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A CISO Guide to Cyber Resilience

You're reading from   A CISO Guide to Cyber Resilience A how-to guide for every CISO to build a resilient security program

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781835466926
Length 238 pages
Edition 1st Edition
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Author (1):
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Debra Baker Debra Baker
Author Profile Icon Debra Baker
Debra Baker
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1: Attack on BigCo FREE CHAPTER
2. Chapter 1: The Attack on BigCo 3. Part 2: Security Resilience: Getting the Basics Down
4. Chapter 2: Identity and Access Management 5. Chapter 3: Security Policies 6. Chapter 4: Security and Risk Management 7. Chapter 5: Securing Your Endpoints 8. Chapter 6: Data Safeguarding 9. Chapter 7: Security Awareness Culture 10. Chapter 8: Vulnerability Management 11. Chapter 9: Asset Inventory 12. Chapter 10: Data Protection 13. Part 3: Security Resilience: Taking Your Security Program to the Next Level
14. Chapter 11: Taking Your Endpoint Security to the Next Level 15. Chapter 12: Secure Configuration Baseline 16. Chapter 13: Classify Your Data and Assets 17. Chapter 14: Cyber Resilience in the Age of Artificial Intelligence (AI) 18. Index 19. Other Books You May Enjoy

Summary

This chapter discussed cyber resilience in the age of artificial intelligence (AI) and addressed various concerns related to AI in cybersecurity. It highlights both the positive and negative aspects of AI’s impact on cybersecurity.

The positive aspects include how machine learning (ML) and AI can enhance cybersecurity tools and products by introducing capabilities such as predictive analytics, pattern recognition, and automated threat response. AI can also help improve threat analysis and reduce false positives, enhancing the efficiency of cybersecurity efforts.

However, the negative aspects involve the risks associated with widespread AI use. These risks include the potential misuse of AI for hacking, data poisoning, and privacy concerns. It’s essential to implement guardrails for AI input ingestion, validate data, and maintain human oversight from development to training and ongoing monitoring to prevent model poisoning.

Responsible AI development,...

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