Book Image

ChatGPT for Cybersecurity Cookbook

By : Clint Bodungen
Book Image

ChatGPT for Cybersecurity Cookbook

By: Clint Bodungen

Overview of this book

Are you ready to unleash the potential of AI-driven cybersecurity? This cookbook takes you on a journey toward enhancing your cybersecurity skills, whether you’re a novice or a seasoned professional. By leveraging cutting-edge generative AI and large language models such as ChatGPT, you'll gain a competitive advantage in the ever-evolving cybersecurity landscape. ChatGPT for Cybersecurity Cookbook shows you how to automate and optimize various cybersecurity tasks, including penetration testing, vulnerability assessments, risk assessment, and threat detection. Each recipe demonstrates step by step how to utilize ChatGPT and the OpenAI API to generate complex commands, write code, and even create complete tools. You’ll discover how AI-powered cybersecurity can revolutionize your approach to security, providing you with new strategies and techniques for tackling challenges. As you progress, you’ll dive into detailed recipes covering attack vector automation, vulnerability scanning, GPT-assisted code analysis, and more. By learning to harness the power of generative AI, you'll not only expand your skillset but also increase your efficiency. By the end of this cybersecurity book, you’ll have the confidence and knowledge you need to stay ahead of the curve, mastering the latest generative AI tools and techniques in cybersecurity.
Table of Contents (13 chapters)

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “If you are using a different shell configuration file, replace ~/.bashrc with the appropriate file (for example, ., ~/.zshrc or ~/.profile).”

A block of code is set as follows:

import requests
url = "http://localhost:8001/v1/chat/completions"
headers = {"Content-Type": "application/json"}
data = { "messages": [{"content": "Analyze the Incident Response Plan for key strategies"}], "use_context": True, "context_filter": None, "include_sources": False, "stream": False }
response = requests.post(url, headers=headers, json=data)
result = response.json() print(result)

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: “In the System Properties window, click the Environment Variables button.”

Tips or important notes

Appear like this.