GenAI for Cybersecurity

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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

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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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• Data privacy, bias in AI, and responsible deployment

10. Tooling Ecosystem Overview

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• Briefly introduce tools used across modules (TensorFlow, PyTorch, Hugging Face, LangChain, Open WebUI, etc.)

11. Mini Capstone Project

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• Build a basic AI-powered cybersecurity tool using concepts learned