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Clear next-step recommendation
Enable AI inference and training directly on encrypted data, eliminating raw data exposure.
We implement fully homomorphic encryption (FHE) libraries like Microsoft SEAL and OpenFHE to allow your AI models to process data while it remains encrypted. This enables regulated industries—healthcare, finance, and defense—to leverage cloud-scale AI for sensitive workloads without compromising data sovereignty or violating regulations like HIPAA and GDPR.
Process proprietary genomic data, financial transactions, or classified documents in the cloud with cryptographic guarantees that raw data is never decrypted on third-party servers.
Move beyond air-gapped infrastructure. Our FHE integration service provides the technical bridge to use advanced cloud AI while keeping your most sensitive data truly private. Explore our broader approach to Privacy-Preserving AI Computation or learn about complementary techniques like Secure Multi-Party Computation (MPC) Engineering.
Homomorphic Encryption AI Integration enables regulated industries to leverage cloud-scale AI while maintaining absolute data confidentiality. Our implementation delivers measurable business results.
Deploy AI in regulated sectors like finance and healthcare without data sovereignty violations. Our FHE implementations are engineered for GDPR, HIPAA, and CCPA compliance, providing auditable privacy guarantees.
Process sensitive data on third-party cloud infrastructure (AWS, Azure, GCP) with zero exposure. We integrate libraries like Microsoft SEAL and OpenFHE to perform inference directly on encrypted data, eliminating the cloud provider as a trust boundary.
Safeguard your trained AI models as valuable intellectual property. Homomorphic encryption allows you to deploy models for client use without exposing the underlying weights or architecture, enabling new SaaS and licensing revenue models.
Minimize cyber insurance premiums and liability exposure by architecturally eliminating the risk of sensitive data exposure during AI processing. This demonstrable risk reduction is a key factor in underwriting and compliance audits.
Our phased approach to Homomorphic Encryption AI Integration allows enterprises to start with a focused pilot and scale to full production with guaranteed data privacy. Each tier includes our expertise in Microsoft SEAL and OpenFHE libraries.
| Capability | Pilot & Validation | Production Integration | Enterprise Scale |
|---|---|---|---|
FHE Library Integration (SEAL/OpenFHE) | |||
Encrypted Inference API Development | |||
Performance Optimization (Latency < 2s) | Basic | Advanced | Custom Hardware |
Compliance Documentation (GDPR, CCPA) | Framework | Full Audit Trail | Automated Reporting |
Uptime SLA | 99.5% | 99.9% | 99.99% |
Dedicated Security & Crypto Engineer | |||
Integration with Existing Data Lakes / Warehouses | 1 Source | Up to 3 Sources | Unlimited |
Support & Maintenance | Business Hours | 24/7 Priority | Dedicated Engineering Pod |
Implementation Timeline | 4-6 Weeks | 8-12 Weeks | Custom Roadmap |
Starting Investment | $50K - $80K | $150K - $300K | Custom Quote |
Homomorphic Encryption enables AI on encrypted data, unlocking new capabilities in highly regulated sectors where data sensitivity is paramount. We implement FHE to solve specific, high-impact business problems.
Process facial recognition, fingerprint, or voice authentication directly on encrypted biometric templates. This allows for secure, privacy-preserving identity verification in access control, mobile banking, and government systems, preventing template database breaches.
Key Deliverables: FHE-optimized neural networks for biometric matching, hardware-accelerated inference, and integration with TEEs for end-to-end security.
Facilitate collaborative cancer research by allowing AI models to analyze encrypted genomic datasets from multiple research institutions. Researchers can identify biomarkers and treatment responses without violating patient privacy or intellectual property agreements.
Key Deliverables: Custom FHE schemes for high-dimensional genomic data, federated learning orchestration with FHE, and secure multi-party computation gateways.
Run natural language processing on encrypted legal documents, contracts, and communications for e-discovery, compliance monitoring, and case prediction. Law firms and corporate legal departments can leverage AI without risking attorney-client privilege or exposing sensitive case strategy.
Key Deliverables: Encrypted NLP pipelines for document classification and summarization, integration with legal tech platforms, and audit trails for regulatory compliance.
Apply AI analytics to encrypted intelligence signals, intercepted communications, and satellite imagery. Agencies can utilize commercial cloud AI capabilities for national security tasks while maintaining full data sovereignty and meeting stringent classification requirements (e.g., IL5/IL6).
Key Deliverables: Air-gapped FHE deployment patterns, integration with sovereign AI infrastructure, and performance-optimized libraries for large-scale encrypted data.
A systematic, four-phase approach to deploy fully homomorphic encryption for secure, compliant AI inference in regulated industries.
We move beyond academic FHE libraries to deliver production-ready, low-latency systems. Our methodology ensures encrypted AI inference meets enterprise SLAs for performance and reliability.
Microsoft SEAL or OpenFHE with secure enclaves for optimal performance.This rigorous process de-risks FHE adoption, allowing financial, healthcare, and defense clients to leverage cloud AI without exposing raw, sensitive data. For related privacy techniques, explore our work on Differential Privacy Algorithm Implementation and Secure Multi-Party Computation (MPC) Engineering.
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, technical answers about implementing fully homomorphic encryption (FHE) for secure AI inference and training on encrypted data.
Standard deployments for integrating FHE libraries like Microsoft SEAL or OpenFHE with an existing AI pipeline typically take 4-8 weeks. This includes cryptographic parameter selection, model adaptation for encrypted operations, and performance benchmarking. Complex, multi-model systems or custom cryptographic circuit development can extend to 12+ weeks. We provide a detailed project plan during the initial discovery phase.
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.
