You're reading from Artificial Intelligence for Cybersecurity Develop AI approaches to solve cybersecurity problems in your organization

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781805124962

Length 358 pages

Edition 1st Edition

Concepts

Data Governance

Authors (4):

Bojan Kolosnjaji

Apostolis Zarras

Huang Xiao

Peng Xu

View More author details

Table of Contents (27) Chapters

Preface

1. Part 1: Data-Driven Cybersecurity and AI FREE CHAPTER

2. Chapter 1: Big Data in Cybersecurity

3. Chapter 2: Automation in Cybersecurity

4. Chapter 3: Cybersecurity Data Analytics

5. Part 2: AI and Where It Fits In

6. Chapter 4: AI, Machine Learning, and Statistics - A Taxonomy

7. Chapter 5: AI Problems and Methods

8. Chapter 6: Workflow, Tools, and Libraries in AI Projects

9. Part 3: Applications of AI in Cybersecurity

10. Chapter 7: Malware and Network Intrusion Detection and Analysis

11. Chapter 8: User and Entity Behavior Analysis

12. Chapter 9: Fraud, Spam, and Phishing Detection

13. Chapter 10: User Authentication and Access Control

14. Chapter 11: Threat Intelligence

15. Chapter 12: Anomaly Detection in Industrial Control Systems

16. Chapter 13: Large Language Models and Cybersecurity

17. Part 4: Common Problems When Applying AI in Cybersecurity

18. Chapter 14: Data Quality and its Usage in the AI and LLM Era

19. Chapter 15: Correlation, Causation, Bias, and Variance

20. Chapter 16: Evaluation, Monitoring, and Feedback Loop

21. Chapter 17: Learning in a Changing and Adversarial Environment

22. Chapter 18: Privacy, Accountability, Explainability, and Trust – Responsible AI

23. Part 5: Final Remarks and Takeaways

24. Chapter 19: Summary

25. Index

Why subscribe?

26. Other Books You May Enjoy

Summary

In this chapter, we presented the content of data quality and its usage in the AI and LLM era first. We then explored several examples of good and bad data quality. Lastly, we showed how to write Python code to practice good data quality. Data quality refers to the overall utility of data based on attributes such as accuracy, completeness, reliability, relevance, and timeliness. It is an assessment of how well-suited data is for making decisions, driving processes, and achieving business objectives.

We hope you can grasp the features of good and poor data quality in your practical applications.

In the next chapter, several topics related to data quality will be presented. You will learn about the connections among data, such as correlation, causation, bias, and so on.