From: aidotengineer
AI is transforming many sectors, including healthcare, finance, automation, and digital marketing [00:00:11]. However, a significant barrier to its widespread adoption is the lack of trust, particularly concerning the use of sensitive data and proprietary models [00:00:14]. Confidential AI addresses this by enabling models to run on sensitive data without exposing it [00:00:36].
The Foundation: Confidential Computing
The core technology behind confidential AI is confidential computing [00:01:10]. This technology solves the critical problem of data and model vulnerability during processing (training, fine-tuning, or inference) [00:01:21].
At the hardware level, this is achieved through Trusted Execution Environments (TEEs) [00:01:37]. A TEE is a secure, isolated part of the processor, such as Intel TDX, AMD SEV-SMP, or Nvidia GPU TEs [00:01:43]. It creates a “confidential environment” where code and data are protected even during execution, invisible to the host OS, hypervisor, or even the hardware owner [00:01:53].
TEEs also generate a cryptographic attestation, which is a signed proof that a workload ran inside verified hardware using unmodified code [00:02:24]. This attestation ensures strong assurances that the workload is protected and verifies the authenticity of the TEE and the chip itself [00:02:40]. In essence, TEEs allow sensitive computations to run securely and provide proof of their intended execution [00:03:14]. This enables AI models to process sensitive data without exposing either the model or the data [00:03:24].
Use Cases of Confidential AI
Healthcare
In healthcare, building or fine-tuning medical AI models is challenging, not due to the model itself, but due to the difficulty of obtaining or getting permission to use raw data [00:03:51]. Hospitals and labs are reluctant to share raw datasets, even if it could improve patient outcomes, due to tight controls, high generation costs, and data siloing [00:04:06]. Current regulations and security policies also prevent bringing models to the data [00:04:21]. This leads to months of negotiation for access to even small datasets and makes working across multiple providers’ datasets nearly impossible [00:04:28]. Confidential AI helps solve data privacy issues in this sector [00:04:41].
Case Study: BEAL and FDA Documentation Audits
BEAL (Brain Electrophysiology Laboratory) sought to expedite FDA approval for a new epilepsy diagnostic device [00:15:21]. Perfect documentation, typically requiring two to four weeks of manual audits with NDAs and risks of exposing trade secrets, was necessary [00:15:40]. Even a minor mistake could cause a 120-day review delay, impacting patient access and ROI [00:15:52].
To accelerate this, BEAL considered using Titonix’s AI-powered audit tool [00:16:01]. The primary concern was keeping BEAL’s data and Titonix’s model safe from exposure in traditional cloud environments [00:16:07].
Titonix utilized Super Protocol’s confidential AI cloud [00:16:19]. The audit ran within secure hardware environments (TEEs) using Nvidia H100/H200 GPUs and Intel TDX CPUs [00:16:22]. Every step was automated by smart contracts and verified by cryptographic proof [00:16:32]. All files and models remained encrypted, readable only inside the secure environment, and completely hidden from Super Protocol, BEAL, Titonix, or any other party [00:16:39].
The results were transformative:
- Audit time drastically reduced from weeks to just one to two hours [00:16:53].
- Zero risk of leaks, ensuring both BEAL’s and Titonix’s IP remained fully protected [00:17:00].
- No re-review delays, eliminating 120-day setbacks [00:17:05].
This demonstrated that guaranteed confidentiality can transform even highly sensitive processes like FDA clearance audits [00:17:20].
Digital Marketing
In digital marketing and custom analytics, there’s a strong desire to fine-tune models on real user behavior, tracking interactions across websites, content, and online services [00:05:47]. However, privacy laws, internal security rules, and ethical considerations often make working with such data risky or blocked [00:06:00]. This creates a significant gap between what is technically possible and what is legally or ethically permissible [00:06:14].
Case Study: Realize and Mars for Ad Performance
Mars, a confectionary company, runs hundreds of ad campaigns globally, facing substantial budget waste [00:12:47]. Realize, a company using AI to measure ad reactions by analyzing facial expressions, helps brands like Coca-Cola and Mars create more impactful ads [00:13:05]. To improve their AI accuracy, Realize needed more biometric video data from external partners [00:13:23].
The challenge arose from privacy laws such as GDPR and CCPA, coupled with data ownership concerns, making providers hesitant to share sensitive footage [00:13:32]. For its Mars project, Realize adopted Super Protocol’s confidential AI cloud [00:13:45]. AI training occurred within secure TEEs using powerful chips like Nvidia H100/H200s and Intel Xeons [00:13:48]. The entire process was automated by smart contracts and verified by hardware and Super Protocol’s open-source certification [00:14:00]. This ensured that data and models remained completely secure, proven to be inaccessible even to the cloud provider, Super Protocol, or Realize themselves [00:14:10].
Verifiable confidentiality changed the landscape:
- Once providers understood their data was truly protected, they shared four times more sensitive footage, increasing the training dataset by 319% [00:14:24].
- This boost improved AI accuracy to 75%, on par with human-level performance [00:14:37].
- For Mars, this translated to a 3-5% sales increase across 30 brands in 19 markets [00:14:47].
This case illustrates that provable data privacy unlocks valuable data, leading to better models, smarter AI, and tangible business impact [00:14:53].
Conclusion
Confidential AI, powered by TEEs and verifiable attestations, offers a practical path forward for developers in industries handling sensitive data [00:43:05]. It enables running models on private data without exposure, deploying proprietary models without losing control, fine-tuning without compliance risks, and verifying execution with cryptographic proof [00:42:37]. This technology transforms privacy into performance and confidence into revenue across data-driven industries [00:15:09].