Private AI Architecture: Hosting Llama 3 for Enterprise Security

The "ChatGPT Problem" for Enterprise

Employees love Generative AI. Security teams hate it. Every time an employee pastes a legal contract or patient record into ChatGPT, that data leaves your perimeter.

The solution isn't to ban AI. It's to build a Private AI Airlock.

Architecture: The "Private AI Airlock"

We deploy Open Source LLMs (like Meta's Llama 3 or Mistral) inside your own Virtual Private Cloud (AWS VPC / Azure VNet). No data ever touches trusted APIs.

The Stack components:

1. The LLM Host: An AWS g5.2xlarge instance (NVIDIA A10G GPU) running a containerized version of Llama 3-70B.
2. The Vector Database: We use Pinecone (Enterprise Tier) or a self-hosted Milvus instance to store your proprietary knowledge (PDFs, Wikis, Codebase).
3. The Context Window: When a user asks a question, our system retrieves relevant snippets from your Vector DB and feeds them to the LLM locally.

Why On-Prem vs OpenAI?

Feature	OpenAI (GPT-4)	Private Llama 3 (Ortem Airlock)
Data Privacy	Data sent to US servers. Used for training (unless opted out).	Zero Leakage. Data never leaves your VPC.
Cost	Per-token pricing. Unpredictable at scale.	Fixed Cost. You pay for the GPU instance hours.
Customization	Fine-tuning is expensive and limited.	Full Control. Fine-tune on your specific industry jargon.
Latency	Variable API latency.	Low Latency. Optimized local inference.

Use Cases for Private AI

1. Legal Document Review

• Scenario: Analyzing M&A contracts for risk clauses.
• Risk: Contracts contain highly confidential financial data.
• Solution: An Air-gapped Llama 3 model extracts clauses without internet access.

2. Medical Record Summarization (HIPAA)

• Scenario: Summarizing patient history for doctors.
• Risk: PII violations if sent to public APIs.
• Solution: A self-hosted model running on a HIPAA-compliant AWS setup. Ortem signs a BAA (Business Associate Agreement).

3. Internal Coding Assistant

• Scenario: A "Copilot" that knows your proprietary legacy codebase.
• Risk: Pasting IP into public code assistants.
• Solution: A fine-tuned CodeLlama model indexed on your private GitHub repos.

Implementation Roadmap

Building a Private AI system requires more than just downloading a model.

1. Data Sanitization: Cleaning your documents (OCR, PII redaction).
2. Infrastructure Setup: Provisioning GPU clusters and Vector DBs.
3. Eval Framework: Testing the model against your truth set (RAG Evaluation).

Secure Your Intelligence

Don't let your data be the product. Contact our AI Solutions Architects to design your Private AI Airlock today. Schedule a consultation to discuss your private LLM requirements.