Confidential Computing That Protects Data During Processing

Deploy secure enclaves with TEEs, attestation, and memory encryption to protect data in use and meet GDPR, HIPAA, and PCI DSS compliance.

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Trusted and top rated tech team

Cloud providers can access your data

Encrypted storage and TLS protect data at rest and in transit, but information becomes visible during processing. Cloud provider admins, compromised hypervisors, and malicious insiders can access plaintext in memory. We implement confidential computing with hardware-based secure enclaves, attestation protocols, and Trusted Execution Environments so your sensitive data stays encrypted even during active computation in untrusted cloud infrastructure.

Our capabilities include:

Secure enclave deployment (Intel SGX, AMD SEV)
TEE implementation and configuration
Hardware attestation protocol setup
Confidential AI workload architecture
Multi-party computation frameworks
Cloud-native enclave integration (AWS, Azure, GCP)

Who we support

Traditional encryption leaves information exposed during processing. We help organizations implement confidential computing where secure enclaves protect sensitive information throughout computation, not just storage and transmission.

Healthcare Processing Patient Data

You need to analyze patient records in the cloud but HIPAA prohibits exposing PHI to third parties. Traditional encryption doesn't protect information during computation, cloud provider access violates compliance, and on-premise processing limits scalability and collaboration with research partners.

Financial Services Processing Transactions

Your team processes transaction data, credit scores, and fraud detection models in cloud environments. PCI DSS and regulatory audits require proving information remains confidential, your current setup can't guarantee cloud providers don't access plaintext, and compliance teams block cloud migration.

Companies Building Confidential AI

You train machine learning models on proprietary or sensitive datasets and need to protect both training data and model weights. Cloud GPU costs make sense economically, but exposing intellectual property or customer data to cloud providers creates unacceptable risk.

Ways to engage

We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.

Staff Augmentation

Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.

Retainer Services

Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.

Project Engagement

Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.

We'll spec out a custom engagement model for you

Invested in creating success and defining new standards

At Curotec, it is more than just the solutions we build. We value relationships between our people and our clients — that partnership is why CEOs, CTOs, and CMOs turn to Curotec.

Replatforming a clinical decision support tool used by physicians globally

Why choose Curotec for confidential computing?

Our engineers deploy secure enclaves using Intel SGX, AMD SEV, and cloud-native TEE services with proper attestation and key management. We build confidential AI workloads, implement multi-party computation, and integrate hardware-based encryption into your applications. This protects your data during active processing, preventing cloud providers or compromised systems from accessing plaintext in memory.

1 Extraordinary people, exceptional outcomes

Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.

2 Deep technical expertise

We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.

3 Balancing innovation with practicality

We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.

4 Flexibility in our approach

We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.

Security capabilities that protect data during computation

Hardware-Based Secure Enclaves

Isolate sensitive workloads in TEEs so data remains encrypted in memory even when cloud provider admins access the host system.

Cryptographic Attestation

Verify enclave integrity before processing so you confirm code and firmware haven't been tampered with prior to releasing sensitive data.

Memory Encryption

Encrypt data during active computation so plaintext never appears in memory where hypervisors or system administrators could access it.

Confidential AI Processing

Train and run ML models on sensitive datasets so you leverage cloud GPU power without exposing proprietary algorithms or training data.

Multi-Party Computation

Analyze combined datasets from multiple organizations so you gain insights from pooled data without any party revealing their raw information.

Cloud-Native Enclave Integration

Run confidential workloads on AWS Nitro, Azure Confidential VMs, or GCP Confidential Computing without needing custom hardware.

Technologies for hardware-enforced data protection

Hardware Security Technologies

Our engineers implement CPU-based security extensions and cryptographic processors that create isolated execution environments.

Intel SGX (Software Guard Extensions) — CPU instructions creating application-level enclaves with hardware-encrypted memory isolation
AMD SEV (Secure Encrypted Virtualization) — VM-level memory encryption protecting entire guest operating systems from hypervisor access
AMD SEV-SNP (Secure Nested Paging) — Enhanced SEV with integrity protection preventing memory remapping attacks on encrypted VMs
Intel TDX (Trust Domain Extensions) — Hardware isolation for VMs with attestation and memory encryption independent of hypervisor
ARM TrustZone — Processor security extension creating secure and normal worlds for mobile and edge computing isolation
NVIDIA H100 Confidential Computing — GPU with TEE support enabling secure AI training and inference on encrypted datasets

Cloud-Native Enclave Services

Curotec deploys managed enclave services from major cloud providers that eliminate custom hardware procurement and management.

AWS Nitro Enclaves — Isolated compute environments on EC2 with cryptographic attestation and no persistent storage or network access
Azure Confidential VMs — AMD SEV-SNP based virtual machines with encrypted memory and attestation through Microsoft Azure Attestation
Google Confidential VMs — AMD SEV encrypted VMs with memory encryption by default and integration with Secret Manager
Azure Container Instances (Confidential) — TEE-based containers running workloads with hardware isolation without VM overhead
Azure Kubernetes Service (Confidential) — Kubernetes nodes running confidential containers with SGX or SEV-SNP enclave support
IBM Cloud Hyper Protect — LinuxONE-based confidential computing with full stack encryption and tamper-responsive hardware

Attestation & Verification

We implement attestation protocols that cryptographically verify enclave integrity before releasing sensitive information to TEEs.

Intel Attestation Service — Remote attestation verifying SGX enclave code, platform security version, and configuration before data release
Azure Attestation — Microsoft service validating TEE properties with JWT-based attestation tokens for Azure confidential computing
Google Cloud Confidential Space — Attestation framework for workload identity verification in confidential VMs and containers
Open Enclave SDK — Cross-platform development framework with unified attestation APIs across Intel SGX and ARM TrustZone
Gramine — LibOS enabling unmodified applications to run in SGX enclaves with attestation and secure file system support
Veraison — Open-source attestation verification framework supporting multiple hardware TEE platforms and protocols

Confidential AI & Machine Learning

Our teams architect ML pipelines with encrypted training data, protected model weights, and secure inference without data exposure.

NVIDIA Confidential Computing — H100 GPU support for training models on encrypted data with TEE protection during computation
Azure Machine Learning (Confidential) — Managed ML service with confidential compute for training on sensitive datasets in encrypted enclaves
Fortanix Confidential AI — Platform for deploying ML models in SGX enclaves protecting intellectual property and inference data
BeeKeeperAI — Healthcare-focused confidential computing enabling multi-institutional AI collaboration without sharing patient records
Opaque Systems — Encrypted data analytics and ML platform built on SGX for processing sensitive data in untrusted clouds
TensorFlow Privacy — Library implementing differential privacy and secure aggregation for confidential model training

Multi-Party Computation Frameworks

Curotec implements secure computation protocols that allow multiple parties to analyze combined data without revealing individual inputs.

Microsoft SEAL — Homomorphic encryption library enabling computation on encrypted data without decryption during processing
Google Private Join and Compute — Protocol for computing functions over joined datasets while keeping individual records private
Conclave — Platform combining SGX enclaves with secure multi-party computation for private data collaboration
Cape Privacy — Confidential computing platform for secure data science collaboration across organizational boundaries
Inpher — Secret computing platform enabling analytics and ML on encrypted data with multi-party computation protocols
OpenMined — Open-source framework for privacy-preserving machine learning with federated learning and differential privacy

Development Tools & SDKs

We use development kits and frameworks that simplify building applications for TEEs with security primitives and attestation support.

Intel SGX SDK — Development toolkit for building enclave applications with cryptographic primitives and sealing APIs
Open Enclave SDK — Cross-platform framework abstracting TEE differences for portable confidential computing applications
Gramine (formerly Graphene) — Library OS running unmodified Linux applications in SGX enclaves without code modification
Enarx — WebAssembly-based runtime for confidential computing supporting multiple TEE architectures with unified deployment
Occlum — Memory-safe LibOS for SGX enclaves written in Rust with multi-process support and file system encryption
Confidential Containers — CNCF project enabling Kubernetes pods to run in hardware TEEs with attestation and encryption

FAQs about confidential computing

How does confidential computing differ from encryption?

Traditional encryption protects data at rest and in transit but data becomes visible during processing. Confidential computing keeps data encrypted in memory using hardware-based secure enclaves, so plaintext never appears even while computation happens. Cloud providers and system administrators cannot access the data.

How do you implement confidential computing?

We assess workload requirements, select appropriate TEE technology (Intel SGX, AMD SEV, cloud enclaves), implement attestation protocols, and migrate applications to secure enclaves. Implementation includes key management setup, performance optimization, and compliance validation for regulatory requirements.

Does confidential computing impact performance?

Impact varies by technology and workload. SGX enclaves typically add 5-15% overhead for CPU-intensive tasks. AMD SEV has minimal performance impact for VM-level encryption. We optimize enclave design, minimize boundary crossings, and benchmark workloads to ensure acceptable performance.

Which cloud providers support confidential computing?

AWS offers Nitro Enclaves, Azure provides Confidential VMs and AKS support, and GCP has Confidential VMs with AMD SEV. All three support different TEE technologies. We help select the right platform based on your workload requirements, compliance needs, and existing infrastructure.

Can existing applications use confidential computing?

Some applications run in enclaves with minimal changes using frameworks like Gramine or Occlum. Others require refactoring to separate sensitive operations into enclave code. We assess application architecture, identify sensitive data flows, and determine the most efficient migration approach.

How long does implementation take?

Our engineers typically deploy proof-of-concept enclaves and validate attestation within 2-3 weeks. Full production implementation with application migration, performance optimization, and compliance documentation takes 6-8 weeks depending on application complexity and regulatory requirements.

Ready to have a conversation?

We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.