13 Generative AI in cybersecurity metrics

 

This chapter covers

  • Using generative AI to enhance cybersecurity metrics
  • Open source tools for AI analysis
  • Generating synthetic data for cybersecurity scenarios
  • Transforming datasets into actionable insights with AI

Generative AI has opened new possibilities in cybersecurity metrics, making it easier to analyze complex datasets, uncover hidden insights, and communicate findings effectively. This chapter explores 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 LM Studio vs. Ollama

13.2.4 Why we chose LM Studio for this chapter

13.3 A note on LLMs

13.4 Prompt engineering

13.4.1 Why prompt engineering matters

13.5 Prompt engineering in cybersecurity

13.5.1 Basics of prompt engineering

13.5.2 Open source vs. cloud API integration

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