GenAI for Cybersecurity #
Course Overview:
Here’s a simplified and enriched version of your course outline for “Generative AI for Cybersecurity”, written in clear, easy-to-understand language while keeping the core topics intact. I’ve also filled in a few gaps for completeness and flow.
Generative AI for Cybersecurity #
Course Overview:
This 3–4 day hands-on workshop introduces how Generative AI is transforming the world of cybersecurity. You’ll learn how AI models work, explore real-life applications like detecting threats and generating synthetic data, and build working prototypes using open-source tools.
1. Getting Started with AI in Cybersecurity #
- What is AI and how it applies to cybersecurity
- The difference between Discriminative and Generative AI models
- A big-picture view of AI’s role in modern cybersecurity
Goal:
Get comfortable with AI basics and understand why Generative AI is becoming important in defending against cyber threats.
2. Lifecycle of a Generative AI Project #
- The typical phases of an AI project: from idea to deployment
- Where Generative AI fits into cybersecurity workflows
- Key applications: threat detection, data synthesis
Goal:
Understand the steps involved in building a Generative AI solution and how it helps secure digital environments.
3. Machine Learning for Cybersecurity #
- Supervised vs. Unsupervised learning
- Important algorithms: clustering, anomaly detection, classification
- How to measure model performance: accuracy, precision, recall, F1 score
Goal:
Learn the basic types of machine learning and how they’re used to detect threats and assess risk.
4. Deep Learning in Cybersecurity #
- Basics of deep learning and neural networks
- Use cases: fraud detection, behavioral analytics
- Challenges: large datasets, high computing power, bias and fairness
Goal:
Understand deep learning and how it powers advanced cybersecurity systems.
5. Generative AI Models and Security Risks #
- Introduction to LLMs (Large Language Models) and LMMs (Large Multimodal Models)
- Prompt engineering and its risks: prompt injection, jailbreaking, adversarial prompting
- Advanced techniques like Retrieval-Augmented Generation (RAG) and agents
Goal:
Learn how generative models work and what risks they pose in cybersecurity contexts.
6. Building Chatbots and Q&A Systems using RAG #
- Key building blocks for LLM apps: models, prompts, memory, chains
- Working with vector databases (FAISS, Milvus, Chroma) and embeddings
- Creating a Q&A system using LangChain
Goal:
Understand and build RAG-powered applications like intelligent chatbots for threat intelligence or security Q&A.
7. LLM-Powered Autonomous Agents #
- What are autonomous agents and how do they differ from basic models
- Agent architecture: ReAct, agent runtime, reasoning via tools
- Multi-agent systems and agentic RAG (Self-RAG, Adaptive-RAG)
- Hands-on implementation with frameworks like LangGraph, Autogen, and CrewAI
Goal:
Explore how autonomous agents powered by LLMs can independently carry out complex security tasks over time.
8. Real-World Cybersecurity Use Cases #
- Detecting and preventing threats using AI
- Identity and Access Management (IAM)
- Spotting phishing using NLP techniques
- Real-time fraud detection with anomaly detection models
Goal:
See how AI is applied in real-world cybersecurity scenarios, from stopping phishing to detecting fraud.
9. Ethical Considerations in AI for Cybersecurity #
- Data privacy, bias in AI, and responsible deployment
10. Tooling Ecosystem Overview #
- Briefly introduce tools used across modules (TensorFlow, PyTorch, Hugging Face, LangChain, Open WebUI, etc.)
11. Mini Capstone Project #
- Build a basic AI-powered cybersecurity tool using concepts learned
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