Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
End-to-end integration of confidential computing hardware to protect sensitive AI data during active processing.
Your proprietary models and sensitive data are exposed in memory during AI inference. We integrate hardware-based Trusted Execution Environments (TEEs) like AWS Nitro Enclaves and Azure Confidential VMs directly into your AI pipelines, creating secure memory enclaves where data is processed in plaintext, isolated from the host OS, hypervisor, and cloud provider staff.
Move beyond encrypted data at rest and in transit. We ensure your most valuable assets—AI models and the data they process—are protected where they are most vulnerable: during computation.
Our end-to-end integration of confidential computing hardware delivers measurable business value by protecting your most sensitive AI assets while they are actively in use, enabling new revenue streams and ensuring regulatory compliance.
Deploy AI models and process sensitive inference data within hardware-enforced memory enclaves (AWS Nitro, Azure CVMs). This prevents intellectual property theft and data exfiltration, even from privileged insiders or a compromised cloud stack.
Achieve compliance with stringent data-in-use mandates under GDPR, HIPAA, and the EU AI Act by design. Our attested TEE integrations provide the technical controls auditors require, reducing time-to-compliance for new AI products in regulated sectors like finance and healthcare.
Safely operationalize AI for high-stakes use cases like biometric processing, algorithmic trading, and clinical decision support. Hardware-rooted attestation guarantees model integrity and data confidentiality, mitigating operational and reputational risk.
Consolidate point security solutions with a hardware-based root of trust. By embedding security into the compute layer, you eliminate overhead from software-based encryption wrappers and reduce the complexity and cost of your overall AI security posture.
A structured delivery plan for integrating hardware-based Trusted Execution Environments into your AI inference pipeline, from architecture to production deployment.
| Phase & Deliverable | Week 1-2 | Week 3-6 | Week 7-8 |
|---|---|---|---|
Architecture & Threat Modeling | |||
TEE Environment Provisioning (AWS Nitro/Azure CVM) | |||
Secure Data Ingestion Pipeline | |||
AI Model Porting & Enclave Optimization | |||
Attestation & Key Management Integration | |||
End-to-End Security Validation & Pen Testing | |||
Production Deployment & Handoff | |||
Ongoing Support & Monitoring | Optional SLA | Optional SLA | Optional SLA |
Our hardware-based TEE integration protects sensitive data during active AI processing, enabling innovation in regulated and high-risk sectors. We deliver attested enclaves, secure key management, and end-to-end pipeline security.
Execute proprietary trading models and quantitative analytics within Intel SGX or AMD SEV enclaves. Protect IP and sensitive market data from insider threats and infrastructure compromise, ensuring sub-millisecond inference for high-frequency systems.
Learn more about our approach in our guide to Financial Algorithmic Modeling in Secure Enclaves.
Deploy HIPAA-compliant AI for medical imaging, clinical decision support, and biometric verification. Sensitive patient data and biometric templates are processed in plaintext only within attested AWS Nitro Enclaves or Azure Confidential VMs.
Explore our specialized service for Confidential Computing for Biometric AI Processing.
Build air-gapped, hardware-rooted AI systems for classified data processing on secure government networks. Our TEE integration ensures model integrity and prevents data exfiltration even on potentially compromised infrastructure, meeting stringent government security standards.
Meet GDPR and EU AI Act data-in-use requirements for AI processing personal data. Our architectures enable secure multi-party computation and confidential model fine-tuning, allowing global collaboration without transferring raw data across borders.
Understand how this integrates with broader data strategy in Geopatriation and Regional Data Engineering.
Deploy lightweight TEEs on edge devices and gateways for local AI inference on sensitive sensor data (video, audio, telemetry). Achieve privacy-by-design by processing data locally without sending raw streams to the cloud, critical for smart cities and industrial IoT.
Enable secure, multi-party AI for drug discovery and clinical trial analysis. Our confidential computing systems allow multiple organizations to jointly train models on combined datasets within TEEs, protecting proprietary biochemical data and patient information.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Direct answers to the most common technical and commercial questions about integrating hardware-based Trusted Execution Environments into your AI infrastructure.
A standard integration project for a single AI workload (e.g., a confidential inference API) takes 2-4 weeks from kickoff to production. Complex, multi-model pipelines with custom attestation flows can extend to 6-8 weeks. We provide a detailed project plan in week one, breaking down environment provisioning, SDK integration, attestation setup, and security validation phases.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
