The more useful AI becomes, the more it needs to know about you.
If you are using AI to answer a general question, you may give it little more than a prompt. An AI workbench is different. It needs access to files, previous conversations, connected tools and account data. It may also coordinate several agents while working through a task that takes hours or days.
That context can reveal a great deal about a person or a company. It may include internal documents, research methods, operating procedures and unfinished ideas. For many users, those things are part of their competitive advantage.
Privacy policies are not enough
Many major enterprise AI providers now say that business customer data is not used to train their models by default. They also offer encryption, access controls and third-party audits.
Those protections matter. They still leave users with a basic problem: most of the system remains difficult to verify from the outside.
Users have to trust the provider's contracts, internal controls and operational processes. They usually cannot check which model handled a particular request or what runtime processed their data. They also cannot easily confirm what happened to the context after the task was completed.
Over time, another problem appears. The more an AI learns about a user, the more valuable its memory and preferences become. Working methods, personal context and accumulated knowledge can end up tied to one platform.
This is one reason Ghast is building on @0G_labs.
Building the workbench on 0G
0G provides infrastructure for AI workloads across Compute, Storage and Chain. Ghast uses these layers to give the Workbench more context without asking users to give up as much control over it.
Ghast Workbench is available on Windows and macOS, with access to AI models deployed on 0G. We are also building Agent Teams, so several agents can work together on longer and more complicated tasks.
As the Workbench becomes more capable, privacy has to be built into the product itself. A policy can describe how data should be handled. Architecture determines what the system can actually do.
Keeping sensitive inference sealed
For sensitive workloads, Ghast plans to use 0G's Private Inference tier, the strictest of its three routing modes.
Requests in this tier go only to providers running inside TEE GPU enclaves. The inference process remains sealed from the provider and from 0G. Each response is cryptographically signed, allowing users to verify which model ran, what hardware was used and whether the result was altered.
Verified Routing provides a lighter option. It can establish where a model came from and how the request was routed, while leaving privacy protections to the provider. That may be suitable for everyday tasks. It is a different standard from protecting the private context that an AI workbench collects over time.
The three routing modes can be compared at pc.0g.ai.
Protecting memory and workflow data
The model call is only one part of the system.
Ghast also plans to encrypt memory, context and workflows on the user's device before storing them on 0G Storage. The chain only needs the information required for ownership, version tracking and verification.
The raw prompts, files and outputs do not need to be recorded onchain.
This approach keeps sensitive data separate from the records needed to prove who owns it and whether it has changed. It also gives the user more control over how that data is stored and used.
Securing agent coordination
Agent Teams will introduce another layer of risk. Multiple agents may need to share context, use tools and handle credentials while working toward the same result.
As the system develops, Ghast plans to move sensitive orchestration and key handling into the Agentic ID runtime. The runtime executes inside 0G's attested, sandboxed environment.
The goal is to protect the whole workflow, not only the request sent to a model. The context, credentials and execution logic used to complete a task also need a clear security boundary.
Users should not have to manage this infrastructure themselves. They use Ghast. 0G provides the compute, storage and verification underneath it.
What this can and cannot guarantee
TEE technology is not magic. It cannot eliminate every vulnerability, and it cannot tell you whether an AI response is correct. External tools and APIs still need their own access controls.
What it can do is reduce the number of people and systems that sensitive data passes through. It can also make parts of the execution independently verifiable.
That gives users something more concrete than a promise written in a privacy policy.
An AI that understands more about your work should not automatically gain more control over it.
Ghast is building an AI Workbench that can learn how you work while keeping the boundaries around your data where they belong.