Service

Confidential AI Inference Enclave Development

Deploy your AI models within hardware-based Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV. We ensure model weights and sensitive inference data are cryptographically protected from all other processes, including the host OS and cloud provider.

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Data scientist building training data pipeline on laptop, data preprocessing visible, technical workspace.

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.

Protect Model IP: Keep proprietary algorithms and fine-tuned weights encrypted during execution, preventing theft or reverse-engineering.
Secure Sensitive Data: Process PII, financial records, or biometrics within the enclave; raw data never leaves the protected memory space.
Meet Compliance Mandates: Address the data-in-use requirements of GDPR, HIPAA, and the EU AI Act with hardware-rooted security.
Maintain Performance: Achieve near-native inference latency with <10% overhead through optimized TEE library integration.

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.

DELIVERING TANGIBLE VALUE

Business Outcomes of Confidential AI Inference

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.

Protect Proprietary Model IP

Safeguard your core AI assets. We deploy models within Intel SGX or AMD SEV enclaves, ensuring model weights and algorithms are encrypted in memory and during computation, preventing theft or reverse-engineering by cloud providers or malicious insiders.

EXPLORE

Secure Sensitive Inference Data

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.

Enable Secure Multi-Party AI

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.

Achieve Regulatory Compliance

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.

Deploy AI at the Confidential Edge

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.

Future-Proof AI Infrastructure

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.

From Assessment to Production

Typical Project Timeline & Deliverables

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.

VERTICAL SOLUTIONS

Industries and Applications We Secure

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.

Financial Services & Algorithmic Trading

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.

< 100μs

Added Enclave Latency

FIPS 140-3

Cryptographic Module Validation

Healthcare & Biometric Processing

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.

HIPAA / GDPR

Compliance Built-In

Zero-Trust

Data-in-Use Protection

Defense & National Intelligence

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.

IL5/IL6

Impact Level Ready

Hardware-Rooted

Chain of Trust

Legal & Compliance Workflows

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.

Attorney-Client

Privilege Maintained

EU AI Act

High-Risk Compliance

Secure Multi-Party AI Computation

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.

Cryptographic

Data Provenance

TEE-Attested

Compute Integrity

Edge & IoT Confidential AI

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.

On-Device

Data Processing

ARM TrustZone

& Intel TDX Support

Enabling Efficiency, Speed & Accuracy

Intelligent Analysis, Decision & Execution

We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.

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Search across company data

Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.

Useful when people spend too long searching or get different answers from different systems.

Enterprise searchRAGPermissions

Automate internal workflows

Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.

Useful when repetitive work moves across multiple tools and teams.

AI agentsWorkflow automationGovernance

Add AI to products and internal tools

Build assistants, guided actions, or decision support into the software your team or customers already use.

Useful when AI needs to be part of the product, not a separate tool.

AI integrationDecision supportModel routing

Technical and Commercial Questions

Confidential AI Enclave Development FAQs

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

Prasad Kumkar

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.

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How We Work

Custom AI workflows for your Business

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.