ZeroTrusted.ai

@frank_denbow, first of all, thank you for your review. I believe we need to do a better job of highlighting all our value propositions in the hero section.

You are right; our product assists companies with their compliance needs.

To address some of the questions you raised:

1) ZeroTrusted.ai acts as middleware between users and Large Language Models (LLMs) through our secure chat or API.

2) There's no need for you to have a separate account or key for each LLM. Instead, we provide our own keys, allowing access without revealing your identity to the LLMs.

3) Your point about the potential exposure of scrubbed data in the event of a breach at a third-party LLM's network is partially correct. However, your identity will not be linked to this data. This scenario is particularly critical for both individual users and businesses.

4) We offer features that maintain context when sanitizing sensitive data.

Example #1

Suppose a medical expert needs to process the following input:

"Create a summary of this patient's diagnosis: Patient Name: Paul Smith Date of Birth: 12/08/1987 SSN: 666-555-5555 Diagnosis: Chronic Heart Disease Treatment Plan: Undergo heart surgery by Dr. Smith at General Hospital on 03/15/2024 Insurance: ABC Health Insurance, Policy Number 434444444"

Before we pass it on to any LLM, we'll alter the sensitive (PHI compliance violations) details and transform to:

"Create a summary of this patient's diagnosis: Patient Name: John Doe Date of Birth: 01/01/1970 SSN: 123-45-6789 Diagnosis: Chronic Heart Disease Treatment Plan: Undergo heart surgery by Dr. Smith at General Hospital on 03/15/2024 Insurance: ABC Health Insurance, Policy Number 987654321"

This way, we preserve the context, while ensuring that your PHI compliance is met and sensitive data isn't exposed to LLM.

After getting a response from the LLM, we revert the swapped sensitive value to your original while preserving the result context.

We will be adding features that will allow RLHF to learn and adjust to customer preferences.

Example #2:

if a customer (individual or corporate) submits legal content containing sensitive information, our "Fictionalize" feature replaces this data with fictional values. This ensures the preservation of context while protecting the actual sensitive information. The original data is restored once a response is received.

Hope this helps. Please us know if there are any more questions you would like us to address.

Once again we appreciate your feedback and we hope your views can gain from the privacy and security solution that we provide.