Latest posts
Super Protocol Announcements
18 Jun, 14:34
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
Super Protocol Announcements
17 Jun, 14:36
Congratulations to the Google Research 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 Nebius AI Cloud, with patient EHR data remaining protected throughout processing. MedGemma anonymizedSuper Protocol Announcements
1 Jun, 20:35
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 2026What 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
Super Protocol Announcements
29 May, 15:33
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
Super Protocol Announcements
25 May, 16:52
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.RorSuper Protocol Announcements
18 May, 16:38edited
"Born out of GPU scarcity, neoclouds now face a harder test." – McKinsey, November 202514–16% 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.NVIDIA H100, H200, Blackwell – 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
Super Protocol Announcements
13 May, 16:32
The faster AI scales, the faster confidence in it erodesFor 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
Super Protocol Announcements
12 May, 16:35
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 Project MONAI Model Zoo – open source, anyone can take it. The data is another story.MONAI, originally started by NVIDIA and King's College London, is the open-source framework for medical imaging AI. Used at Siemens Healthineers, Mayo Clinic, and beyond. Millions ofSuper Protocol Announcements
4 May, 15:15edited
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
Super Protocol Announcements
30 Apr, 16:16edited
The first data collaboration works. Then your AI roadmap asks for ten more.One partnership took long enough that everyone forgot how it started – legal, compliance, integration, security review. The model trained, the results were good, and everyone moved on. Then the product team came back with more ideas.Every new partnership becomes its own project – not just operationally, but technically. Even with the same partners, nothing carries over. A new use case means new rules, new pipelines, new approvals. The environment gets rebuilt from scratch.And the environment itself doesn't stay fixed. What starts as a well-defined setup quickly grows – participants, data, objectives, rules, infrastructure – and becomes impossible to standardize or reuse.▸ No neutral ground A global FMCG brand – selling through multiple retail chains – wants to build audience models across three competing
Super Protocol Announcements
27 Apr, 17:37
SOC 2 doesn't answer the question that kills the deal.An enterprise company is evaluating an AI vendor. The demo went well. The use case is clear – processing sensitive contracts and financial records. The price works. And then, one question comes up:If something changes on your end, or your provider's – what happens to our data?The vendor points to their SOC 2 certification and their contract with the infrastructure provider. The customer's legal team reads it carefully. It explains how access is managed and what happens if something goes wrong. But it doesn’t define what is technically enforced at runtime – if anything is.The question behind the question: 🔹 who can access your data at runtime 🔹 who can change how it’s processed 🔹 whether safeguards can be bypassedThose are questions of enforcement – not just process. The deal goes on hold. Legal gets involved. Months
Super Protocol Announcements
24 Apr, 17:46
Every enterprise AI roadmap has the same graveyard.Partnerships that made obvious sense. Models that would have been genuinely better. Deals that everyone wanted – and nobody could close.The reason is simpler and more frustrating than most people admit: to process data, you have to decrypt it. And the moment it's decrypted, someone on the other side can see it. An admin with the wrong access. A misconfigured bucket. A subpoena nobody anticipated. The exposure doesn't need to be malicious to be real.So the deal goes to legal. Legal adds clauses. IT adds requirements. Security adds reviews. Six months later, you're still negotiating who gets access to what – and you haven't moved a single row. When the next partnership comes, you start from scratch.This is why healthcare AI trains on a fraction of the data that exists. Why bank fraud models stay siloed even when sharing signals
Super Protocol Announcements
23 Apr, 18:29edited
Banks know more about you than almost anyone. And they do nothing with it.Your salary lands in their account. Your transactions reveal where you go. Their app captures how you behave. Where you live and how you actually live – all visible, all logged.Customers aren't saying "stop collecting my data." They're saying: "You already have it. Why aren't you using it for me?"Alex Pyatigorskiy, product executive with a background spanning Disney, global banks, and telecoms, now CPO at Vama, heard this across thousands of customer interviews. And it reframes the whole problem.Banks are not short on data. But they legally cannot share customer data with partners – and partners won't expose theirs either. So a joint offer that could benefit everyone never gets built. The knowledge stays locked. The customer stays underserved. And loyalty erodes to whoever offers 0.1% more on a savingsSuper Protocol Announcements
10 Apr, 14:38
67% of companies say security risk is the #1 blocker to scaling agentic AI – according to McKinsey's 2026 AI Trust Maturity Survey.For a while, the main worry was AI saying the wrong thing – hallucinations, bad outputs, bias. That’s a model problem.Agentic AI changes the stakes. As systems become more autonomous, an agent that accesses a database, calls an API, or processes sensitive records is not just a chatbot. It acts. And the gap between what it's allowed to do – and what actually happens at execution time, and on what data – is where trust breaks down. That’s an execution problem.The survey maps it: 🔹 Only ~30% of organizations have mature controls (scoring 3+) for strategy, governance, or agentic AI – meaning 70% are flying partially blind into autonomous systems. 🔹 60% of respondents cite knowledge and training gaps as the leading obstacle to responsible AI implementation
Super Protocol Announcements
7 Apr, 16:22
There is a tension at the center of every enterprise AI deployment right now.On one side, clients don't want their data used beyond their own use case – especially when it's proprietary or sensitive. On the other, vendors need data across customers to improve what they deliver. Both sides make sense. And that's exactly the problem. A data processing agreement can document the boundary. But it cannot enforce it.This is what AI governance frameworks keep running into: the compliance layer describes what should happen. It has no mechanism to prove it did.Agentic AI makes the problem structurally harder. With a single model call, the risk boundary is relatively clear. With agents operating across tools, APIs, and multi-step pipelines, a failure at one step compounds downstream. Governance written for static systems is already behind – and the frameworks haven't caught up.The answerSuper Protocol Announcements
3 Apr, 14:46
A physician sees the patient record. Here's how AI connects the dots – without exposing the data.🔹 The full picture is there – complaints, history, medications, allergies, notes from previous visits. AI could reason over all of it, flag what matters, catch what's easy to miss. The technology exists. So do the tools. What's been missing is an architecture that doesn't force a choice between using AI and protecting patient data.When patient data is processed on infrastructure you don’t control, someone else controls how it’s handled – and may access it. Not necessarily – but physically, they can. No contract changes that. No audit prevents it. Some organizations accept that risk. Most can't. So the data stays inside. And the AI stays out.It doesn't have to be that way.🔹 How it works A LangGraph agent collects and structures the patient record from the HIS. The MedGemma modelSuper Protocol Announcements
1 Apr, 18:20
Two years ago we validated against the ARM CCA emulator. Today ARM is shipping their own silicon. The TEE landscape just got bigger. Again.ARM announced the Arm AGI CPU – their first ever production silicon, built on Neoverse V3 cores. Notably: ARM Confidential Computing Architecture (CCA) support is built-in from day one.Until now, ARM operated purely as an IP licensor – designing CPU architectures and licensing them to manufacturers like Apple, NVIDIA, Qualcomm, Samsung, and AWS, who built their own silicon on top. With the AGI CPU, ARM crosses that line for the first time: expanding from IP provider to silicon manufacturer as well. That shift matters – accelerating ARM CCA adoption across the industry.ARM CCA hardware is arriving – and more to come: 🔹 NVIDIA’s Vera CPU (including Vera Rubin platform) and Fujitsu’s next-gen server CPU – bringing CCA deeper into AI infrastructure
Super Protocol Announcements
27 Mar, 17:14
Everyone is talking about what AI agents are allowed to do. Fewer people are asking whether you can prove they actually did it.AI agents are moving fast from demos into production – and they are not just answering questions anymore. They access databases, process sensitive records, call internal APIs. NVIDIA introduced NemoClaw at GTC 2026 to govern exactly that: policy enforcement, network guardrails, privacy routing. The kind of foundation the space needs.But there is a layer underneath that often gets skipped: Can you verify the environment the agent is actually running in? Because if the infrastructure is not attested, every policy still comes down to trust in whoever is running it.With Super Swarm, agents run in environments that are hardware-isolated and cryptographically attested – with verifiable evidence of what actually ran and under what conditions, independently
Super Protocol Announcements
17 Mar, 14:11
"Confidential Computing is super important." – Jensen Huang, NVIDIA GTC 2026At GTC 2026, Confidential Computing is placed right at the center of the NVIDIA AI Platform – between Blackwell and Rubin, as part of the foundation.To scale AI globally, you must protect everything – even from the infrastructure operator itself.That's the stack we've been building. Super Swarm: open-source by design, self-organizing CC clusters. NVIDIA provides the hardware. Super makes it deployable – any cloud, on-prem, hybrid, and even air-gapped environments. Verifiable by any party, at any time.🎥 nvidia.com/gtc/keynote on CC (1:02:30) 🔗 superprotocol.com#GTC2026 #NVIDIA #ConfidentialComputing #TEE #AIInfrastructure #Blackwell #VeraRubin
Super Protocol Announcements
13 Mar, 16:51edited
Yesterday at Open Confidential Computing Conference (OC3), the confidential computing ecosystem shared its insights.Our COO Yulia Gontar joined the Confidential Computing Consortium (CCC) to showcase the real-world impact of verifiable AI. We brought six projects that solve a universal structural problem: AI workloads require scalable high-performance compute but cannot afford to expose sensitive data or proprietary models to the provider, or any other participant.The Proof Grid (as presented at OC3): 🔹 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐀𝐈: MedGemma-27B achieving a 9.4/10 doctor score inside a verifiably confidential environment. 🔹 𝐒𝐦𝐚𝐫𝐭 𝐇𝐨𝐬𝐩𝐢𝐭𝐚𝐥: Real-time EHR-to-Clinician AI on NVIDIA Blackwell (B200) via Nebius. 🔹 𝐅𝐃𝐀 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Cutting AI audit submissions from 4 weeks to 2 hours. 🔹 𝐀𝐝𝐓𝐞𝐜𝐡: Unlocking 319% growth on external training data for Mars & Realeyes.
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