How to Implement Generative AI and Local RAG in Compliance with ISO 27001 and ISO 9001

Introduction

As Generative Artificial Intelligence becomes indispensable in the enterprise, a critical regulatory challenge emerges: how to process confidential corporate data without violating information security regulations? Sending audit reports, financial data, or technical specifications to proprietary cloud APIs can pose a direct risk of leaking intellectual property.

The solution to this impasse lies in Data Sovereignty combined with Local AI. In this instructive article, we demonstrate how to design a local RAG (Retrieval-Augmented Generation) pipeline running on CPU (using Ollama and Llama 3) in full compliance with ISO 27001 (Information Security) and ISO 9001 (Quality Management) standards.

🔒 Aligning Local AI with ISO 27001 Controls

The ISO/IEC 27001 standard demands strict control over where data resides and who has access to it. By adopting a local AI architecture, we directly address several clauses of the standard:

A.8.1.1 (Inventory of Assets): Data never leaves the company's own infrastructure, facilitating the traceability and control of information assets.
A.18.1.4 (Data Protection and Privacy): Because requests do not travel across the public internet to third-party servers, we eliminate the risk of intercepted traffic or the unauthorized use of data to train commercial external models.

⚙️ The CPU-Based Local RAG Architecture

To comply with ISO 9001 (Quality and Repeatability), we need a consistent data retrieval process. The recommended architecture is structured into 4 internal layers:

[Internal Documents (PDF/txt)] 
              │
              ▼
[Markdown Conversion / Chunking]
              │
              ▼
[Local Vector Indexing (PostgreSQL + pgvector)]
              │
              ▼
[Python Orchestrator (LangChain)] <───> [Local LLM (Ollama / Llama 3)]

Step 1: Qualified Document Ingestion

In compliance with ISO 9001, ensure that input files pass through a data sanitization routine (removing PII - Personally Identifiable Information) before being fragmented into chunks.

Step 2: Local Vector Database

Use PostgreSQL with the pgvector extension on a local server. This unifies your existing relational database with vector search, eliminating the need to manage additional cloud services.

Step 3: Local LLM Execution via Ollama

Ollama allows you to spin up advanced language models (like Llama 3 or Mistral) locally. To optimize hardware usage on standard servers without dedicated GPUs, utilize 4-bit quantized models (Q4_K_M), which run with high efficiency directly on the CPU.

Bring the model up without leaving the machine

The control an auditor wants (ISO 27001 and, in practice, SOC 2 CC6) is easy to show: the process listens only on localhost and the model was pulled to local disk.

ollama pull llama3
ollama pull nomic-embed-text
ss -ltnp | grep 11434
curl -s http://127.0.0.1:11434/api/generate \
  -d '{"model":"llama3","prompt":"List the access controls for the production environment.","stream":false}'

If ss shows 127.0.0.1:11434 and not 0.0.0.0:11434, the data does not cross the network. That is the screenshot I take to the audit, next to the model hash from ollama list.

📈 Conclusion and Audit Benefits

Presenting a self-hosted AI system during a compliance audit drastically reduces the scope of risks assessed by auditors. You can prove end-to-end that the company's artificial intelligence operates under the same physical and logical security policies that already govern your traditional databases.