Self‑sovereign AI stacks combine on‑device LLM inference, privacy‑first retrieval, and cryptographic credentialing so users retain control of their data, models, and permissions. Promoted in recent conversations (including proposals from Vitalik Buterin) as a technical and governance direction, these stacks pair local semantic search and RAG with trusted execution and verifiable credentials to reduce data leakage and centralization risks. This topic is timely in 2026 because on‑device LLMs and secure enclaves have matured, regulatory pressure on data sovereignty has increased, and interoperability protocols for AI context (Model Context Protocol, MCP) are coalescing. Practical components include Local RAG and Minima for offline, on‑premises semantic search over PDFs and local files; Attestable MCP implementations that run MCP servers inside TEEs (e.g., Gramine/Intel SGX) to provide RA‑TLS remote attestation; Keycloak MCP Server for programmatic credential and identity management; Openfort for integrating smart‑wallet based auth and project infrastructure; and Archestra.AI for centralized orchestration, observability, and policy control across MCP endpoints. Taken together, these tools illustrate a pragmatic stack: index and query data locally (Local RAG/Minima), serve model contexts with attested integrity (Attestable MCP), manage identities and credentials (Keycloak MCP), enable cryptographically backed actions via smart wallets (Openfort), and operate at scale with governance tooling (Archestra.AI). The result is an ecosystem that emphasizes verifiable device‑level guarantees, minimal external exposure of sensitive context, and programmable credential flows—key capabilities for privacy‑preserving AI assistants and enterprise deployments seeking compliance and user sovereignty.