Private AI for Business: The Complete 2026 Guide for Regulated Sectors
67% of companies cannot use ChatGPT, Copilot, or Gemini. If you work in legal, healthcare, finance, insurance, or industrial sectors, this guide explains how to deploy AI while keeping complete control over your sensitive data.
Why Most Companies Can't Use Public AI
Problem 1: Your Data is Stored in the US
When you use ChatGPT, your conversations are stored on American servers. GDPR violations can cost 4 to 7% of global revenue. Real example: Samsung banned ChatGPT after engineers leaked source code three times in 20 days.
Problem 2: Costs Are Fragmented
Most companies subscribe to 5-10 different AI tools costing 50-80 euros per user monthly. API billing varies by 300% between models. 35% of SMEs cite cost as their primary barrier.
Problem 3: Generic AI Doesn't Know Your Business
ChatGPT has no access to your documents, client history, or company terminology. Legal teams waste 20 hours per week re-explaining context to AI.
What is Private AI?
Private AI gives you ChatGPT-level capabilities deployed in an environment you control. Your data never leaves your infrastructure, and you access multiple AI models from one platform.
The core principle: You should never choose between performance and confidentiality. Private AI gives you both.
The Three Deployment Approaches
Approach 1: On-Premise (Full Control)
You install everything on your own servers. Maximum security, data never leaves your premises.
Cost at VaultAI: 20 euros/user/month
Timeline: 1-4 weeks
Best for: Banks, insurance, government (100+ employees)
Approach 2: Private Cloud (Sovereign Hosting)
Dedicated cloud environment in France/EU with contractual privacy guarantees.
Cost at VaultAI: 45 euros/user/month (no upfront cost)
Timeline: 24 hours
Best for: Law firms, clinics, consulting (5-250 employees)
Approach 3: Hybrid
Critical data on-premise, less sensitive workloads in private cloud.
Cost: 25-40 euros/user/month blended
Timeline: 1-3 weeks
Best for: Hospitals, pharma, manufacturing
How to Build Your Own Private AI System
Here's the technical roadmap if you want to deploy private AI yourself.
Step 1: Choose Your Infrastructure (Week 1)
On-premise: Buy servers with 8-32 GB RAM, 8+ CPU cores, 500GB+ SSD. Install Ubuntu Server or similar.
Cloud: Rent dedicated servers from OVH, Scaleway, or AWS (French providers). Choose instances in France/EU datacenters only.
Step 2: Set Up Your Database with Security (Week 1-2)
Install PostgreSQL (or another database system) with Row-Level Security enabled. This ensures data isolation at the database level, not just application level.
Create policies that filter every query by tenant ID. Even if your application code has bugs, the database prevents data leaks between companies or users.
Set up a vector database for document storage. Options: Qdrant (open-source, self-hosted), Weaviate, or Milvus. This stores your company documents as mathematical vectors for semantic search.
Step 3: Deploy the RAG System (Week 2-3)
RAG (Retrieval-Augmented Generation) makes AI know your business. Here's how it works:
Split your documents into 500-character chunks with 50-character overlap. Convert each chunk to a vector using OpenAI's embedding API (text-embedding-3-small model, costs 0.02 cents per 1000 tokens). Store vectors in your database with metadata (source, date, author).
When users ask questions: Convert their question to a vector, search your database for the 5 most similar document chunks, inject those chunks as context in the AI prompt, send to the LLM for answer generation.
Result: AI answers based on YOUR documents, not generic internet training.
Step 4: Connect Your Tools (Week 3-4)
Build connectors to your existing systems. Most have APIs you can use.
Google Drive: Use Google Drive API to sync documents automatically. Refresh vectors when documents change.
Notion: Notion API provides access to your workspace. Index pages and databases.
Meeting recordings: If you record meetings (Zoom, Teams), transcribe them using Whisper API and add to your knowledge base.
CRM: Connect to Salesforce, HubSpot, or custom CRMs via their APIs to give AI access to client history.
Step 5: Set Up a Secure AI Provider (Week 4)
Don't call OpenAI/Anthropic directly. If you want to use their models, we recommend going through Azure or AWS.
For a 100% European solution, we recommend using Scaleway or OVH. The models available on these platforms are less powerful but more secure!
This gives you GDPR Article 22 compliance (right to explanation) and AI Act traceability requirements.
Step 6: Implement Security Layers (Week 4-5)
Input sanitization: Remove dangerous patterns from user input before sending to AI. Block phrases like "ignore previous instructions," "system:", etc.
Rate limiting: Restrict users to 60-70 requests per minute to prevent abuse and cost explosions.
Prompt injection defense: Structure your prompts with clear boundaries. Mark system instructions as IMMUTABLE, trusted context as VERIFIED, and user input as UNTRUSTED.
API key management: Never hardcode keys. Use environment variables or secret management systems.
Step 7: Build the User Interface (Week 5-6)
Create a simple chat interface with a web framework (React, Vue, or similar). Users type questions, see streaming responses like on ChatGPT, and get source citations for every answer.
Add model selection: let users switch between GPT-4 (complex reasoning), GPT-4o-mini (fast/cheap), Claude (writing), Mistral (French language).
Step 8: Optimize Costs (Ongoing)
Smart model routing: Use cheap models (GPT-4o-mini at 0.15 per million tokens) for simple questions, expensive models (GPT-4o at 2.50 per million tokens) only for complex reasoning. This cuts costs 60-90%.
Aggressive caching: Cache responses for 1 hour using Redis. If someone asks "What's our vacation policy?" and 10 people ask the same question that day, you only pay for one API call.
Fallback system: If OpenAI is down, automatically switch to Anthropic Claude. If Claude is down, try Mistral. Never have a single point of failure.
Step 9: Compliance and Certifications (Months 3-6)
For sales to enterprises, you'll need certifications:
ISO 27001 (6-12 months, 25-50K euros): Information security management. Required for most large clients.
HDS (for healthcare, 3-6 months): French health data hosting certification. Mandatory for patient records.
SOC 2 Type II (6-12 months, 30-80K euros): Alternative to ISO 27001, common in US markets.
Total Timeline: 4-6 Months with 2-3 Engineers
Architecture design: 2-4 weeks
RAG implementation: 4-6 weeks
Security hardening: 4-6 weeks
Tool integrations: 2-3 weeks each
Testing and compliance: 4-6 weeks
Total Cost: 150-300K euros First Year
Infrastructure: 50-100K euros (on-premise) or 30-60K euros (cloud hosting)
Engineer salaries: 100-200K euros for 2-3 people for 6 months
API costs: 500-2000 euros/month depending on usage
Certifications: 25-50K euros for ISO 27001
Ongoing: 150-300K euros Annually
Maintenance, updates, new integrations, user support, security patches, compliance audits.
Key Regulatory Deadlines 2026
EU AI Act (June 2025): High-risk AI systems require complete traceability. Every decision needs audit logs.
GDPR enforcement: Increased scrutiny of US data transfers. CNIL issuing warnings to companies using American AI services.
DORA (Finance): Detailed AI risk management required January 2025.
HDS v2 (Healthcare): Mandatory for any system processing patient data.
For Teams Who Want It Now Without the Build Time
Building private AI yourself gives you maximum control. But it requires months of intense development and approximately 150-300K euros in the first year.
If your company needs secure AI immediately without the development time, that's why we built VaultAI.
We handle everything described above:
✅ Multi-model access (GPT, Claude, Mistral, Llama)
✅ RAG with pre-built connectors (Drive, Notion, CRM, meetings)
✅ Security architecture (RLS, audit logs, encryption)
✅ On-premise or French sovereign cloud deployment
✅ GDPR and AI Act compliance built-in
Deployment: 24 hours
Pricing: 20-45 euros/user/month depending on deployment mode
The choice: Build it yourself (4-6 months, full control, 150-300K euros first year) or deploy VaultAI (24 hours, 45 euros/user/month).
Either way, don't let another quarter pass using AI tools that put your data at risk. The technology exists to do this properly. 2026 is the year to make it happen!