As the integration of companies into artificial intelligence accelerates, concerns about data security have also peaked. Artificial intelligence is no longer just summarizing text, it is involved in critical processes of businesses, from financial transactions to transaction strategies. Thus, information security has become an essential requirement for organizations, not a choice.
Privacy gap grows in centralized artificial intelligence
Storing data on third-party servers creates serious vulnerabilities in traditional artificial intelligence infrastructures. For example, centrally recording each request sent can lead to the leakage of sensitive information. Just recently, there have been concrete incidents such as the code leak of Samsung engineers and the redirection of Korean users’ data to servers in China.
Such cases show that the readability of the instructions given to artificial intelligence also puts strategic information of high commercial value in danger. Crypto market analyst Kaff drew attention to this risk by emphasizing, “An agent’s system instruction is its alpha; if it can be read, it can be copied.”
An agent’s system instruction carries all its strategic value; If this data can be accessed transparently, it creates opportunities for competitors, which opens the door to “MEV”-like advantages.
Mini dictionary: MEV (Maximum Extractable Value) means the maximum gain that miners or validators in blockchain networks can achieve by manipulating the order of transactions.
Research conducted at the institutional level also clarifies the picture. According to McKinsey’s 2025 artificial intelligence report, data security stood out as the biggest obstacle for the business world and increased by 10 percent in one year. Additionally, 80% of institutions stated that artificial intelligence-based applications lead to unauthorized data access.
Crypto-based privacy solutions stand out
Technology giants are developing their own solutions for security. NVIDIA offers special security modes on its Blackwell GPUs, while Apple processes user data via the encrypted cloud. From Meta to Google Cloud, many platforms stand out with their “private transaction” options. However, all these solutions are ultimately dependent on central structures.
Crypto-focused projects, on the other hand, promise advantages such as transparency, resistance to censorship and verifiable infrastructure. The Venice platform offers local encrypted memory and end-to-end privacy with over two million users. NEAR provides services without server access to user information, even for daily transactions in private transaction environments (TEE).
The Nillion project combines multi-party transaction (MPC), holistic encryption and TEE technologies. While more than 640 million documents were stored on the platform, more than 1.4 million transaction calls were made. Phala Network, on the other hand, securely performs large language model (LLM) operations at almost standard speed through Intel and NVIDIA supported hardware.
Mini dictionary: TEE (Trusted Execution Environment) is a hardware-based security technology that ensures secure processing of data and code against external interventions by creating an isolated area embedded in the processor.
Gartner’s latest prediction states that secure transaction environments such as TEE will become mandatory in 75% of untrusted infrastructures by 2029. This presents a huge window of opportunity for crypto-backed privacy solutions to expand into the enterprise market in the coming years.
| Project | Scope/Solution | Number of Users/Transactions | Security Method |
|---|---|---|---|
| Venice | AI privacy platform | 2M+ users, 15K transactions/hour | End-to-end encryption |
| NEAR | AI Cloud (TEE secure environment) | no information | TEE (Trusted Execution Environment) |
| Nillion | MPC, multiple encryption | 643M+ documents, 1.4M transactions | MPC, TEE |
| Phala Network | LLM token processing | 1B+ tokens/day | TEE, Intel TDX, NVIDIA GPU |
