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

The regulatory landscape

The regulatory landscape for AI in cybersecurity data analytics is still evolving. As AI-powered solutions become more sophisticated, regulators must develop new regulations to address challenges associated with this technology.

Data privacy is one of the most critical regulatory challenges associated with AI in cybersecurity. AI-powered solutions often collect and analyze large amounts of data, which raises concerns about privacy and data protection. For example, AI-powered malware detection solutions may collect data about users’ browsing behavior, which could be used to track users’ online activity.

To address this challenge, regulators are developing new regulations requiring organizations to obtain user consent before collecting their data. They also require organizations to implement appropriate technical and organizational measures to protect the privacy of users’ data.

Another important regulatory challenge is transparency...