OPAQUE Studio
OPAQUE Studio is a platform that lets enterprises deploy AI agents on sensitive data inside hardware-secured environments, keeping data encrypted during execution and continuously verifying that approved policies are enforced.
Publisher review
OPAQUE Studio is a platform that lets enterprises deploy AI agents on sensitive data inside hardware-secured environments, keeping data encrypted during execution and continuously verifying that approved policies are enforced. It is designed for data scientists, ML engineers, and compliance officers who need to run AI workloads on regulated or proprietary data without exposing that data to the cloud provider or the AI model vendor. The platform generates verifiable proof of what ran, where it ran, and which policies were enforced, providing audit-ready evidence on demand. OPAQUE Studio targets organizations in finance, healthcare, and government where data privacy and governance are non-negotiable.
The platform secures AI workloads at runtime by executing them inside trusted execution environments (TEEs), which are hardware-enforced enclaves that isolate code and data from the host operating system. Data remains encrypted in memory and storage throughout the entire AI lifecycle, from training to inference. OPAQUE Studio defines and enforces policies automatically at every step of an AI workflow, ensuring that no unauthorized access or data leakage occurs. It continuously verifies that approved policies are enforced and generates verifiable proof of execution, including which policies were applied, where the workload ran, and what code was executed. This proof can be used for internal audits, regulatory compliance, and customer assurance.
OPAQUE Studio competes with platforms like Gemini Enterprise Agent Platform and StackAI, which also offer AI agent deployment but typically lack hardware-level data isolation and verifiable runtime guarantees. Unlike these alternatives, OPAQUE Studio provides a common trust layer for consistent AI governance across teams and clouds, reducing runtime security risk and preventing data leakage. The platform is positioned as the first to offer verifiable privacy and governance for AI workloads, with a focus on enterprises that require audit-ready evidence for compliance with regulations such as GDPR, HIPAA, and SOC 2.
The primary trade-off is that OPAQUE Studio requires infrastructure that supports hardware-secured environments (e.g., Intel SGX or AMD SEV), which may not be available in all cloud regions or on-premises setups. This can increase deployment complexity and cost compared to standard AI platforms. Additionally, the platform is relatively new, and the ecosystem of pre-built integrations and model support is smaller than that of established competitors. Organizations with simple AI workloads that do not involve sensitive data may find the overhead of TEEs unnecessary. Finally, pricing is not publicly disclosed, which can make budgeting difficult for small teams.
How it works
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Hardware-secured execution
Runs AI workloads inside trusted execution environments (TEEs) that isolate code and data from the host OS and cloud provider.
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Encrypted data processing
Keeps data encrypted during execution, ensuring that even the platform operator cannot access raw data in memory or storage.
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Continuous policy enforcement
Continuously verifies that approved data usage and access policies are enforced throughout the AI lifecycle, from training to inference.
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Verifiable proof generation
Generates cryptographic proof of what code ran, where it ran, and which policies were enforced, enabling audit-ready evidence on demand.
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Runtime workload security
Secures AI workloads at runtime by preventing unauthorized access, data leakage, and tampering during execution.
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Policy lifecycle management
Defines and enforces policies across the entire AI lifecycle, including data ingestion, model training, and inference.
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Cross-cloud trust layer
Provides a common trust layer for consistent AI governance across multiple teams, clouds, and on-premises environments.
Strengths and trade-offs
Strengths
- Ensures verifiable privacy and governance for AI workloads by executing them inside hardware-enforced TEEs that keep data encrypted at all times.
- Enforces policies automatically at every step of an AI workflow, from data ingestion to inference, reducing the risk of human error or misconfiguration.
- Reduces runtime security risk and prevents data leakage by isolating AI workloads from the host OS and cloud provider infrastructure.
- Generates audit-ready evidence on demand, including cryptographic proof of execution, policy enforcement, and data location, which can be used for compliance audits.
Trade-offs
- Requires cloud or on-premises infrastructure that supports hardware-secured environments such as Intel SGX or AMD SEV, which may not be available in all regions.
- Increases deployment complexity and cost compared to standard AI platforms due to the need for TEE-compatible hardware and additional configuration.
- Has a smaller ecosystem of pre-built integrations and model support than established competitors like Gemini Enterprise Agent Platform.
- Pricing is not publicly disclosed, making it difficult for small teams or startups to budget for the platform without a sales conversation.
Pricing context
Not specified in the provided sources.
Getting started with OPAQUE Studio
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Sign up for OPAQUE Studio
Visit the OPAQUE Studio website and create an account. Provide your enterprise email and organization details. Complete the onboarding form to request access, as the platform may require approval for new users.
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Connect your data sources
In the OPAQUE Studio dashboard, navigate to the data connections section. Add your sensitive data sources by providing connection strings or uploading encrypted datasets. Ensure your data is in a supported format for TEE processing.
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Configure AI workload policies
Define data usage and access policies for your AI workload. Use the policy editor to set rules for data ingestion, model training, and inference. Specify which users or roles can access the workload and under what conditions.
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Deploy an AI agent inside a TEE
Select a pre-built AI model or upload your own. Choose a trusted execution environment (TEE) from the available hardware options, such as Intel SGX. Deploy the agent, which will run inside the TEE with encrypted data and enforced policies.
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Generate verifiable audit proof
After the workload runs, request a cryptographic proof from the platform. This proof includes details of the executed code, policies enforced, and data location. Download the proof for compliance audits or internal review.
Frequently Asked Questions
What is OPAQUE Studio and what does it do?
OPAQUE Studio is a platform that lets enterprises deploy AI agents on sensitive data inside hardware-secured environments. It keeps data encrypted during execution and continuously verifies that approved policies are enforced, providing audit-ready evidence for compliance.
How does OPAQUE Studio secure AI workloads on sensitive data?
It runs AI workloads inside trusted execution environments (TEEs), which are hardware-enforced enclaves that isolate code and data from the host operating system. Data remains encrypted in memory and storage throughout the entire AI lifecycle, preventing unauthorized access.
What kind of proof does OPAQUE Studio generate for audits?
OPAQUE Studio generates cryptographic proof of what code ran, where it ran, and which policies were enforced. This verifiable evidence can be used for internal audits, regulatory compliance with GDPR, HIPAA, and SOC 2, and customer assurance.
How does OPAQUE Studio compare to platforms like Gemini Enterprise Agent Platform?
Unlike competitors that lack hardware-level data isolation, OPAQUE Studio provides a common trust layer for consistent AI governance across teams and clouds. It reduces runtime security risk and prevents data leakage by executing workloads inside TEEs with verifiable guarantees.
What are the main trade-offs of using OPAQUE Studio?
OPAQUE Studio requires infrastructure that supports hardware-secured environments like Intel SGX or AMD SEV, which may not be available everywhere. This increases deployment complexity and cost, and the ecosystem of pre-built integrations is smaller than established competitors.
Who is OPAQUE Studio designed for?
OPAQUE Studio targets data scientists, ML engineers, and compliance officers in finance, healthcare, and government. These organizations need to run AI workloads on regulated or proprietary data without exposing that data to the cloud provider or AI model vendor.
Alternatives
How OPAQUE Studio compares
Direct head-to-head against 2 competitors. Picked by 7wData.
OPAQUE Studio
- Pricing
- Not specified in the provided sources.
- Target
- OPAQUE Studio is a platform that lets enterprises deploy AI agents on sensitive data inside hardware-secured environments, keeping data encrypted during execution and continuously verifying
- Strength
- Ensures verifiable privacy and governance for AI workloads by executing them inside hardware-enforced TEEs that keep data encrypted at all times.
- Watch for
- Requires cloud or on-premises infrastructure that supports hardware-secured environments such as Intel SGX or AMD SEV, which may not be available in all regions.
LangGraph
- Pricing
- Custom/Contact sales
- Target
- AI agent development teams
- Deployment
- Cloud, hybrid
- Strength
- Open-source agent orchestration framework
- Watch for
- Early-stage project, limited enterprise support
Anthropic
- Pricing
- $15/million tokens (Claude 3 Opus)
- Target
- Enterprises needing secure AI
- Deployment
- API, cloud
- Strength
- Constitutional AI for safety
- Watch for
- No verifiable execution guarantees
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Sources
Reporting on this tool draws on these publicly available sources.