Overview

This book examines governance structures for enterprise AI deployments that prioritize data sovereignty through self-hosted intelligence platforms and locally executed models. It addresses compliance requirements under evolving 2026 regulatory landscapes, including frameworks such as ISO/IEC 42001, NIST AI RMF, and region-specific data residency mandates, while focusing on audit-ready architectures for on-premises and private environments. Key topics include risk management for local large and small language models, implementation of traceability and explainability controls, alignment with sovereignty principles to prevent data exfiltration, and operational audit mechanisms tailored to regulated industries. The content emphasizes technical controls, policy integration, and continuous monitoring practices suitable for organizations deploying autonomous or agentic AI systems without reliance on external cloud providers. Designed for AI architects, compliance officers, chief data officers, and security professionals with prior experience in AI deployment or enterprise governance, this volume provides detailed analysis and practical considerations rather than introductory concepts. Professionals seeking to implement robust, auditable governance for sovereign AI initiatives in 2026 will find structured guidance here. Acquire this reference to strengthen your organization's approach to compliant, self-hosted AI operations.

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