Data Privacy in the AI Era: Ensuring Your Custom GPT is Compliant
Last month, a fintech CTO asked us to audit their internal GPT. Within 10 minutes, we found that every customer SSN processed through their bot was being logged in plain text in OpenAI's default conversation history. They had no idea.
"If we upload our data, does ChatGPT learn from it?" This is the #1 question we get from CTOs. The uncomfortable truth is: it depends entirely on how you built it. The answer has changed significantly with OpenAI's enterprise updates, and most teams are still operating on outdated assumptions.
The "Training" Toggle
By default, OpenAI may use consumer chats to train models. However, the new Enterprise & Team updates introduced Zero-Retention APIs. When we build a GPT for your business using the API (not the chat interface), we can sign a BAA (Business Associate Agreement) that legally guarantees your data is discarded after processing.
Best Practices for Secure GPTs
1. PII Redaction Middleware
Before your user's message even hits OpenAI, it should pass through a "Redaction Layer." We build middleware that spots Credit Card numbers, SSNs, or distinct names and replaces them with tokens (e.g., <PERSON_NAME>) before sending to the LLM.
2. Role-Based Access Control (RBAC)
Not every employee needs access to the "Finance GPT." We implement authentication wrappers (using OAuth) around your internal GPTs, ensuring only authorized emails can invoke specific Actions.
3. Knowledge Base Segmentation
Don't dump everything into one vector database. We organize your data into "Silos." The Customer Support Agent accesses the Public Docs silo. The Internal Strategy Agent accesses the Private Strategy silo. They never cross.
Conclusion
AI is powerful, but a data leak is fatal. Don't rely on default settings. Engineering privacy into the architecture is the only way to scale safely. (Related: Hiring a GPT Developer vs DIY and Internal Company GPT Use Cases)
