5) Observability that doesn’t violate privacy

You need logs for debugging and audits, but logs can become your biggest data exposure.

Patterns:

• Store hashed user identifiers
• Log metadata by default (latency, token counts), not full prompts
• Allow “break-glass” full logging only with approvals and short retention
• Separate security audit logs from application logs

6) Safety controls and data loss prevention

Sovereign private AI still needs guardrails:

• PII detection (pre- and post-processing)
• Secret scanning (API keys, tokens)
• Output filtering for regulated content
• Prompt-injection defenses (especially in RAG)

Diagram (described): A layered architecture diagram. From top to bottom: “Client Apps” -> “AI Gateway (AuthZ, DLP, Rate Limits)” -> “LLM Inference (GPU nodes)” and “RAG Service” -> “Vector DB (in-region)” + “Document Store (in-region)” -> “KMS/HSM.” A side channel shows “Audit Logs (WORM storage)”. Everything is enclosed in a box labeled “Sovereign Region / On-Prem Boundary.”

Practical Application: Building a Private RAG Service (Code + Config)

Below is a minimal but realistic example: a private RAG API using FastAPI, PostgreSQL + pgvector, and a local model endpoint (could be vLLM or another in-cluster runtime). The goal is to keep documents, embeddings, and inference in your controlled environment.

1) Postgres + pgvector (sovereign vector store)

SQL (schema):

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id UUID PRIMARY KEY,
  title TEXT NOT NULL,
  content TEXT NOT NULL,
  created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE TABLE document_embeddings (
  document_id UUID REFERENCES documents(id) ON DELETE CASCADE,
  chunk_id INT NOT NULL,
  chunk_text TEXT NOT NULL,
  embedding vector(768) NOT NULL,
  PRIMARY KEY (document_id, chunk_id)
);

-- IVFFlat index for approximate nearest neighbor search
CREATE INDEX ON document_embeddings USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

2) FastAPI RAG service (simplified)

Python (RAG endpoint):

import os
import uuid
import asyncpg
import httpx
from fastapi import FastAPI, Depends

DATABASE_URL = os.environ["DATABASE_URL"]
EMBEDDINGS_URL = os.environ["EMBEDDINGS_URL"]  # local embedding model endpoint
LLM_URL = os.environ["LLM_URL"]                # local LLM inference endpoint

app = FastAPI()

async def get_db():
    conn = await asyncpg.connect(DATABASE_URL)
    try:
        yield conn
    finally:
        await conn.close()

async def embed(text: str) -> list[float]:
    async with httpx.AsyncClient(timeout=30) as client:
        r = await client.post(EMBEDDINGS_URL, json={"text": text})
        r.raise_for_status()
        return r.json()["embedding"]

async def retrieve(conn, query_embedding: list[float], k: int = 5):
    rows = await conn.fetch(
        """
        SELECT chunk_text
        FROM document_embeddings
        ORDER BY embedding <=> $1
        LIMIT $2
        """,
        query_embedding,
        k,
    )
    return [r["chunk_text"] for r in rows]

async def generate_answer(prompt: str) -> str:
    async with httpx.AsyncClient(timeout=60) as client:
        r = await client.post(LLM_URL, json={"prompt": prompt})
        r.raise_for_status()
        return r.json()["text"]

@app.post("/ask")
async def ask(question: str, db=Depends(get_db)):
    q_emb = await embed(question)
    contexts = await retrieve(db, q_emb, k=5)

    context_block = "\n\n".join([f"- {c}" for c in contexts])
    prompt = (
        "You are a helpful assistant. Use only the context below. "
        "If unsure, say you don't know.\n\n"
        f"Context:\n{context_block}\n\n"
        f"Question: {question}\nAnswer:"
    )

    answer = await generate_answer(prompt)
    return {"answer": answer, "sources": contexts}

3) Kubernetes network policy (keep traffic inside)

A common sovereignty failure mode is unintended egress. Use default-deny egress and explicitly allow only what’s needed.

Kubernetes NetworkPolicy (example):

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: rag-default-deny-egress
spec:
  podSelector:
    matchLabels:
      app: rag-service
  policyTypes:
  - Egress
  egress: []

Then add a specific allow policy to reach only in-cluster model services and the database.

4) Audit logging pattern (WORM + minimal sensitive data)

For regulated environments, consider WORM storage (write once, read many) for audit trails.

Log:

• request id
• user id (pseudonymized)
• model id/version
• dataset id
• timestamps
• policy decisions (e.g., “PII redaction applied: yes/no”)

Avoid storing full prompts by default.

Diagram (described): A sequence diagram. Actor “User” calls “AI Gateway.” Gateway calls “RAG Service.” RAG calls “Embeddings Service” then “Vector DB,” then calls “LLM Runtime.” Gateway writes an audit event to “WORM Audit Store.” No arrows leave the “Sovereign Boundary.”

Best Practices: Patterns That Hold Up in Production

1) Classify data and map it to model routes

Not all data needs the same treatment. Implement routing rules:

• Public/low-risk: can use broader model set (still governed)
• Confidential: private inference only
• Regulated (PHI/PCI): private inference + stricter logging + shorter retention

This reduces cost while preserving sovereignty where it matters.

2) Use “policy as code” at the gateway

Encode rules in version-controlled policy (OPA/Rego or similar):

• Which teams can access which models
• Which datasets can be used for retrieval
• Which tools/functions can be invoked

This is how you make governance auditable and repeatable.

3) Separate duties and isolate environments

• Separate dev/test/prod with different keys and datasets
• Restrict who can deploy models vs. who can access data
• Use dedicated namespaces/projects per business unit

4) Control egress aggressively

Sovereignty is often broken by:

• hidden telemetry
• package downloads at runtime
• external model fallbacks

Use:

• private container registries
• pinned dependencies
• egress proxies with allowlists
• runtime policy (e.g., Kubernetes admission controls)

5) Manage model lifecycle like any other critical dependency

• Track model versions and hashes
• Maintain evaluation baselines (accuracy, toxicity, leakage)
• Roll out with canaries
• Keep a rollback path

6) Harden RAG against prompt injection

RAG adds a new attack surface: documents can contain malicious instructions.

Defenses:

• Strip or tag untrusted content
• Use a system prompt that explicitly ignores instructions in retrieved text
• Use structured retrieval (metadata + citations)
• Consider a “retrieval firewall” that filters risky chunks

7) Encrypt and minimize embeddings and caches

• Encrypt vector DB at rest
• Minimize retention of query logs
• Consider per-tenant vector indexes for strong isolation

Limitations: Tradeoffs You Should Acknowledge Up Front

Private AI and sovereignty controls are powerful, but not free.

1. Cost and capacity planning: GPUs are expensive, and peak demand can force overprovisioning.
2. Operational complexity: You now own model serving, patching, scaling, and incident response.
3. Model quality gaps: Some open/self-hosted models may lag top proprietary models for certain tasks.
4. Evaluation burden: You must prove the system is safe and accurate for your use case.
5. Sovereignty isn’t binary: “In-region” isn’t enough if support access, subprocessors, or key control violate your requirements.

A good strategy is to start with a narrow, high-value use case (e.g., internal knowledge assistant) and expand once governance patterns are proven.

Further Reading: Authoritative Resources

• GDPR portal and key concepts: https://gdpr.eu/
• NIS2 Directive overview: https://digital-strategy.ec.europa.eu/en/policies/nis2-directive
• ISO/IEC 27001 (ISMS standard): https://www.iso.org/isoiec-27001-information-security.html
• NIST AI Risk Management Framework (AI RMF 1.0): https://www.nist.gov/itl/ai-risk-management-framework
• OWASP Top 10 for LLM Applications (prompt injection, data leakage, etc.): https://owasp.org/www-project-top-10-for-large-language-model-applications/
• Cloud Security Alliance (AI and cloud governance resources): https://cloudsecurityalliance.org/

Conclusion

Private AI is ultimately an engineering discipline: define the sovereignty boundary, keep inference and retrieval inside it, and enforce policy at the gateway with auditable controls. Start by mapping your data classes to allowed processing locations, implement strict egress control, and make logging privacy-aware by default. Then iterate: add RAG, add evaluation pipelines, and harden against prompt injection as you expand.

If you’re designing a private AI platform and want a second set of eyes on your sovereignty boundary, control plane, and RAG architecture, cabrillo_club can help you turn requirements into a production-ready reference design.