Centralized data networks, owned and/or managed by a single entity, have been structurally broken for years. Why? Single points of failure. If one entity (or even a few) has access to a database, then there is only one “point” to compromise to gain full access. This is a serious problem for networks that hold sensitive data such as customer information, government files, and financial records, and those that control infrastructure such as power grids.
Billions of digital records were stolen in 2024 alone, causing an estimated $10 trillion in damage! Among the notable violations almost all AT&T customer information and call logs, half of America’s personal health information, 700 million end-user records from companies using Snowflake, 10 billion unique passwords stored on RockYou24, and Social Security records for 300 million Americans.
Source: statesman2024
This is not just a private sector issue — governments and critical national infrastructure also rely on centralized networks. Among the known recent breaches records on 22 million Americans stolen from the US Office of Personnel Management, sensitive government communications from multiple US federal agencies, personal biometric data on India’s 1.1 billion citizens, and the continued Chinese infiltration of some US internet service providers.
Although hundreds of billions of dollars are spent each year on cyber security, data breaches are growing and occurring more frequently. It became clear that incremental products could not fix these network vulnerabilities — the infrastructure must be completely re-architected.
Source: market. us2024
AI amplifies the issue
Recent advances in generative AI have made it easier to automate everyday tasks and improve work productivity. But the most useful and important AI applications require context, i.e., access to sensitive health, financial, and personal user information. Since these AI models also require enormous computing power, they mainly cannot run on consumer devices (computers, mobiles), and must instead access public cloud network, such as AWS, to process more complex inference requests. Due to the serious limitations inherent in centralized networks described earlier, the inability to securely connect sensitive user data to cloud AI has become a major barrier for adoption.
Even Apple pointed this out during their announcement for Apple Intelligence earlier this year, citing the need to get help from larger, more complex models in the cloud and how the traditional cloud model is no longer viable.
They name three specific reasons:
- Privacy and security verification: Providers’ claims, such as not logging user data, often lack transparency and enforcement. Service updates or infrastructure troubleshooting may inadvertently log sensitive data.
- The runtime has no transparency: Providers rarely disclose software details, and users cannot verify whether the service is running unchanged or has detected changes, even with open-source tools.
- One point of failure: Administrators require a high level of access for maintenance, risking accidental data exposure or abuse by attackers targeting these privileged interfaces.
Fortunately, Web3 cloud platforms offer the perfect solution.
Blockchain-Orchestrated Confidential Cloud (BOCC)
BOCC networks are like AWS — except built entirely on confidential hardware and managed by smart contracts. Although still early days, this infrastructure has been in development for years and is finally starting to onboard Web3 projects and Web2 enterprise customers. The best example of this architecture is Super Protocolan off-chain enterprise-grade cloud platform fully managed by on-chain smart contracts and developed no trust in execution environments (TEEs). These are secure hardware enclaves that keep code and data proven confidential and secure.
Source: Super Protocol
The implications of this technology address all of Apple’s concerns mentioned earlier:
- Privacy and security verification: Through public smart contracts that orchestrate the network, users can verify that user data has been transported and used as promised.
- Work and program transparency: The network also verifies the work done within the confidential TEEs, cryptographically verifying that valid hardware, data, and software were used, and that the output was not tampered with. This information is also submitted on-chain for everyone to audit.
- One point of failure: Network resources (data, software, hardware) can only be accessed by the owner’s private key. Therefore, even if a user is compromised, only that user’s resources are at risk.
Although cloud AI represents a huge opportunity for Web3 disruption, BOCCs can be applied to any type of centralized data network (power grid, digital voting infrastructure, military IT, etc.), to provide superior and verifiable privacy and security, without sacrificing performance or latency. Our digital infrastructure has never been more vulnerable, but the blockchain-orchestra can fix it.
