Super Protocol

Full-stack confidential AI for models, agents and data, across GPU clouds, on-prem and hybrid. Powered by #confidentialcomputing ❇️

[Super Protocol @super__protocol]

What does Sovereign AI look like from a GPU cloud provider’s perspective? BE, Super Protocol and NVIDIA Blackwell are putting it into practice in the UK.

Today, we are announcing that The BE Company, a UK-based AI infrastructure and services provider, has integrated Super Protocol into its existing inference service, enabling confidential LLM inference for enterprises handling sensitive business data, powered by NVIDIA Blackwell.

Data residency addresses where information is processed, but not how it is protected while in use. The integration is designed to let BE’s enterprise customers protect sensitive information during processing, including from the provider and privileged administrators, and independently verify the environment and software handling it, without relying solely on the infrastructure operator’s assurances.

Here’s how BE and @nvidia describe the value:

Sath Ganesarajah, Founder and Chief Executive Officer, BE:

“Our enterprise customers need to protect sensitive data while running AI at scale, with fast responses, reliable availability and clear service commitments. Super Protocol gives us the complete stack within our existing infrastructure, allowing us to deploy and scale protected inference without building a separate confidential computing platform. Customers can verify the service before sending sensitive information, giving them access to advanced AI with data privacy they can verify independently.”

Anthony Hills, Regional Sales Leader for NVIDIA in the UK & Ireland:

��As the UK expands its sovereign compute and inference capacity, enabling sensitive and regulated workloads is increasingly important. NVIDIA Confidential Computing provides hardware-backed protection during AI processing. Super Protocol supplies the complete software stack that makes this protection deployable and verifiable across inference services, enabling providers such as BE to add verifiable confidentiality to UK-based sovereign AI infrastructure.”

👉 Read more: https://t.co/WFAa8B7Sw8

[Super Protocol @super__protocol]

What does Sovereign AI look like in healthcare? Reset Health and Super Protocol are putting it into practice with NVIDIA Blackwell.

Together with Reset Health, we’ve announced a new Confidential AI capability for Reset AI within Reset Health’s clinician-led digital care platform for cardiometabolic conditions, including obesity and type 2 diabetes.

Super Protocol enables Reset AI to analyse sensitive treatment data on externally hosted NVIDIA Blackwell systems, keeping patient data protected during processing and inaccessible to infrastructure providers and their administrators.

Reset AI can turn this treatment data into clear clinical summaries, allowing care teams to personalise care based on each patient’s progress.

Here’s how NVIDIA and Reset Health describe the value:

Anthony Hills, Regional Sales Leader for NVIDIA in the UK & Ireland:
“As the UK builds out its sovereign AI capabilities, healthcare and other regulated sectors need practical ways to apply advanced AI to sensitive data. NVIDIA Confidential Computing provides hardware-backed protection for data during processing. Super Protocol provides the complete, ready-to-use software stack for verifiable execution, with a Confidential AI layer that simplifies adoption. Reset Health demonstrates how a healthcare organisation can use AI on external NVIDIA GPUs while keeping treatment data inaccessible to infrastructure providers and retaining control over how it is used.”

David Wong, Executive Chairman and Co-founder of Reset Health:
“Our opportunity is to use AI to turn treatment data collected through Reset’s platform into more personalised care. Super Protocol enables us to analyse treatment data on external GPU systems while keeping it protected from infrastructure operators, without building confidential computing infrastructure in-house. Our AI team can therefore focus on helping care teams review treatment progress and developing therapeutic optimisation: identifying which combinations of medication and support work best for different patients.”

👉 Read more: https://t.co/XznZS8WJdO

[Super Protocol @super__protocol]

How many clicks does it take to launch Sovereign AI Collaboration between 3 independent organizations?

Sensitive data. Proprietary AI. No Confidential Computing expertise required.

Watch the demo and count for yourself!

👉 https://t.co/Z1BIygZQhn

[Super Protocol @super__protocol]

𝐒𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧 𝐀𝐈 𝐡𝐚𝐬 𝐚 ��𝐚�� 𝐭𝐡𝐚𝐭 𝐫𝐚𝐫𝐞𝐥𝐲 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐞 𝐬𝐥𝐢𝐝𝐞.

A sovereign cloud solves a real problem. Data stays in the region, the operator is local, and exposure to foreign jurisdictions is reduced.

Now count who may still have access to a client's data, model weights and prompts: the infrastructure operator and its admins, the model provider, the agent vendor, the integrator, the MSP.

Each of them is held back by a contract. 𝐀 𝐜𝐨𝐧𝐭𝐫𝐚𝐜𝐭 𝐥𝐢𝐦𝐢𝐭𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐚𝐫𝐞 𝐚𝐥𝐥𝐨𝐰𝐞𝐝 𝐭𝐨 𝐝𝐨, 𝐧𝐨𝐭 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐜𝐚𝐧 𝐬𝐞𝐞.

What closes the gap is a confidential AI layer. That is what Super Protocol built.

Models, agents and data run on CPUs and NVIDIA GPUs in confidential mode, together with the full stack around them: orchestration, storage and key management. Teams deploy their existing applications using standard tools, across GPU clouds, on-prem or hybrid environments.

The infrastructure operator, the vendors and Super Protocol itself have no access to the protected data during processing, and the client's security team, partners and auditors can independently verify what's deployed.

𝐖𝐡𝐨 𝐢𝐬 𝐢𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐞 𝐭𝐫𝐮𝐬𝐭 𝐛𝐨𝐮𝐧𝐝𝐚𝐫𝐲 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐬𝐭𝐚𝐜𝐤 𝐭𝐨𝐝𝐚𝐲, 𝐚𝐧𝐝 𝐰𝐡𝐨 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐫𝐞𝐦𝐨𝐯𝐞?

[Super Protocol @super__protocol]

@HPE Sources:
https://t.co/0FQSfZPpOc

https://t.co/gOxs9cnB1E

https://t.co/al1KHHixeD

[Super Protocol @super__protocol]

Zoom out on three announcements from the past month and a larger picture emerges.

@HPE made NVIDIA Confidential Computing available across its entire AI Factory portfolio – from sovereign deployments to private cloud AI – marking a shift from optional add-on to standard enterprise offering.

@Apple extended its Private Cloud Compute privacy guarantees to Google Cloud – the first time Apple trusted a public cloud with its privacy infrastructure, made possible by NVIDIA Confidential Computing.

@WhatsApp launched Incognito Chat with Meta AI – completely private conversations processed inside TEEs where not even Meta can read the content. Consumer scale, default privacy.

Enterprise & Sovereign AI. Private clouds & Hyperscalers. Consumer platforms.

Different markets. Different requirements. Same direction.

For organizations evaluating AI platforms, the question is increasingly shifting from "Do we need Confidential Computing?" to "How do we operationalize it across our existing infrastructure?"

Super Protocol helps make that transition practical by providing a consistent confidential execution layer across public cloud, private cloud, on-premises, and hybrid infrastructure as a single operational environment – without requiring organizations to rebuild their AI stack or become Confidential Computing experts.

The direction is clear. The question is whether your infrastructure is ready for it.

[Super Protocol @super__protocol]

@GoogleResearch HAI-DEF Showcase: https://t.co/wkndVjk7qO

[Super Protocol @super__protocol]

Congratulations to @GoogleResearch Health AI Developer Foundations (HAI-DEF) team on the launch of the HAI-DEF Showcase.

We're honored to see two confidential healthcare AI case studies developed by Yma Health and Super Protocol featured as the first entries in the "Technical Solutions and Tools" section of the showcase.

The two case studies demonstrate how the open medical foundation model #MedGemma 27B can be deployed in real healthcare workloads through Confidential AI. By transforming #TEE-enabled infrastructure into a verifiable confidential execution layer, Super Protocol enables sensitive data to remain protected during processing regardless of where the workloads run.

One implementation demonstrates confidential inference on @nvidia Blackwell B200 infrastructure hosted by @nebiusai AI Cloud, with patient EHR data remaining protected throughout processing. MedGemma anonymized patient records while preserving the clinical context and value of the data.

Another demonstrates MedGemma fine-tuning on NVIDIA H200 infrastructure using real patient dialogues, achieving a 9.4/10 evaluation score while preserving data confidentiality.

As the health tech ecosystem explores the potential of open foundation models and Confidential AI, sharing practical implementation examples on real healthcare data will be essential for accelerating adoption across research, clinical workflows, and precision medicine.

👉 HAI-DEF Showcase - link in comments

[Super Protocol @super__protocol]

→ NVIDIA Press Release https://t.co/0wnqcQu5lk

→ GPU+CPU TEE requirements https://t.co/w90Yx1Y1W4

[Super Protocol @super__protocol]

This fall, Confidential Computing ships at rack scale. 72 GPUs. One TEE.

NVIDIA announced Vera Rubin back in March. Now it's ramping to production.

Currently, each server forms its own TEE boundary: up to 8 GPUs, 2.3 TB shared memory. Vera Rubin extends this to the entire rack – 72 GPUs, 20.7 TB of shared memory in one TEE. Imagine what model will fit in there!

"Everything across this is secure because the AI model is so precious. This is the reason why this entire system obeys confidential computing." – Jensen Huang, NVIDIA CEO, GTC Taipei 2026

What can you do while waiting? We recently updated our GPU + CPU TEE requirements guide, covering all available GPU TEE-capable SKUs, from Hopper to Blackwell, with compatibility details for Intel TDX and AMD SEV-SNP – and what is not TEE-capable as well.

Check them out and start deploying Confidential AI today. With Super Swarm – no TEE expertise required.

Links in comments 👇

[Super Protocol @super__protocol]

Read the white paper: https://t.co/oxjS3IRDM2

[Super Protocol @super__protocol]

More organizations think they have Confidential Computing than actually do.

The CCC's new white paper "3 Degrees of Confidential Computing" makes this concrete: Level 1 migrating to Confidential VMs provides hardware isolation, but as the paper notes, “without integrating remote attestation, it does not meet the definition of Confidential Computing”.

However, what is even more telling is the direction in which the paper points beyond Level 3 towards Confidential AI: multi-CVM interactions, AI agent sandboxes, CC-aware network protocols and CC-enforced software provenance.

That future isn't theoretical for us. It's what Super Swarm is built on today – self-organizing, mutually attesting GPU clusters that form a single hardware-verified trust domain across cloud, on-prem, hybrid, and multi-cloud environments. Every interaction is independently verifiable. No custom builds. No TEE expertise required.

The complexity of operationalizing Confidential AI shouldn't become a project of its own. That's exactly the problem the execution layer should solve.

👉 Link to CCC paper in comments

[Confidential Computing Consortium @ConfidentialC2]

CC has transitioned from a niche security technology to a strategic imperative for protecting data in use, but its security benefits depend on how deeply it is integrated into your stack.

Learn about the practical 3-level maturity model to help orgs roadmap their adoption. ⬇️

[Super Protocol @super__protocol]

Full episode on what safety really means in clinical AI, and why slow feedback loops are an opportunity: https://t.co/mz8eIe9FHz

[Super Protocol @super__protocol]

The feedback loop healthcare AI never got

A radiologist reviews a scan. The AI flags a suspicious mass. The patient is referred, biopsied, diagnosed. The physician closes the loop.

The AI never does.

Was the flag correct? Was it a false positive? The answer sits in a different EHR, a different department, sometimes a different institution – and arrives months later. Nobody systematically pipes that signal back to the model, because the infrastructure to do so was never built.

It is how healthcare has always been organized: services separated, records siloed, pathways fragmented. Imperfect, but functional enough for clinical care – and invisible enough that nobody felt the cost.

In most systems, the feedback loop is the first thing you set up. You ship, you measure, you iterate. The signal is fast, systematic, and the model improves. Healthcare AI never got that infrastructure.

Rory Pilgrim, Product Manager at Google Research, made an observation in the "Confidentially Yours" episode worth sitting with:

The slow feedback loop is not just a limitation. It is an opportunity.

If closing the loop leads to better outcomes – fewer missed diagnoses, fewer unnecessary recalls, models that improve on real-world data – institutions have a concrete reason to build the outcome pipelines they never prioritized. AI creates the business case for data infrastructure healthcare never had sufficient reason to build.

But acting on that immediately hits a structural wall.

Outcome data is patient data – highly regulated and, in most architectures, legally immovable. Traditionally, that immovability is the barrier. The data that would close the loop cannot cross the compliance boundary, so the loop stays open.

Super Swarm inverts the problem. Models can live anywhere – on-premise, in the cloud, across institutions. Instead of moving data to the model, computation runs inside a hardware-attested confidential computing environment – where even the operator cannot access what's being processed. Institution-specific outcomes never cross organizational or regulatory lines. The exposure risk is architecturally eliminated.

The feedback loop healthcare AI never got is now within reach.

🎥 "Confidentially Yours" with Rory Pilgrim and host Mike Bursell (Advisor, Super Protocol).

👉 Scan to watch the full episode, or find the link in the comments

[Super Protocol @super__protocol]

https://t.co/RM63VXYIdL

[Super Protocol @super__protocol]

"Born out of GPU scarcity, neoclouds now face a harder test." – McKinsey, November 2025

14–16% gross margin after depreciation. Lower than many non-tech retail businesses. The prescribed move is clear: orchestration, managed inference, platform layers. And the market is moving fast. But there’s something already inside the hardware that the stack race is overlooking.

H100, H200, B200, B300 — and every generation after — already include confidential computing capabilities. Super Swarm turns those capabilities into a verifiable confidential execution layer for neoclouds – enabling sovereign compute environments for sensitive data and AI workloads. GPU cloud instances stop being just rented compute and become independently verifiable confidential infrastructure. Customers with their own on-prem infrastructure can extend workloads into cloud instances without leaving the trust boundary.

That’s not just another platform feature. It’s a different category of infrastructure.

👉 McKinsey: The evolution of neoclouds and their next moves – link in comments

[Super Protocol @super__protocol]

The faster AI scales, the faster confidence in it erodes

For nine years Stanford Human-Centered AI has tracked where AI actually stands and suggests where it’s heading across academia, industry, and government. The 2026 report is out. Here's what stood out.

Adoption is accelerating. Confidence is eroding.
70% of organizations now use AI in at least one business function. But look one layer deeper:
�� Among orgs that experienced incidents, those facing 3-5 per year jumped from 30% to 50%
🔹 "Excellent" incident response self-ratings fell from 28% to 18%

Deployment is accelerating. Confidence in handling what breaks is not.

Agentic AI is stuck – and the blocker isn't capability.
🔹 62% cite security as #1 barrier to scaling agentic AI – outpaces #2 by 24 percentage points
🔹 Scaled agent use sits in single digits across virtually every business function
🔹 Only exception: tech sector at 24% in software engineering, 22% in IT, 21% in service ops

Organizations aren't waiting for better models. They're waiting for infrastructure they can trust.

Medical AI hits the same wall – from a different angle.
Medical AI is ready to move into live clinical deployment. Prospective trials grew 28.5% year-over-year (417 → 536 in 2025). The pipeline is there.
But the data isn't:
🔹 Medical imaging training data is roughly 100x smaller than non-medical AI datasets
🔹 Fragmentation across institutions further limits the development of large-scale medical foundation models

The models are ready. The environment to run them on real data is not there yet.

Three sectors. Three blockers. One root cause:
the gap between how fast AI is being deployed and the infrastructure needed to actually trust what it does.

Trust is a vulnerability – and it cannot be legislated away. The policies are already multiplying faster than anyone can implement them – and fragmented regulations across jurisdictions don't provide the technical enforceability that sensitive workloads demand.

It demands proof that you can independently verify, automatically enforce, and continuously audit.

That is exactly what Super Swarm provides. It bridges the gap by delivering cryptographic proof of what actually ran, on which data, and across independently verified infrastructure. Super Swarm makes verifiable confidentiality an architectural guarantee – not a contractual promise.

[Super Protocol @super__protocol]

Full demo: Super Swarm: Confidential Healthcare AI – MONAI CT Segmentation https://t.co/o3QQJ9cveY

[Super Protocol @super__protocol]

Ask a hospital to run AI on their patient data. The answer is always the same.

A hospital, a GPU provider, and a medical AI vendor. Everyone has what the others need and none of them can just hand it over. The hospital won't send data to infrastructure they don't control. The vendor won't expose their model. The GPU provider can't take on liability for what runs on their hardware. The model never runs. The patient never benefits.

This is the real reason healthcare AI moves slowly. Not the models. Not the regulations. Trust is a vulnerability. Super Swarm solves it structurally.

In this demo we used a model from the @ProjectMONAI Model Zoo – open source, anyone can take it. The data is another story.

MONAI, originally started by @nvidia and @KingsCollegeLon, is the open-source framework for medical imaging AI. Used at Siemens Healthineers, Mayo Clinic, and beyond. Millions of downloads worldwide.

We deployed one of those models on Super Swarm. The app segments the spleen from a CT scan, calculates volume and area, and returns the results. What makes it different is the execution environment and the verifiable proof it leaves behind.

The computation runs inside a hardware-protected TEE. Patient data is processed within that sealed environment and never exposed to anyone – including us. Whether the infrastructure is public cloud, on-prem, or hybrid. No policy makes that guarantee. The hardware does.

At deployment, Super Swarm generates Deployment Evidence – a cryptographic proof of what code is running, in what environment, on what hardware. No compliance reports. No trust agreements. Access is granted only when the proof matches.

Ask a hospital to run AI on their patient data. With Super Swarm, the answer changes – wherever you run it.

👉 Scan to watch the full demo, or find the link in the comments.

[Super Protocol @super__protocol]

Trust. Control. Scale. Access – the full breakdown:
https://t.co/fLfbZzOtgM

[Super Protocol @super__protocol]

The system works – until you try to automate it.

The trust domain spans every infrastructure, every organization. Data never leaves its sealed environment. Nobody depends on anyone else’s goodwill. And then the product team asks: can we automate this?

AI agents are already operating on behalf of organizations – querying data, calling models, chaining actions across boundaries. Not one request at a time. Thousands per hour. A bank deploys a fraud detection agent. It needs to cross-reference transaction patterns across three partner institutions in real time. Each request takes milliseconds. Each approval takes days. The fraud happened. The access request is still pending.

The verification model still applies. Sealed hardware. Cryptographic proof. A trust domain that spans every cloud and every data center. But the decision about who gets access can't wait for a human to review it. No administrator can keep up. No approval queue moves fast enough. The same rigor that made the first collaboration work becomes the bottleneck that makes the next hundred impossible.

This is Problem #4 of 4. The Access Problem.

Super Swarm solves this with policy-driven access. Each data owner defines their conditions once: what code, what configuration, what hardware qualifies to touch their data. When an agent requests access, it presents a cryptographic proof of its runtime environment – the same proof a human would review manually. The system checks it automatically. Match – execution is allowed. No match – nothing happens. No human in the loop. No delay.

The data owner’s role is simple: define the policy once. The system enforces it at whatever speed the agents operate. A hospital might set conditions as narrow as a specific model, a specific partner, a specific project. Or as broad as any application running inside verified secure hardware with a certified diagnostic framework. The policy reflects their risk tolerance – not the system’s limitations.

Hardware nobody can see into. Proofs that verify in milliseconds. Infrastructure that spans every cloud and every data center. Policies that govern access at the speed AI actually operates. Each piece exists because the one before it made it necessary. None of them works on its own.

That’s the system. That’s Super Swarm.

Trust. Control. Scale. Access. How they connect – link in the comments.👇