Create your first security triage agent
- • Real SOC automation cost analysis
Between Weeks: Practice & Exploration
Homework
Hands-on exercises to reinforce learning and prepare for Week 2
Support
Office hours, Slack channel, and async help from instructors
Resources
Additional reading materials and video tutorials
Explore Other Tracks
Business Analysts
AI for data analysis, reporting, and decision support
Cloud & Platform Engineers
AI for cloud architecture, cost optimization, and scaling
Data Engineers
AI for pipeline design, data quality, and ETL automation
Database Engineers
AI for query optimization, data management, and automation
Infrastructure Engineers
AI for DevOps, SRE, and platform teams
IT Support / Help Desk
AI for ticket triage, troubleshooting, and knowledge management
Network Engineers
AI for network config, routing analysis, and traffic optimization
Product Managers
AI for roadmap planning, user research, and prioritization
Project Managers
AI for sprint planning, risk analysis, and status reporting
QA / Test Engineers
AI for test generation, bug analysis, and quality assurance
Software Developers
AI for code generation, review, and debugging
Splunk Engineers
AI for SPL queries, log analysis, and SIEM operations
Technical Writers
AI for documentation, API references, and style compliance
UX Designers
AI for user research, design critique, and accessibility