13 Generative AI in cybersecurity metrics

Generative AI has opened up new possibilities in cybersecurity metrics, making it easier to analyze complex datasets, uncover hidden insights, and communicate findings effectively. In this chapter, we will explore how AI can become a practical tool in your cybersecurity toolkit. Instead of focusing on theoretical concepts, we’ll demonstrate how you can use AI to solve real-world problems, such as assessing risk, monitoring compliance, and evaluating incident trends. Whether you're a cybersecurity professional or someone new to the field, this chapter will show you how to apply Generative AI in meaningful ways, using simple tools and interactive exercises. By the end, you'll see how AI can help you simplify the challenging work of analyzing cybersecurity metrics and help you make informed decisions for your organization.

13.1 Understanding generative AI

13.2 Open-source generative AI alternatives

13.2.1 LM Studio

13.2.2 Ollama

13.2.3 Comparing LMStudio with Ollama

13.2.4 Why I chose LMStudio for this chapter

13.3 A note on LLM Models

13.4 Prompt engineering

13.4.1 Why prompt engineering matters

13.5 Prompt engineering in cybersecurity

13.6 Generating and analyzing cybersecurity data

13.6.1 Generating synthetic cybersecurity data

13.6.2 Analyzing the output

13.7 Enhancing reporting and visualization

13.7.1 Failed login reasons reporting

13.8 Automating incident response and trend analysis

13.8.1 Setting the context

13.9 Exploring generative AI

13.9.1 Learning resources

13.9.2 Inspiration to experiment

13.9.3 Your next steps

13.10 Limitations and ethical considerations

13.11 Summary