The Problem: AI Is Growing in Power, Not in Trust

Large Language Models (LLMs) are no longer limited to generating casual conversations or summaries. They now help users:

• Manage crypto wallets

• Parse financial statements and legal documents

• Recommend governance votes

• Answer sensitive questions about health, finance, and risk

Despite this expanded role, most models still run inside closed cloud environments where users must take everything on faith.

You don’t know where your prompt went.
You don’t know what model handled it.
You don’t know if the data was logged, intercepted, or altered.

In an age of critical AI decisions, this is no longer acceptable.

What Is the AI Trust Stack?

The AI Trust Stack is a framework for delivering verifiable, privacy-preserving, and auditable inference. It ensures that model outputs are not just accurate—but that they were generated under transparent, secure, and accountable conditions.

Here’s what it includes:

1. Privacy-Preserving Relay

Your prompt should not be visible to the systems that route it.

Using Trusted Execution Environments (TEEs), prompts can be encrypted during transit and only decrypted inside a hardware-isolated environment—ensuring no one, not even the relay provider, can inspect or store your data.

2. Verified Model Endpoints

You should always know which model you're interacting with.

The trust stack enforces model identity verification using cryptographic proofs. This removes the possibility of:

• Shadow model substitution

• Fine-tuned impersonators

• Fake or spoofed API endpoints

3. Minimal Metadata Footprint

Infrastructure should not retain or analyze data longer than necessary.

Every piece of metadata—timestamps, prompt size, IP logs—is a liability. The trust stack minimizes the data footprint to what's strictly required for routing, and nothing more. No persistent logs. No silent analytics.

4. Signed Responses with Attestation Proofs

Model responses should come with verifiable guarantees.

With hardware-backed attestation, responses are signed and include a cryptographic proof that the inference:

• Happened inside a secure, attested environment

• Used a specific, verified model endpoint

• Followed strict execution policies

This allows downstream systems, auditors, and even users to independently verify what happened.

Trust = Minimalism + Verifiability

Every added abstraction—proxies, third-party handlers, hidden APIs—increases your trust surface. More dependencies mean more assumptions.

The trust stack does the opposite:

• No third-party model swapping

• No silent data retention

• No reliance on centralized logs or monitoring

Just enough metadata to route the request—then it's gone.

Toward a New Standard for AI Infrastructure

LLMs are increasingly powering critical applications, not just productivity tools. From Web3 governance and autonomous agents to financial systems and medical reasoning, the stakes are too high for unverifiable inference.

Infrastructure must evolve:

• From blind trust → to cryptographic verification

• From black-box APIs → to transparent execution

• From convenience → to accountability