"Explainable AI for Cloud Security Automation": Building Trustworthy Defenses

The book "Explainable AI for Cloud Security Automation" explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) into modern cloud environments with a focus on explainability, transparency, and trust. It highlights how automated security systems powered by AI can enhance threat detection, incident response, access control, anomaly detection, and compliance management in dynamic cloud infrastructures.

A central theme of the book is that AI-based security models must not only be accurate but also interpretable to build confidence among cloud administrators, auditors, and regulatory bodies. It covers frameworks and techniques such as XAI (Explainable AI), SHAP, LIME, counterfactual reasoning, interpretable deep learning, and their applications in cloud monitoring, intrusion detection, vulnerability management, and zero-trust architectures.

The book addresses challenges like data privacy, scalability, adversarial attacks, and regulatory compliance, while discussing case studies and practical deployments. It emphasizes balancing automation efficiency with explainability, enabling secure, resilient, and accountable cloud ecosystems.

This comprehensive guide provides practical insights for security professionals, cloud architects, and AI practitioners who need to implement trustworthy AI-driven security solutions in cloud environments. The content bridges the gap between theoretical AI concepts and real-world security applications.

Whether you're looking to enhance your cloud security posture with AI or understand how to make AI systems more transparent and trustworthy, this book provides the essential knowledge and practical insights needed to succeed in the era of explainable AI for cloud security.

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