AI-Driven Enterprise Security : Machine Learning Strategies for Threat Detection, Zero Trust Architecture, and Automated Cyber Defense
Language: English
Published by Independently Published Jun 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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- Title
- AI-Driven Enterprise Security : Machine Learning Strategies for Threat Detection, Zero Trust Architecture, and Automated Cyber Defense
- Author
- Victor Cipher
- Publisher
- Independently Published Jun 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798180793065
- Item weight
- 446 grams
- Dimensions
- 279x216x10 mm
AI-Driven Enterprise Security: Machine Learning Strategies for Threat Detection, Zero Trust Architecture, and Automated Cyber Defense is a practical guide for modern security teams facing an era of faster, smarter, and more persistent attacks.
Today’s enterprise environments are more complex than ever. Traditional perimeter defenses, signature-based tools, and manual response processes are no longer enough to keep pace with advanced threats, cloud-native risks, insider activity, and sprawling hybrid infrastructures. This book shows how to build a more adaptive security posture by combining machine learning, zero trust principles, and automated cyber defense into one coherent strategy.
Inside, you will discover how AI can strengthen threat detection by identifying anomalies, exposing hidden patterns, reducing false positives, and helping security teams focus on what matters most. You will learn how machine learning can support earlier threat discovery, faster triage, and more intelligent decision-making across the security lifecycle. Instead of relying solely on reactive tools, this book explains how to move toward predictive, behavior-driven defense.
You will also gain a clear understanding of zero trust architecture and why it has become essential in modern enterprise security. Rather than assuming trust based on network location, zero trust enforces continuous verification, least privilege access, and identity-aware controls across users, devices, applications, and workloads. This book connects those principles to real-world enterprise needs, showing how AI can add context and intelligence to access decisions, segmentation, and policy enforcement.
A major focus of the book is automated cyber defense. Security teams are under pressure to respond quickly, consistently, and at scale. Automation helps close the gap between detection and response by enabling actions such as isolating compromised assets, blocking suspicious traffic, triggering incident workflows, and enforcing controls at machine speed. This book explores how automation can reduce dwell time, limit damage, and ease the burden on overworked SOC teams.
Whether you are a CISO, security architect, SOC analyst, cloud security professional, or IT leader, this book is designed to help you think more strategically about security modernization. It offers a forward-looking framework for protecting enterprise systems in environments shaped by remote work, cloud adoption, SaaS platforms, mobile endpoints, and constantly shifting attack surfaces.
More than a theory-driven overview, this book is built around actionable security thinking. It helps readers understand how AI, zero trust, and automation work best when deployed together rather than as isolated initiatives. The result is a stronger, more scalable, and more resilient defense model that can evolve with emerging threats.
If your organization is looking to improve visibility, reduce alert fatigue, strengthen identity-based controls, and accelerate incident response, this book provides the concepts and strategies to move in that direction with confidence.
AI-Driven Enterprise Security is not just about protecting systems. It is about building a smarter security foundation for the future—one that can detect, decide, and defend faster than the threats it faces.
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