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It covers foundational concepts, historical evolution, and current trends shaping the landscape of threat detection, prediction, and response.
Readers will find in-depth discussions on designing secure data collection frameworks, data governance, privacy, and compliance, as well as practical insights into machine learning techniques such as supervised, unsupervised, and deep learning architectures.
The book also delves into anomaly detection, feature engineering, model optimization, and managing model performance over time.
Case studies illustrate successful threat prediction implementations, while strategic guidance addresses deploying AI solutions across enterprise networks, scaling, and maintaining accuracy.
Emerging technologies like reinforcement learning, federated learning, and quantum computing are examined for their potential impact.
Designed for security professionals, data scientists, and organizational leaders, this book provides the knowledge necessary to leverage AI for proactive threat hunting, automated responses, and informed security policymaking in an increasingly complex digital world.
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With over 24 years of experience in the field of cyber security, the author combines deep expertise with a passion for safeguarding digital landscapes. A seasoned professional, they have dedicated their career to developing and implementing robust security controls that protect organizations against evolving threats. Known for their insightful perspectives and hands-on approach, they are committed to empowering others through knowledge and best practices in the ever-changing realm of cyber security
