Implementing LLMs Without Leaking IP

The productivity gains from Large Language Models (LLMs) like ChatGPT are undeniable. But for businesses with proprietary data, the risk of "leaking" trade secrets into a public model is a massive security hole.

The "Training Data" Problem

When you use the free version of most public AI tools, your inputs are fair game for training future models. If your lead developer pastes a snippet of your proprietary algorithm into ChatGPT to fix a bug, that code effectively becomes public knowledge for the AI.

We've seen cases where:

• Sales teams paste confidential client lists to "format them."
• HR paste internal salary bands to "write a job description."
• Executives paste board meeting minutes to "summarise them."

How to Safely Deploy AI

You don't have to ban AI (and lose the competitive advantage). You just need Enterprise Guardrails.

VCTO operates on a "Private Instance" model. When you chat with VCTO, your data is processed via API agreements that explicitly opt-out of model training. Your data remains yours. It is used to generate the answer, and then discarded from the model's short-term memory.

Context-Aware, Not Public-Aware

The other risk of public LLMs is hallucination based on generic internet data. VCTO is "grounded" in your specific business context. We index your documentation (PDFs, Confluence, Codebase) into a secure vector database.

This means when you ask "How do we handle refunds?", it doesn't give you a generic answer from Wikipedia—it gives you your policy, citing the specific page in your employee handbook.