Architecting Sovereign AI: Zero Trust Patterns for Data Control and Enterprise Guardrails

Your AI is running. Your data is leaving. Every external model call, every unsecured agent session, every RAG query that ignores the source system's access controls is a breach waiting for a discovery event. This book closes that gap with a complete, deployable architecture - not principles, not aspirational frameworks, but working code and verified patterns you can put into production. Architecting Sovereign AI builds the Sovereign AI Control Plane (SACP): a four-pillar enforcement architecture covering data sovereignty, model sovereignty, compute sovereignty, and governance sovereignty. It is the only book that combines SPIFFE workload identity, Open Policy Agent, AI gateway design, ACL-preserving RAG pipelines, and autonomous agent security into a single end-to-end implementation guide - with every chapter verified against real breach incidents, EU AI Act obligations, and NIST AI RMF requirements. By working through this book, you will: - Design a four-pillar sovereign AI architecture covering data, model, compute, and governance sovereignty - Deploy SPIFFE/SPIRE workload identity for LLM pipelines, agent sessions, and RAG pipeline workers - Build Open Policy Agent policies that enforce least-privilege access across data sources, model versions, and context window limits - Implement an AI gateway with five-stage enforcement: identity verification, injection scanning, OPA routing, PII redaction, and structured audit logging - Preserve document access control lists from SharePoint through vector database ingestion using ACL-preserving chunk metadata - Apply OWASP Agentic Top 10 controls and CSA Agentic Trust Framework maturity levels to autonomous agent deployments - Map your AI deployments to EU AI Act Article 10, NIST AI RMF, and CISA Zero Trust Maturity Model requirements with audit-ready documentation - Construct cryptographically signed, Merkle-anchored audit trails that satisfy EU AI Act Article 12 five-year retention requirements - Build a phased 90-day SACP implementation sprint that operates alongside existing AI systems without downtime > Each chapter works through a verified real-world incident - the Samsung ChatGPT leak, the European bank agent governance failure, the Vercel OAuth supply-chain breach - and shows exactly what architectural control would have prevented it. Code examples are complete and runnable in Python, Rego, bash, YAML, and FastAPI. No pseudocode. No theoretical diagrams that stop short of implementation. This book is for senior security architects, CISOs, AI platform leads, and DevSecOps engineers who are already running AI in production and need the enforcement architecture, not the introductory explanation. If your AI environment has reached production before your governance has, start at Chapter 10 and read forward. Build the control plane.