Recommended Strategies and Best Practices

Great job reaching the last chapter of this book! The last few chapters have been focused primarily on giving you the hands-on experience needed to help you build more complex penetration testing labs in the cloud. If you took the time to understand what’s happening in the hands-on examples and solutions in this book, then you should be a bit more confident about what you can accomplish with your current knowledge and skills. In this chapter, we will build on top of what you learned in the previous chapters, and we will explore how we can take things to the next level!

We will cover the following in this chapter:

• Increasing the complexity of penetration testing lab environments
• Leveraging Generative AI for estimating penetration testing lab costs
• Unleashing the power of AI-powered tools to accelerate automation script development
• Using AI-powered solutions to generate and explain IaC template code
• Recognizing...

Technical requirements

Before we start, you must have the following ready:

• Visual Studio Code (VS Code) installed and set up on your local machine
• GitHub Copilot set up and configured with VS Code – sign up for a free trial subscription (Copilot for Individuals) using the following link: https://github.com/features/copilot. Make sure that the GitHub Copilot extension is installed and set up completely. You may check the following link for more information: https://docs.github.com/en/copilot/getting-started-with-github-copilot?tool=vscode.
• GitHub Copilot Labs set up and configured with VS Code – sign up using the following link: https://githubnext.com/projects/copilot-labs/. Make sure that the GitHub Copilot Labs extension is installed and set up completely. You may check the following link for more information: https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-labs.
• Amazon CodeWhisperer set up and configured with VS Code – we...

Increasing the complexity of penetration testing lab environments

If you have been to a bouldering (rock-climbing) gym before, you would realize how similar a penetration testing lab environment is to an indoor facility filled with climbing walls of varying difficulty. Just like how indoor rock-climbing gyms provide climbers with a controlled environment to exercise and practice their climbing skills, penetration testing labs provide cybersecurity professionals with an isolated environment to practice and perfect their hacking techniques. Both environments challenge users with various types of scenarios with increasing complexity and difficulty to push their limits. Given that these environments have been built to mimic real-world challenges and obstacles, we should expect these environments to evolve and grow in complexity so that users are presented with new challenges to solve.

In this section, we will discuss how we can further evolve and increase the complexity of the penetration...

Leveraging Generative AI for estimating penetration testing lab costs

The way we design our penetration testing lab environments can have a significant impact on the overall cost of running these labs in the cloud. Certain implementations and variations may require more resources than others, which would lead to increased costs. By carefully considering the architecture of our lab setup, we can identify opportunities to reduce costs without compromising the quality, performance, and stability of our penetration testing lab environment. Estimating the associated costs when running these environments is another crucial aspect as this allows security professionals (and teams) to plan their budget and maintain a sustainable lab setup in the long run.

In Chapter 6, Setting Up Isolated Penetration Testing Lab Environments on AWS, we prepared a lab setup where we can practice network pivoting techniques. In case you’ve forgotten already, here’s a simplified diagram showing...

Unleashing the power of AI-powered tools to accelerate automation script development

Being able to fully automate the creation and deletion of our penetration testing lab environment would help us significantly reduce the cost of running these lab environments in the cloud. While the potential benefits are undeniable, in reality, fully automating the preparation of lab environments is not as easy as it sounds. Coding automation scripts takes time, skill, and effort, and it may sometimes involve an entire team of experienced (and expensive) engineers to get the job done properly.

Maybe AI-powered tools can help! In addition to ChatGPT, there are many other AI-powered solutions available to help us significantly speed up the preparation of the automation scripts for building our penetration testing lab environments. In this section, we will take a closer look at how AI-powered tools such as GitHub Copilot, Amazon CodeWhisperer, and Tabnine can help us accelerate automation script...

Using AI-powered solutions to generate and explain IaC template code

In the previous chapters of this book, we manually prepared the Terraform template code for setting up various penetration testing lab environments on AWS, Azure, and GCP. If you’ve actually worked on the hands-on examples and solutions in the previous chapters, you are probably aware that it takes a significant amount of time to code and prepare these IaC templates from scratch! To accelerate the preparation of IaC template code, we can use AI-powered solutions to generate code automatically using the right set of prompts. In addition to this, we can use these tools to explain existing code as well.

In this section, we will take a closer look at how AI-powered solutions such as ChatGPT and GitHub Copilot Labs can be used to generate and explain IaC template code. You’ll see how we can use these tools to significantly speed up the process of reading and writing code.

Important note

Make sure...

Recognizing relevant considerations and practical strategies when building and automating lab environments

We are down to the last major section of this book! In the previous few sections of this chapter, we learned how to use various AI-powered solutions and tools to accelerate and automate relevant tasks when building lab environments. In addition to the strategies and solutions we have discussed already, we have a few more considerations and recommended practices we must take into account when building penetration testing lab environments in the cloud.

Here is a quick list of the things we should consider and plan for when designing lab environments:

• Identifying the purpose of the lab: Before designing and building a lab environment, it is important that we identify why we are building the lab in the first place. We need to know how the lab will be used as this will dictate the necessary resources and configurations required for the lab environment. For one thing, it...

Summary

In this chapter, we took a closer look at how we can increase the complexity and difficulty of the penetration testing lab environments we set up in the previous chapters of this book. In addition to this, we learned how to utilize various AI-powered solutions such as ChatGPT, GitHub Copilot, Amazon CodeWhisperer, and Tabnine to significantly speed up relevant tasks when building these vulnerable-by-design labs. These include estimating the cost of running these labs in the cloud, generating automation scripts and IaC templates, and explaining existing code written by other professionals. We ended the chapter by tackling relevant recommendations, considerations, and strategies when building penetration testing lab environments in the cloud.

You’ve finally reached the end of this book! Congratulations on completing all the chapters along with the hands-on examples and solutions. Close your eyes and take a moment to reflect on everything you have learned. I hope this...