Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Execute proprietary trading models and risk analytics in hardware-secured enclaves to protect IP and sensitive data.
Protect your core intellectual property from insider threats and infrastructure compromise. We deploy your quantitative models within attested hardware enclaves (Intel SGX, AMD SEV) where code and data are cryptographically shielded—even from the host OS and cloud provider.
FPGA/ASIC systems for sub-microsecond inference.GDPR, MiFID II, and internal audit requirements for data-in-use protection.Move beyond perimeter security. Isolate your most sensitive calculations at the hardware level to prevent model theft and data exfiltration.
Deploy a secure algorithmic trading MVP within 2 weeks. Our expertise in confidential AI data pipeline architecture and hardware-based TEE integration ensures your competitive edge remains protected. For broader financial AI strategies, explore our financial services algorithmic AI and risk modeling services.
Deploying proprietary financial models within hardware-secured enclaves delivers measurable competitive advantages and risk reduction, directly impacting your bottom line.
Architect your AI systems with hardware-based data protection built-in, providing auditable evidence for compliance with the EU AI Act, SEC rules, and internal governance. Our enclave deployment includes attestation reports and integrity verification.
Eliminate the risk of privileged cloud administrators, compromised firmware, or malicious dependencies exfiltrating your models or data. The hardware root of trust ensures only authorized, verified code executes within the protected environment.
Our optimized integration of TEEs with high-performance computing stacks ensures sub-millisecond inference latency for time-sensitive trading signals. We architect for performance isolation, preventing "noisy neighbor" impacts in multi-tenant clouds.
Our proven, milestone-driven approach to deploying your proprietary financial models within hardware-secured enclaves, ensuring intellectual property protection and compliance from day one.
| Phase | Key Activities | Deliverables | Typical Duration |
|---|---|---|---|
| Threat modeling, algorithm compatibility analysis, TEE platform selection (Intel SGX, AMD SEV, AWS Nitro) | Architecture recommendation, risk mitigation report, proof-of-concept enclave | 1-2 weeks |
| Porting of core algorithms to enclave SDK, secure I/O channel implementation, attestation service setup | Functional enclave binary, integration test suite, attestation validation pipeline | 3-4 weeks |
| Design of confidential data ingestion, implementation of in-enclave decryption, secure key management integration | Encrypted model weights, secure data loader, key management system configuration | 2-3 weeks |
| Latency profiling, memory footprint optimization, side-channel vulnerability assessment, load testing | Performance benchmark report, security audit findings, optimized production binary | 2-3 weeks |
| Kubernetes operator deployment for enclave lifecycle, monitoring & logging integration, SLA definition | Production-ready deployment manifests, operational runbook, 99.9% uptime SLA | 1-2 weeks |
| Optional managed service for updates, security patching, and performance tuning | Dedicated engineer support, quarterly security reviews, scaling guidance | Ongoing |
Protect your most valuable intellectual property—proprietary algorithms and sensitive market data—by executing core quantitative workflows within hardware-isolated, attested enclaves. We architect solutions that secure data-in-use against insider threats and infrastructure compromise.
Deploy and run high-frequency and algorithmic trading strategies within Intel SGX or AMD SEV enclaves. Model weights, logic, and live market data are cryptographically protected in memory, preventing IP theft and front-running even if the host OS is compromised.
Learn more about our approach to Confidential AI Inference Enclave Development.
Execute complex Monte Carlo simulations, VaR calculations, and stress-testing models on sensitive portfolio data within secure enclaves. Ensure raw position data and the resulting risk metrics are never exposed to cloud providers or other tenants, meeting internal governance and regulatory data-in-use requirements.
Collaborate on joint quantitative research with external hedge funds or data vendors without sharing underlying proprietary datasets. Our systems use secure enclaves to enable federated learning and encrypted computation, allowing models to learn from combined data while each party's inputs remain confidential.
Explore our capabilities for Secure Multi-Party AI Computation Services.
Serve production risk or pricing models via APIs where the model remains encrypted at rest and in memory. Inference requests and results are processed within the enclave, protecting the algorithm from reverse-engineering and client data from exposure, ideal for B2B fintech platforms.
Deploy machine learning models to detect market manipulation or internal fraud by analyzing order flow and communications. Sensitive trading communications and employee data are processed within enclaves, enabling investigation without creating new data privacy liabilities.
Automate the generation of MiFID II, Dodd-Frank, or SEC reports by running compliance logic directly on raw trade and communication data within a TEE. This provides auditors with verifiable attestation reports proving the integrity of the computation without exposing the underlying sensitive data.
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 clear answers on how we protect your proprietary trading algorithms and sensitive market data with hardware-based confidential computing.
We deploy your models within hardware-based Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV. These create isolated memory enclaves where your algorithms and live market data are processed. The host operating system, cloud provider, and any other processes cannot access the enclave's memory, protecting your intellectual property from insider threats and infrastructure compromise. This is a core part of our Confidential Computing for AI Workloads service pillar.
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.
