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ChatGPT for Cybersecurity Cookbook

ChatGPT for Cybersecurity Cookbook

By : Clint Bodungen
4.8 (12)
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ChatGPT for Cybersecurity Cookbook

ChatGPT for Cybersecurity Cookbook

4.8 (12)
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)
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Analyzing Vulnerability Assessment Reports using LangChain

As powerful as ChatGPT and the OpenAI API are, they currently have a significant limitation—the token window. This window determines how many characters can be exchanged in a complete message between the user and ChatGPT. Once the token count exceeds this limitation, ChatGPT may lose track of the original context, making the analysis of large bodies of text or documents challenging.

Enter LangChain—a framework designed to navigate around this very hurdle. LangChain allows us to embed and vectorize large groups of text.

Important note

Embedding refers to the process of transforming text into numerical vectors that an ML model can understand and process. Vectorizing, on the other hand, is a technique to encode non-numeric features as numbers. By converting large bodies of text into vectors, we can enable ChatGPT to access and analyze vast amounts of information, effectively turning the text into a knowledgebase...

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