This article is the result of a personal inquiry rather than a technical analysis. Because as a content producer, I work very closely with artificial intelligence while shaping the content, and in every process, I question both my own knowledge and its suggestions separately and try to reach a conclusion.
Especially on platforms like @DAO Labs that encourage participation, this relationship with artificial intelligence agents is really important. With these agents, we try to think, decide and even understand some issues even better. And in this process, it becomes inevitable to question the systems that create content as much as producing it. That's why I asked myself: “Will I be this comfortable with my personal data?”
In the age of #AI3 , security is not only a matter of the system, but also of the user. And trust often starts not from complex cryptographic terms, but from something much more human: Understanding. That's why this article starts with the questions I, as a user, have been asking. And it seeks to answer them honestly, with the official sources available to us.
The first concept I came across was #TEE : Trusted Execution Environment. In Dr. Chen Feng's definition, these systems are isolated structures built in an untrusted environment; areas that are closed to outside intervention and can only be accessed within certain rules. It is possible to think of it as a kind of fortress, but this fortress is not built outside the system, but right inside it. The agent works here, the data is processed here and no one from the outside can see what is happening. Everything sounds very secure. But I still have a very basic question in my mind: Who built this castle? Who has the key to the door? And at this point a new question popped up in my mind: How secure is this structure really? #ConfidentialAI
It would be too optimistic to assume that this structure is foolproof, no matter how protected it looks. Because it is usually the hardware manufacturer that builds these spaces, which brings us to the inevitable trust relationship. Of course, over time, vulnerabilities have been discovered in some TEE implementations. However, the issue here is not only whether this structure is flawless or not, but also how these structures are used and what they are supported with. Today, these systems are not considered as standalone solutions, but as part of larger and more balanced architectures. This makes them logical, but not absolute.
This is why system design makes sense not only by relying on one method, but by balancing different technologies. There are alternative solutions. For example, ZKP, Zero-Knowledge Proof, manages to verify the accuracy of information while keeping its content secret. Or systems such as MPC, which process data by breaking it up and sharing it between multiple parties. These are impressive methods. In the past, these technologies were thought to be slow, but there have been significant advances in speed in recent years. As Dr. Feng puts it, we may have to wait until the end of the century for these technologies to mature. As much as this sentence speaks of a technical reality, it is also striking.
Now I come to the real question: Where does #AutonomysNetwork fit into all this? Is this project just a promise of privacy, or is it really building a different architecture? I'm more interested in the answer to this question because I don't just want to trust the technology; I also want to know how the system works. Autonomys doesn't leave TEE alone. It protects the agent's actions within TEE and records the rationale for its decisions in the chain. These records are made permanent through PoAS, Proof of Archival Storage. In other words, the decision history cannot be deleted or changed. This ensures that the system is not only secret but also accountable. The agents are creating their own memories. And even when verifying my identity, the system does not reveal my data. This detail is supported by the ZKP.
But I still believe that when evaluating these systems, it is important to consider not only the technology, but also the structure within which it works. After all, I didn't build the system, I didn't write the code, but Autonomys' approach tries to include me in the process instead of excluding me. The fact that the agents' decisions are explainable, their memories are stored in the chain, and the system is auditable makes the concept of trust more tangible. As Dr. Feng puts it: “Trust begins where you are given the right to question the system from the inside.”
At this point, security is not only about whether the system is closed or not, but also about how much of what is happening inside can be understood. True security begins with the user being able to ask questions of the system and understand the answers they receive. While Autonomys' TEE architecture may not be the ultimate solution on its own, when combined with complementary logging mechanisms, verification layers like PoAS, and identity protection solutions, it offers a multi-layered and holistic approach.
The fact that Dr. Chen Feng, who has a strong academic background in artificial intelligence, is behind such a detailed structure demonstrates that this approach is not random but rather deliberate and research-based. In my opinion, this is precisely what elevates Autonomys from being an ordinary privacy initiative to a more serious framework.