Introduction

You encounter AI through inference. Internally, your employees use AI on business data — documents, CRM records, financial systems. Externally, you offer AI as a service to customers and partners. And increasingly, your AI agents interact with other agents across organizational boundaries. In each case, the model is not the hard part. The hard part is that useful inference requires access to sensitive data — and there is always someone in the chain who should not see it.

Scenarios

Every inference deployment involves some combination of the following parties:

• Your organization — owns the data, the models, or both
• Customers / users — use the AI service and send their data to it
• The infrastructure provider — runs the servers underneath

Depending on the scenario, different parties are exposed.

Corporate AI on private data

The AI tools worth deploying internally are the ones that work with real business data — documents, CRM records, ERP systems, data lakes. You own everything: the model, the data, the queries. The only question is who runs the servers.

If you run them yourself, there is no trust problem. But the moment you move to cloud infrastructure — for scale, for cost, for availability — the infrastructure provider can see your data, your queries, and your model. So internal AI stays limited to safe, low-value tasks. Not because the technology isn't ready, but because the trust model isn't there.

Cloud vulnerabilities:

• The infrastructure provider can see your data
• The infrastructure provider can see your queries
• The infrastructure provider can see your model

On-prem vulnerabilities:

• No vulnerabilities — you control everything

Inference as a service

You offer AI to customers or partners. They send their data to your service. This is where the trust problem gets wider — because your customers are exposed not just to the infrastructure, but to you.

On cloud, the infrastructure provider can see everything: your customer's data and your model. But even on your own servers, the core problem remains. Your customers have no way to verify that you can't see their data. And they know it. Customers who don't trust what happens to their inputs will limit what they send, or won't use the service at all. That's not a security concern — it's a business constraint.

Cloud vulnerabilities:

• The infrastructure provider can see your customer's data
• The infrastructure provider can see your model
• You can see your customer's data

On-prem vulnerabilities:

• You can see your customer's data
• You are both the AI provider and the infrastructure provider — there is no separation

AI Agents

Your agents interact with other agents across organizational and infrastructure boundaries. Agent A, running on your infrastructure, exchanges data with Agent B, running on someone else's. Each agent carries sensitive context — customer data, business logic, proprietary reasoning. Every interaction is a potential exposure point.

This is the widest trust problem. It's not one workload accessing one data source — it's multiple workloads, on different infrastructure, owned by different organizations, passing sensitive data back and forth. And unlike the previous scenarios, you're not just trusting infrastructure — you're trusting the other party's software.

Vulnerabilities:

• Each agent's infrastructure provider can see that agent's data and logic
• When agents exchange data, the receiving agent's infrastructure provider sees it too
• The organizations behind each agent are exposed to each other through the interaction
• No party can verify what the other agent is actually running

How SWARM solves this

All three scenarios share the same requirement: sensitive data must be processed without being exposed — to the infrastructure provider, or to the other participants. Super SWARM is a confidential compute fabric that runs across on-prem, public cloud, and managed infrastructure under a single hardware-attested trust domain. All parties must be running inside SWARM — the guarantees hold only when the entire interaction happens within the trust domain.

1. Each party deploys their workload into a confidential environment inside SWARM.
2. The execution environment is hardware-isolated — the infrastructure provider cannot access the data, the queries, or the model.
3. Before sending data, any party can verify the other party's workload — what code is running, in what environment, and how it's configured.
4. All data stays inside the SWARM trust domain. Only the agreed-upon output leaves the environment.

The mechanism is the same whether inference serves your employees, your customers, or your agents interacting with the outside world. The trust problem gets wider as the number of parties grows. The security guarantee does not change.