All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
Opaque Systems turns the clouds' own security hardware into what it says the clouds do not sell: proof that AI ran on sensitive data without exposing it. Its platform runs AI workloads and agents inside confidential virtual machines where data stays encrypted even during processing, and exports signed evidence of what ran and which policies held. The team, out of UC Berkeley's RISELab with Databricks co-founder Ion Stoica, has raised $55.5 million, most recently in February 2026. Its biggest exposure is its suppliers: Microsoft, Google, and AWS operate that hardware and sell confidential virtual machines themselves, so the policy and audit layer Opaque adds is one bundling decision from a cloud feature. Most defensible where a buyer needs the same AI audit evidence across several clouds.
| Description | Opaque Systems sells a confidential AI platform that runs AI workloads and agents inside hardware-attested trusted execution environments in the customer's cloud, keeping data encrypted even while it is processed. | [f1] |
|---|---|---|
| Founded | 2021 | [f2] |
| HQ | San Francisco, California, US | [f3] |
| Funding | $55.5M total | [f2] |
| Latest funding | Series B, $24M (2026) | [f2] |
| Product | What it does |
|---|---|
| Opaque Confidential AI Platform | Confidential AI platform that runs AI agents and workloads inside hardware-attested confidential virtual machines in the customer's cloud, enforcing runtime policies and producing audit evidence. |
AI Defense Matrix
| Govern | Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|---|
| AI-Workload Platforms Inference servers, training platforms, vector DB platforms, and the model-loading supply chain. | ||||||
| AI Orchestration Tools Agentic orchestration tools, plus their plugins, skills, hooks, system prompts, scaffolding, harnesses, configuration settings, and MCP clients on user devices. | ||||||
| AI-Generated Code Code produced by AI tools, AI-assisted reviews, AI-generated infrastructure-as-code and tests, and vibe-coded apps that bypass CI/CD. | ||||||
| AI Gateways & Routers MCP proxies and gateways, LLM routers, outbound AI-service traffic, shadow AI egress, and model-registry traffic. | ||||||
| AI Model Model weights, fine-tuning checkpoints, model cards, registries, AIBOM, and the third-party LLMs your enterprise consumes. | ||||||
| Training Data Datasets used for training, fine-tuning, and continued learning. | ||||||
| Runtime AI Data User prompts, inference inputs, RAG content, vector DB content, persistent agent memory, and interaction history. | ||||||
| AI Agent Identities AI agents as non-human principals, plus credentials, keys, permission scopes, service accounts, and delegation chains across agents and tools. |
The Opaque Confidential AI Platform runs AI agents and workloads inside hardware-attested confidential virtual machines in the customer's cloud, keeping data encrypted in memory during processing. These capabilities are mapped to the AI Defense Matrix. [f4]
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score | Rationale |
|---|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 | Opaque names a specific buyer, the regulated enterprise whose privacy, policy, and audit concerns stall AI deployments, and press frames the same use cases on financial and healthcare data. The pain is argued by the vendor and by category-level analyst language rather than quantified independently, the present-but-unproven middle. [s5, s6, s2] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 4/5 | Public product pages and the FAQ document the runtime architecture, confidential virtual machines, GPU-backed processing, and signed attestation artifacts, and external validation points exist: the founders' peer-reviewed NSDI 2017 system paper, the open-source MC2 lineage, and Microsoft's Azure confidential-computing partner page. [s2, s15, s8, s9] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 | The enabler is recent and sourced: the major clouds now offer GPU-backed confidential virtual machines, Azure's H100-enabled instances among them, which the vendor positions as the foundation for large-scale encrypted-in-use AI, and the February 2026 round funds the resulting confidential AI push. Buyer-side demand is still indirect, press-framed use cases and vendor-cited analyst language rather than independent budget evidence. [s15, s5, s6] |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 4/5 | Independent reporting documents founders including UC Berkeley professors Raluca Ada Popa and Ion Stoica, a Databricks co-founder, and the team published the peer-reviewed Opaque systems paper at NSDI 2017 before founding the company. Verifiable prior builds plus a publication record, multiply evidenced, short of the category-defining bar. [s4, s8, s3] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 3/5 | Named references exist but are vendor-published: a logo wall naming ServiceNow, Anthropic, Wells Fargo, and Accenture, case studies with ServiceNow and Bloomfilter, and an announced Azure Marketplace listing. No independent reporting corroborates deployment scale or revenue. [s1, s6] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 | The February 2026 Series B is proportional to stage and follows visible shipping, a platform relaunch, a technology acquisition, and a senior platform hire, but no revenue or efficiency figure is disclosed. The honest default for a funded private startup. [s5, s7, s14] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 | Confidential computing is an established category and an analyst market report lists Opaque Systems among its named vendors, but the company sells a vendor-coined variant, the confidential AI platform, that buyers still need explained and that lacks a settled budget line. [s11, s6, s12] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 | The major clouds sell confidential virtual machines natively, so the policy and audit layer Opaque adds is exposed to bundling, but cross-cloud portability, the attestation-evidence workflow, and the acquired cryptographic technology add friction a checkbox feature would not replicate. No structural moat is evidenced. [s15, s7, s9] |
Opaque Systems targets the enterprise that wants AI working on its most sensitive data but stalls at privacy, policy, and audit questions. The company frames the barrier to production AI as trust: security and compliance teams pause deployments because they cannot verify what a model or agent did with regulated data. Its answer is to run the AI where hardware enforces the protections and to hand the buyer signed evidence rather than assurances.
The buyers it courts are technology and security executives in regulated industries. The vendor names high tech, financial services, insurance, and healthcare as its segments, and its homepage speaks directly to CIOs and CTOs balancing innovation with compliance. Press coverage frames the same use cases: generative AI agents working over financial records, healthcare data, and other regulated or proprietary datasets.
Independent quantification of the pain is thin. Analyst market reports size confidential computing as a category, and the vendor cites analyst language on securing generative AI, but no independent study in the reviewed sources quantifies demand for Opaque's specific slice of it. The problem is clearly framed, and the framing is still mostly the vendor's own. [s6, s1, s5, s11]
The product runs AI where the protection is. Workloads and agents execute inside confidential virtual machines in the customer's cloud, and the hardware keeps data encrypted in memory during processing. A runtime and software development kit let teams call that execution layer directly, enforce policies and guardrails while a workload runs, and export hardware-signed attestation and audit artifacts.
Policy control spans the AI lifecycle. The product page describes controlling which data, models, agents, and tools can be used at build time, deploy time, and runtime, with automatic enforcement at each step. The company also sells packaged confidential agents for retrieval-augmented generation, and its FAQ documents GPU-backed deployments on Azure's H100-enabled confidential virtual machines for large-model serving.
The capability claims trace to more than marketing pages. The founders published the Opaque encrypted-analytics system at USENIX NSDI in 2017 and open-sourced the MC2 project at UC Berkeley, Microsoft documents Opaque on its Azure confidential-computing partner pages, and the vendor announced an Azure Marketplace listing in April 2025. In May 2026 TechAfrica News reported the acquisition of multi-party computation, fully homomorphic encryption, and post-quantum technology from the Technology Innovation Institute to extend the platform toward confidential AI training. [s2, s15, s8, s9, s7, s1]
Opaque competes on neutrality between two heavier forces. The hyperscalers sell the underlying confidential computing: an analyst vendor list puts Microsoft, Google, AMD, and AWS in the same market, and Azure sells GPU-backed confidential virtual machines directly. The same list names specialist rivals, including Fortanix, Anjuna Security, Edgeless Systems, and Decentriq, and privacy-technology vendors reach the same keep-data-protected-in-use outcome with cryptography alone.
Its differentiation is the lifecycle wrapper rather than the hardware primitive. Confidential virtual machines are becoming a standard cloud offering, and Opaque's case for existing independently is policy enforcement across build, deploy, and runtime, exportable signed evidence, and portability across clouds and agent frameworks. The June 2026 hire of the creator of Microsoft's Agent Governance Toolkit as Chief Platform Officer doubles down on the agent-governance side of that wrapper.
The absorption question shapes everything else. Opaque depends on the cloud providers for the hardware it orchestrates, features them on its own partner wall, and competes with what they could bundle next quarter. Cross-cloud buyers are the segment where that tension resolves in Opaque's favor, because no single cloud has an incentive to certify a rival's infrastructure. [s11, s15, s14, s1, s9]
Traction evidence is real but vendor-published. The homepage logo wall names ServiceNow, Anthropic, Microsoft, Accenture, Ant Group, Wells Fargo, Encore Capital Group, Bloomfilter, and The Institutes RiskStream Collaborative, and the newsroom states that customers and partners include ServiceNow, Anthropic, Encore Capital, and Accenture. Case studies with ServiceNow, Accenture, RiskStream Collaborative, and Bloomfilter appear on the vendor's site. TechCrunch reported a customer base including banks and large healthcare providers in 2022, but no outside reporting in the reviewed sources confirms revenue, customer counts, or current deployment scale.
Distribution is enterprise-direct with an emerging marketplace motion. The site sells through demo requests rather than self-service, and the vendor announced an Azure Marketplace debut in April 2025. Investors corroborate the money but not the sales: SiliconANGLE reports $55.5 million raised in total, with the February 2026 Series B led by Walden Catalyst Ventures valuing the company at roughly $300 million.
The company sells with hired executives rather than founders. Aaron Fulkerson runs the company as CEO while co-founder Rishabh Poddar leads research and the cryptographic platform as CTO, and the newest round funds expansion into post-quantum security, confidential AI training, and sovereign cloud deployments. [s1, s6, s5, s14, s3]
The founding team is independently documented and unusually academic. TechCrunch names UC Berkeley professors Raluca Ada Popa and Ion Stoica, the co-founder of Databricks, plus Berkeley graduates Rishabh Poddar, Wenting Zheng, and Chester Leung. The same group published the Opaque encrypted-analytics paper at USENIX NSDI in 2017 and open-sourced the MC2 project the company later commercialized.
Leadership has professionalized around the founders. Aaron Fulkerson serves as CEO, Poddar moved to CTO leading research and the cryptographic platform, and in June 2026 the company hired Imran Siddique, creator of Microsoft's Agent Governance Toolkit, as Chief Platform Officer to run engineering and platform strategy. Gartner named the company a 2023 Cool Vendor in Privacy, a recognition the vendor displays on its own newsroom. [s4, s8, s3, s14, s12]
Trust evidence is the product itself more than the company's own paperwork. The platform's core output is hardware-signed attestation and audit artifacts that a customer can hand to auditors and regulators, and the vendor sells that verifiability as the reason regulated enterprises can adopt AI at all.
The company's own attestation surface is thin by comparison. A probe of trust.opaque.co and security.opaque.co, the /trust, /security, and /compliance paths, and the site footer found no trust portal and no downloadable attestation report as of 2026-07-03. The homepage footer displays an AICPA SOC badge image with empty alt text, a self-displayed badge with no inspectable report behind it in the public record, and the vendor gates its architecture and security white paper behind a download form. [s2, s13, s1]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| Microsoft | competes with | Operates Azure confidential computing and sells confidential virtual machines directly, including the GPU-backed instances Opaque builds on, so it is both the platform Opaque depends on and the most plausible bundler of a native equivalent. | N/AMicrosoft is scored by product line, not as a whole company, so there is no company-wide column to compare. Open its profile to compare a specific product. |
| Fortanix | competes with | Confidential-computing platform vendor named in the same analyst market listing, securing data with trusted execution environments. | N/AWe scored these companies at different scopes, so the totals measure different things. |
| Anjuna Security | competes with | Confidential-computing software vendor in the same analyst vendor list, running applications inside enclaves and confidential virtual machines. | N/AWe captured the evidence for these companies under different evidence-model versions (v1 vs v2), so the totals were scored under different conditions and are not directly comparable. |
| Edgeless Systems | competes with | Confidential-computing vendor in the same analyst vendor list, known for open-source confidential Kubernetes tooling. | N/AWe captured the evidence for these companies under different evidence-model versions (v1 vs v2), so the totals were scored under different conditions and are not directly comparable. |
| Decentriq | competes with | Data clean rooms built on confidential computing, overlapping Opaque's earlier multi-party analytics positioning. | |
| Duality Technologies | competes with | Privacy-preserving data collaboration using homomorphic encryption, an alternative cryptographic route to the same keep-data-protected-in-use outcome Opaque reaches with hardware enclaves. | N/AWe captured the evidence for these companies under different evidence-model versions (v1 vs v2), so the totals were scored under different conditions and are not directly comparable. |
| Enveil | competes with | Privacy-enhancing technology vendor keeping data encrypted during search and analytics, competing for the same regulated-enterprise data-in-use budget. | N/AWe captured the evidence for these companies under different evidence-model versions (v1 vs v2), so the totals were scored under different conditions and are not directly comparable. |
| Confident Security | competes with | Sells verifiably private AI inference with encryption and hardware attestation, overlapping Opaque's confidential runtime promise for inference workloads. |
Add analyzed competitors to compare them side by side with Opaque Systems.
A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
reinforce or reposition
No cross-customer data asset appears in Opaque Systems' public record, so what a rival cannot quickly match is people and code. The founders published the original Opaque encrypted-analytics research in 2017, and in 2026 the company bought advanced cryptography (multi-party computation, homomorphic encryption) from the Technology Innovation Institute, expertise a rival would need years to assemble. The platform around it, policy enforcement and signed audit evidence for AI in confidential virtual machines, is the reproducible part, and the clouds sell that hardware themselves. Leaving means moving AI workloads and rebuilding audit trails, effort rather than consequence. The expertise is a head start, not yet a lock, until a named data asset or compliance mandate accumulates around it.
| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 | Opaque delivers software the customer deploys and runs in its own cloud through negotiated enterprise deals, the software-product level. The attestation evidence it produces is software output rather than a human judgment or accountability layer. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | Once production AI workloads run inside the confidential runtime with policies and audit trails built around them, leaving means moving those workloads to other infrastructure and rebuilding the evidence pipeline, meaningful integration friction. No network effect or regulatory data-residency lock appears in the record. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | Audit-ready evidence for regulated AI is the product's core promise, but the homepage AICPA SOC badge is self-displayed with no inspectable report, and no certification, liability acceptance, or mandate that would block a replacement is documented in the public record. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 | Trusted-execution orchestration, GPU-backed confidential computing, and the acquired multi-party computation and homomorphic encryption technology require years of specialized expertise, anchored by the founders' peer-reviewed systems research. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 2/3 | The vendor courts regulated enterprises, its logo wall names banks and insurers, and TechCrunch reported a customer base including banks and healthcare providers in 2022, but the displayed case studies pair it with software vendors, a consulting firm, and an insurance-industry group rather than named regulated-procurement wins, holding the evidenced buyer at the enterprise-IT level. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 3/3 | AI workloads execute inside Opaque's confidential runtime, execution infrastructure rather than an application, and removing it means moving production workloads off the platform. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 | No named non-public dataset appears in the record. MC2 is open source, and the acquired cryptographic technology is expertise and code a funded rival could also buy or build. |
Opaque Systems sells to enterprises whose data sensitivity blocks AI adoption. The vendor names high tech, financial services, insurance, and healthcare as its segments, and its homepage addresses CIOs and CTOs balancing innovation with compliance. The implied buyer runs a meaningful cloud estate already, because the platform deploys into the customer's own cloud rather than as a hosted service.
The segmentation choice concentrates on depth rather than breadth. A confidential runtime matters most where regulators, auditors, or counterparties demand evidence, which narrows the early market to large, compliance-bound organizations and to software companies that serve them. The vendor's displayed references match that split: banks and insurers on the logo wall, and case studies with software vendors and a consulting firm.
The strategic investor base points at a second, geographic motion. The newest round added a strategic partner connected to Abu Dhabi's technology establishment, and the vendor describes expansion into sovereign cloud deployments, a segment where data-control guarantees are the purchase trigger rather than a feature.
The core capability is running AI where hardware enforces protection. Workloads and agents execute inside confidential virtual machines in the customer's cloud, data stays encrypted in memory during processing, and the runtime exports hardware-signed attestation and audit artifacts. Policy control spans build time, deploy time, and runtime, covering which data, models, agents, and tools a workflow may use.
The AI-specific engineering is what separates this from generic confidential computing. The FAQ documents GPU-backed confidential environments, including Azure's H100-enabled confidential virtual machines for large-model serving, and the company sells packaged confidential agents for retrieval-augmented generation. The May 2026 announced acquisition of multi-party computation, fully homomorphic encryption, and post-quantum technology from the Technology Innovation Institute extends the roadmap toward confidential AI training.
The claims rest on an unusually deep research base. The founders published the Opaque encrypted-analytics system at USENIX NSDI in 2017 and open-sourced the MC2 project at UC Berkeley, and Microsoft documents the platform on its Azure confidential-computing partner pages, naming Opaque Workspaces and Opaque Gateway.
The motion is enterprise-direct sales run by hired executives. The site converts through demo requests rather than self-service, Aaron Fulkerson leads the company as CEO with co-founder Rishabh Poddar as CTO, and the June 2026 Chief Platform Officer hire from Microsoft adds senior engineering and platform leadership behind the agent-governance story.
Channel and marketplace routes are forming around the cloud relationship. The vendor announced an Azure Marketplace debut in April 2025, appears on Microsoft's confidential-computing partner pages, and displays Accenture among its partners, a consulting channel suited to the regulated-enterprise buyer.
What the record does not show is independently corroborated scale. TechCrunch reported in 2022 that the company had built a customer base including banks and large healthcare providers, but the named references, ServiceNow, Anthropic, Encore Capital, and Accenture among them, appear on the vendor's own pages, and no outside reporting in the reviewed sources confirms revenue, customer counts, or current deployment scale.
Opaque publishes no pricing. The site routes every path through a demo request, which usually signals negotiated, larger-ticket enterprise deals rather than product-led adoption.
The pricing unit is not publicly documented, so what the vendor believes buyers pay for cannot be read from a rate card. The product's shape suggests candidates, workloads run or infrastructure capacity protected, but the reviewed sources do not settle it, and the absence itself is consistent with an early enterprise motion where each deal is scoped individually.
Delivery happens inside the customer's cloud, not the vendor's. The platform runs on confidential computing infrastructure in the customer's environment, which keeps data under the customer's control and makes the customer's cloud bill part of the operating cost.
That choice trades operational simplicity for trust. The customer gains hardware-enforced protection and exportable evidence, and takes on running AI workloads inside a specialized runtime, with GPU-backed confidential instances required for demanding jobs such as large-model serving. A software development kit and integrations with existing data stacks are the vendor's answer to that operational weight.
The product is the trust story. Opaque sells verifiable evidence, hardware-signed attestation and audit artifacts a customer can hand to auditors and regulators, and that output is the core of its pitch to compliance-bound buyers.
The company's own public attestation surface is thinner. A probe of trust.opaque.co and security.opaque.co, the /trust, /security, and /compliance paths, and the site footer found no trust portal and no downloadable attestation report as of 2026-07-03. The homepage footer displays an AICPA SOC badge image with empty alt text, a self-displayed badge with no inspectable report behind it in the public record. Gartner named the company a 2023 Cool Vendor in Privacy, a recognition displayed on the vendor's own newsroom.
Opaque's ecosystem position is symbiotic with the companies most able to displace it. It builds on the hyperscalers' confidential-computing infrastructure, is documented on Microsoft's Azure partner pages, and announced an Azure Marketplace listing, while those same clouds sell confidential virtual machines directly and appear in the same analyst vendor list as Opaque.
The company is also positioning as the enforcement layer for other vendors' agent-governance standards. Hiring the creator of Microsoft's Agent Governance Toolkit as Chief Platform Officer ties the platform to a policy toolkit built at Microsoft, and the open-source MC2 lineage gives the company a research and open-source origin.
Model providers sit on the wall between partner and rival. Anthropic appears on the vendor's logo wall, and providers shipping their own confidential inference guarantees could shrink the independent layer to orchestration and audit.
The founding bench is the company's clearest asset. TechCrunch documents founders including UC Berkeley professors Raluca Ada Popa and Ion Stoica, the co-founder of Databricks, plus Berkeley graduates Rishabh Poddar, Wenting Zheng, and Chester Leung. Three of them, Zheng, Popa, and Stoica, coauthored the peer-reviewed Opaque systems paper with Berkeley colleagues in 2017 before the company existed.
Leadership has professionalized in stages. Poddar, the founding CEO at the 2022 Series A, now serves as CTO leading research and the cryptographic platform, Aaron Fulkerson runs the company as CEO, and Imran Siddique joined in June 2026 as Chief Platform Officer to lead engineering and platform strategy. That is the structure of a company preparing to sell into large enterprises rather than a research lab.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | Opaque product page: The Confidential AI Platform (runtime, encryption in use, attestation) | official | 2026-07-03 |
| f2 | SiliconANGLE: Opaque raises $24M at $300M valuation for confidential AI platform | press | 2026-07-03 |
| f3 | TechAfrica News: OPAQUE expands confidential AI platform with acquisition from Technology Innovation Institute (TII) | press | 2026-07-03 |
| f4 | Opaque (AI Defense Matrix Catalog mapping) | other | 2026-07-03 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Opaque homepage (customer and partner logo wall image alt text, pipe-joined; Confidential Agents for RAG; Azure Marketplace debut news card, April 2025) “Wells Fargo | Encore Capital Group | Servicenow | Anthropic | Microsoft | Accenture | Ant Group | Bloomfilter | The Institutes RiskStream Collaborative” | official | 2026-07-03 |
| s2 | Opaque product page: OPAQUE Confidential Runtime and SDK (encrypted in memory, hardware-signed attestation and audit artifacts) “Control which data, models, agents, and tools can be used, at build time, deploy time, and runtime, and have those policies enforced automatically at every step of an AI workflow.” | official | 2026-07-03 |
| s3 | Opaque about page: UC Berkeley RISELab founders and leadership (founded 2021, Aaron Fulkerson CEO, Imran Siddique Chief Platform Officer) “Together, Professor Ion Stoica (also founder and chairman of Databricks), Professor Raluca Ada Popa, Dr. Rishabh Poddar, Professor Wenting Zheng, and Chester Leung created, incubated, and open-sourced the breakthrough MC2 (Multiparty Collaboration and Competition) platform” | official | 2026-07-03 |
| s4 | TechCrunch: Opaque Systems secures cash to keep data private (Kyle Wiggers, June 28, 2022, $22M Series A, total $31.6 million) “Opaque was founded by University of California, Berkeley professors Raluca Ada Popa and Ion Stoica, the co-founder of Databricks, as well as UC Berkeley graduates Rishabh Poddar, Wenting Zheng and Chester Leung.” | press | 2026-07-03 |
| s5 | SiliconANGLE (Duncan Riley, February 12, 2026): Opaque raises $24 million at $300 million valuation (Series B led by Walden Catalyst Ventures) “The new funding takes the total raised by Opaque to $55.5 million. The company's previous rounds include $9.5 million in July 2021 and $22 million in June 2022” | press | 2026-07-03 |
| s6 | Opaque newsroom: $24M Series B (customers and partners include ServiceNow, Anthropic, Encore Capital, Accenture) “This brings OPAQUE's total funding to $55.5 million and values the company at approximately $300 million post-money.” | official | 2026-07-03 |
| s7 | TechAfrica News (May 4, 2026): OPAQUE acquires cryptographic AI technologies from Abu Dhabi's Technology Innovation Institute (TII) “The acquired technologies, already validated in real-world applications, strengthen OPAQUE's platform with capabilities for confidential AI model training using techniques such as multi-party computation and fully homomorphic encryption, alongside post-quantum cryptographic protections.” | press | 2026-07-03 |
| s8 | USENIX NSDI 2017: Opaque, an oblivious and encrypted distributed analytics platform (Zheng, Popa, Stoica et al., UC Berkeley) “Opaque: An Oblivious and Encrypted Distributed Analytics Platform. Wenting Zheng, Ankur Dave, Jethro G. Beekman, Raluca Ada Popa, Joseph E. Gonzalez, and Ion Stoica, University of California, Berkeley” | research | 2026-07-03 |
| s9 | Microsoft Learn: Opaque Systems, Inc. partner page under Azure confidential computing (Opaque Workspaces, Opaque Gateway) “Opaque is the confidential AI platform unlocking sensitive data to securely accelerate AI into production. Created by world-renowned researchers at the Berkeley RISELab, Opaque's user-friendly platform empowers organizations to run cloud-scale, general purpose AI workloads on encrypted data.” | research | 2026-07-03 |
| s10 | VentureBeat (Victor Dey, June 22, 2023): Opaque Systems unveils confidential AI and analytics tools (Jay Harel, VP of product) “They comprise a privacy-preserving generative AI optimized for Microsoft Azure's Confidential Computing Cloud, and a zero-trust analytics platform: Data Clean Room (DCR).” | press | 2026-07-03 |
| s11 | MarketsandMarkets confidential computing market report: major vendors list naming Opaque Systems (US) alongside Decentriq, Edgeless Systems, and Anjuna Security “major vendors offering confidential computing across the globe are Microsoft (US), IBM (US), Intel (US), Google (US), AMD (US), Fortanix (US), AWS (US), Arm (UK), Alibaba Cloud (China), Swisscom (Switzerland)” | research | 2026-07-03 |
| s12 | Opaque newsroom: Opaque Systems named a 2023 Cool Vendor in Privacy by Gartner (vendor-displayed recognition) “Gartner, Cool Vendors in Privacy, 2023, Bart Willemsen, Bernard Woo, Nader Henein, 14 August 2023.” | official | 2026-07-03 |
| s13 | Trust surface probe (2026-07-03): trust./security. subdomains unresolvable, /trust /security /compliance 404, HTML grep found footer AICPA SOC badge, empty alt | other | 2026-07-03 |
| s14 | PR Newswire (Opaque announcement, June 3, 2026): OPAQUE names creator of Microsoft's Agent Governance Toolkit as Chief Platform Officer “Imran Siddique, the creator of Microsoft's Agent Governance Toolkit (AGT), will join the company as Chief Platform Officer. He will lead OPAQUE's engineering organization and platform strategy, working with co-founder and CTO Rishabh Poddar” | press | 2026-07-03 |
| s15 | Opaque FAQ: GPU-backed confidential computing for RAG and LLM workloads “It supports GPU-backed confidential computing environments, such as Azure's H100-enabled confidential VMs, which are engineered for secure, large-scale AI processing.” | official | 2026-07-03 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Opaque homepage (customer and partner logo wall image alt text, pipe-joined; Confidential Agents for RAG; Azure Marketplace debut news card, April 2025) “Wells Fargo | Encore Capital Group | Servicenow | Anthropic | Microsoft | Accenture | Ant Group | Bloomfilter | The Institutes RiskStream Collaborative” | official | 2026-07-03 |
| s2 | Opaque product page: OPAQUE Confidential Runtime and SDK (encrypted in memory, hardware-signed attestation and audit artifacts) “Control which data, models, agents, and tools can be used, at build time, deploy time, and runtime, and have those policies enforced automatically at every step of an AI workflow.” | official | 2026-07-03 |
| s3 | Opaque about page: UC Berkeley RISELab founders and leadership (founded 2021, Aaron Fulkerson CEO, Imran Siddique Chief Platform Officer) “Together, Professor Ion Stoica (also founder and chairman of Databricks), Professor Raluca Ada Popa, Dr. Rishabh Poddar, Professor Wenting Zheng, and Chester Leung created, incubated, and open-sourced the breakthrough MC2 (Multiparty Collaboration and Competition) platform” | official | 2026-07-03 |
| s4 | TechCrunch: Opaque Systems secures cash to keep data private (Kyle Wiggers, June 28, 2022, $22M Series A, total $31.6 million) “Opaque was founded by University of California, Berkeley professors Raluca Ada Popa and Ion Stoica, the co-founder of Databricks, as well as UC Berkeley graduates Rishabh Poddar, Wenting Zheng and Chester Leung.” | press | 2026-07-03 |
| s5 | SiliconANGLE (Duncan Riley, February 12, 2026): Opaque raises $24 million at $300 million valuation (Series B led by Walden Catalyst Ventures) “The new funding takes the total raised by Opaque to $55.5 million. The company's previous rounds include $9.5 million in July 2021 and $22 million in June 2022” | press | 2026-07-03 |
| s6 | Opaque newsroom: $24M Series B (customers and partners include ServiceNow, Anthropic, Encore Capital, Accenture) “This brings OPAQUE's total funding to $55.5 million and values the company at approximately $300 million post-money.” | official | 2026-07-03 |
| s7 | TechAfrica News (May 4, 2026): OPAQUE acquires cryptographic AI technologies from Abu Dhabi's Technology Innovation Institute (TII) “The acquired technologies, already validated in real-world applications, strengthen OPAQUE's platform with capabilities for confidential AI model training using techniques such as multi-party computation and fully homomorphic encryption, alongside post-quantum cryptographic protections.” | press | 2026-07-03 |
| s8 | USENIX NSDI 2017: Opaque, an oblivious and encrypted distributed analytics platform (Zheng, Popa, Stoica et al., UC Berkeley) “Opaque: An Oblivious and Encrypted Distributed Analytics Platform. Wenting Zheng, Ankur Dave, Jethro G. Beekman, Raluca Ada Popa, Joseph E. Gonzalez, and Ion Stoica, University of California, Berkeley” | research | 2026-07-03 |
| s9 | Microsoft Learn: Opaque Systems, Inc. partner page under Azure confidential computing (Opaque Workspaces, Opaque Gateway) “Opaque is the confidential AI platform unlocking sensitive data to securely accelerate AI into production. Created by world-renowned researchers at the Berkeley RISELab, Opaque's user-friendly platform empowers organizations to run cloud-scale, general purpose AI workloads on encrypted data.” | research | 2026-07-03 |
| s10 | VentureBeat (Victor Dey, June 22, 2023): Opaque Systems unveils confidential AI and analytics tools (Jay Harel, VP of product) “They comprise a privacy-preserving generative AI optimized for Microsoft Azure's Confidential Computing Cloud, and a zero-trust analytics platform: Data Clean Room (DCR).” | press | 2026-07-03 |
| s11 | MarketsandMarkets confidential computing market report: major vendors list naming Opaque Systems (US) alongside Decentriq, Edgeless Systems, and Anjuna Security “major vendors offering confidential computing across the globe are Microsoft (US), IBM (US), Intel (US), Google (US), AMD (US), Fortanix (US), AWS (US), Arm (UK), Alibaba Cloud (China), Swisscom (Switzerland)” | research | 2026-07-03 |
| s12 | Opaque newsroom: Opaque Systems named a 2023 Cool Vendor in Privacy by Gartner (vendor-displayed recognition) “Gartner, Cool Vendors in Privacy, 2023, Bart Willemsen, Bernard Woo, Nader Henein, 14 August 2023.” | official | 2026-07-03 |
| s13 | Trust surface probe (2026-07-03): trust./security. subdomains unresolvable, /trust /security /compliance 404, HTML grep found footer AICPA SOC badge, empty alt | other | 2026-07-03 |
| s14 | PR Newswire (Opaque announcement, June 3, 2026): OPAQUE names creator of Microsoft's Agent Governance Toolkit as Chief Platform Officer “Imran Siddique, the creator of Microsoft's Agent Governance Toolkit (AGT), will join the company as Chief Platform Officer. He will lead OPAQUE's engineering organization and platform strategy, working with co-founder and CTO Rishabh Poddar” | press | 2026-07-03 |
| s15 | Opaque FAQ: GPU-backed confidential computing for RAG and LLM workloads “It supports GPU-backed confidential computing environments, such as Azure's H100-enabled confidential VMs, which are engineered for secure, large-scale AI processing.” | official | 2026-07-03 |
This site is an experimental research aid created by Zeltser Security Corp. All its data gathering and analysis was performed autonomously without human review, and it can contain errors of fact, interpretation, and judgment that a human reviewer might catch.
The analyses are statements of opinion, not statements of fact. Machine analysis produced the scores, summaries, and matrix placements by weighing the public sources each page cites, and reasonable people can weigh the same sources differently. Where a page states a fact, it cites the public source and the date it was checked, and the statement is only as accurate as that source. Unless a profile expressly says otherwise, the analysis involves no hands-on testing and no independent validation of any company's products or services.
Nothing here is professional, security, legal, financial, investment, or purchasing advice, and nothing here is a recommendation to invest in, do business with, or avoid any company. Inclusion of a company is not an endorsement, and absence of a company is not a judgment about it. Reading this site creates no advisory or client relationship. Verify any detail you plan to act on against the vendor's current materials.
The content is provided "as is" and "as available," with all warranties disclaimed, express or implied, including merchantability, fitness for a particular purpose, accuracy, and non-infringement. No entry is warranted to be complete, current, or correct. Companies change, vendors update their claims, sources can be wrong, and automated analysis can misread them.
To the fullest extent permitted by law, the operator, Zeltser Security Corp, is not liable for any damages that arise from using this site or relying on its content, including direct, indirect, incidental, special, and consequential damages and lost profits, even if advised that such damages were possible. If you are dissatisfied with the site or disagree with these terms, your remedy is to stop using it.
Entries link to vendor pages, press coverage, and other external sites that Zeltser Security Corp does not control and is not responsible for. A link is not an affiliation with the destination or an endorsement of it. Product and company names and trademarks are the property of their owners, used here nominatively to identify the companies described. Short quotations from cited sources appear for identification and commentary.
Use, quotation, automated retrieval, and redistribution of the content are governed by the Terms of Use at cybercompanyprofiles.com/terms, which permit personal and internal business use with attribution and prohibit republication and resale.
