Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy AI models within hardware-based Trusted Execution Environments (TEEs) to protect intellectual property and sensitive data during inference.
Your proprietary model weights and customer data are vulnerable in standard cloud memory. We architect secure enclaves using Intel SGX or AMD SEV, creating isolated, attested environments where data is processed in encrypted memory, inaccessible to the host OS, hypervisor, or cloud provider.
This transforms AI from a data liability into a secure asset, enabling high-value applications in regulated industries without compromise.
Move beyond basic encryption at rest. Our service delivers end-to-end confidential AI pipelines, from secure data ingestion within the enclave to attested model serving APIs. This is critical for deploying AI in financial services, healthcare, and defense, or for any company protecting its core algorithmic advantage. Explore our broader approach to Confidential Computing for AI Workloads or learn about securing multi-organization collaboration with Secure Multi-Party AI Computation Services.
Deploying AI within hardware-based Trusted Execution Environments (TEEs) delivers measurable security and competitive advantages. Our enclave development services translate technical controls into direct business outcomes.
Process confidential data with zero exposure. Our enclave architecture ensures raw customer data, financial transactions, or biometric inputs are decrypted and processed only within the secure hardware boundary, enabling compliance with GDPR, HIPAA, and the EU AI Act for data-in-use.
Collaborate without compromising data. We engineer systems for joint training and inference across organizations using TEEs for secure aggregation, allowing you to gain insights from combined datasets while each party's raw data remains cryptographically isolated. Learn about our related service for secure multi-party AI computation services.
Meet stringent data sovereignty mandates. Our TEE implementations provide verifiable attestation reports, creating an audit trail that proves sensitive AI processing occurred within a certified secure environment, directly supporting compliance with frameworks like NIST AI RMF and ISO/IEC 42001. Explore our broader enterprise AI governance and compliance frameworks.
Extend privacy-by-design to IoT and remote sites. We implement lightweight TEEs on edge devices, enabling local AI inference on video, audio, or sensor data without sending raw streams to the cloud, drastically reducing latency and bandwidth while maintaining security.
Build a foundation for next-generation AI. Confidential computing is becoming a baseline requirement for sensitive workloads. Our enclave development establishes a secure, portable architecture ready for cross-cloud confidential AI workload migration and hybrid deployments, protecting your long-term AI investment.
A transparent breakdown of our phased approach to developing and deploying your confidential AI inference enclave, detailing key milestones, deliverables, and typical timeframes.
| Phase & Key Deliverables | Timeline | Outcome |
|---|---|---|
Security Architecture & TEE Selection | 1-2 weeks | Architecture document detailing hardware platform (Intel SGX, AMD SEV, etc.), threat model, and attestation strategy. |
Enclave Development & Model Integration | 3-5 weeks | Production-ready enclave code with integrated AI model, secure data channels, and internal attestation. |
Attestation & Key Management Integration | 1-2 weeks | Integration with your PKI or cloud KMS (e.g., Azure Managed HSM, AWS KMS) for remote attestation and secure key release. |
CI/CD Pipeline & Security Testing | 2-3 weeks | Automated build, test, and deployment pipeline with integrated security scanning and penetration testing report. |
Staging Deployment & Performance Validation | 1 week | Validated performance benchmarks (latency, throughput) and final security audit in a staging environment. |
Production Rollout & Monitoring | 1 week | Deployed enclave in production with integrated monitoring, logging (secured), and alerting for attestation status. |
Ongoing Support & Maintenance | Ongoing | Optional SLA covering security patches, TEE SDK updates, and performance optimization. |
Our confidential AI inference enclaves are engineered to protect the most sensitive data and intellectual property across regulated and high-value industries. Each solution is built on hardware-based TEEs with verifiable attestation.
Execute proprietary trading models and quantitative analytics within Intel SGX enclaves. Protect algorithmic IP and sensitive market data from insider threats and infrastructure compromise, ensuring deterministic, low-latency inference for high-frequency applications.
Learn more about our Financial Algorithmic Modeling in Secure Enclaves service.
Deploy HIPAA-compliant AI for medical imaging diagnostics and biometric verification. Sensitive patient data and biometric templates are processed in encrypted memory enclaves, never exposed to the host OS, cloud provider, or other tenants.
Explore our specialized Confidential Computing for Biometric AI Processing offering.
Build air-gapped, hardware-rooted AI systems for classified data processing on secure government networks. Our enclaves ensure model integrity and prevent data exfiltration even on potentially compromised infrastructure, meeting stringent sovereign requirements.
See how we implement TEE-Based AI for Defense and Intelligence.
Automate contract analysis and litigation prediction on sensitive case files within attested AMD SEV-SNP environments. Enforce data sovereignty for cross-border discovery and ensure privileged client communications remain confidential during AI processing.
Integrate with our broader Legal and Compliance Workflow Automation capabilities.
Enable multiple organizations to jointly train models on combined datasets without exposing their private data. Using TEEs for secure aggregation, this is ideal for consortium research, fraud detection networks, and cross-company analytics.
Engineer collaborative systems with our Secure Multi-Party AI Computation Services.
Deploy lightweight TEEs on edge devices and gateways for local inference on sensitive sensor data (video, audio, telemetry). Perform privacy-by-design analytics without sending raw data to the cloud, critical for smart cities and industrial IoT.
Extend security to the edge with Confidential AI for Edge and IoT Devices.
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.
Get specific answers on timelines, costs, security, and process for deploying AI models within hardware-secured enclaves like Intel SGX and AMD SEV.
Standard deployments take 2-4 weeks from kickoff to production-ready enclave. This includes environment provisioning, model integration, attestation setup, and security hardening. Complex integrations with existing on-premises infrastructure or custom hardware (e.g., FPGA) can extend to 6-8 weeks. We provide a detailed project plan with weekly milestones during scoping.
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.
