FHEnom for AI™ secures the four workloads where plaintext exposure creates the most risk — inference, training, sovereign compute, and model IP protection. No decryption. No trade-offs.
Run AI models on encrypted data. Prompts, RAG, weights, and outputs never exist in plaintext.
Fine-tune models on regulated data without ever decrypting it on the compute infrastructure.
Burst to cloud without compromising data sovereignty. Encrypted data never leaves your control.
Your proprietary model is your most valuable asset. Encrypt it so it runs anywhere — without ever exposing the weights.
Every AI inference request exposes prompts, RAG context, model weights, and outputs in plaintext — inside TEEs, on the bus, in VRAM. FHEnom for AI™ eliminates this exposure entirely.
In standard AI inference — even with Confidential Computing — prompts and RAG context are decrypted for tokenization, model weights sit in plaintext in GPU memory, and outputs are generated in cleartext before re-encryption. Every stage is a plaintext exposure window. Side-channel attacks, memory dumps, and insider threats can extract your most sensitive data at the moment it matters most.
FHEnom for AI™ encrypts prompts and RAG context at the client, processes them through an Encryption Tokenizer, runs inference on ciphertext using the encrypted model, converts outputs back through a symmetric Decryption Tokenizer, and returns encrypted results. The infrastructure — CPU, GPU, bus, OS — never sees plaintext at any stage. Inference performance is identical to plaintext, with ~0.6ms total encryption overhead for 4K tokens.
Standard tokenizers are the last plaintext gap. FHEnom for AI™'s Encryption Tokenizer processes prompts in ciphertext space; the Decryption Tokenizer converts encrypted outputs back — no readable tokens ever generated on infrastructure.
Model weights exist only as ciphertext in GPU memory. Even NVIDIA's B200 encrypted memory still decrypts for compute — FHEnom for AI™ doesn't.
Every inference session uses a unique cryptographic key, created and destroyed per-session. No correlation between sessions is possible.
Fine-tune or train models on regulated, proprietary, or multi-party data — without the training infrastructure ever seeing the data or the resulting model.
AI training requires exposing your most sensitive datasets — patient records, financial transactions, proprietary research — on compute infrastructure you don't fully control. The training operator can see the data, the gradients, and the resulting model. Model inversion attacks can reconstruct training samples. Gradient leakage exposes individual records. The model itself becomes an extraction vector for the data it was trained on.
The data owner encrypts both the training data and the foundational model with their own keys, then passes the encrypted model and encrypted training data — without the key — to the third-party training operator. The operator trains on ciphertext: they can measure convergence, but cannot evaluate accuracy — that requires sending the encrypted results back to the data owner for decryption and validation. The training operator never sees the data, the base model, the gradients, or the fine-tuned output. The data owner retains full control of the data, the model, and the resulting IP.
The client encrypts both the foundational model and the training data, then hands off to the SI or cloud provider. The operator trains blind — measuring convergence but sending results back to the client for accuracy validation.
The trained model is encrypted from creation. It can be deployed, served, and operated on shared infrastructure without exposing architecture, weights, or parameters.
Training gradients remain encrypted throughout backpropagation. Gradient leakage attacks — a proven method for reconstructing training data — are eliminated.
Organizations with on-premises infrastructure need cloud burst capacity — but can't expose sovereign data to foreign jurisdictions or third-party operators.
Data sovereignty regulations (GDPR, PIPL, local equivalents) require that certain data never leave jurisdictional control. But on-prem GPU capacity is finite and expensive. Cloud burst is the obvious answer — except it requires trusting a third-party cloud provider with your plaintext data. TEE-based solutions still decrypt inside the cloud provider's hardware, which may be in a foreign jurisdiction or under a different legal framework.
Data is encrypted before it leaves your premises. It stays encrypted during transit, during processing, and during storage on cloud infrastructure. The cloud provider processes ciphertext — they cannot see the data, the model, or the results. Sovereignty is maintained mathematically, not by trusting hardware attestation in a foreign datacenter. Burst to any cloud, in any jurisdiction, without compliance risk.
Data can be processed in any cloud, in any country. The cloud operator never sees plaintext — so jurisdictional access laws are irrelevant to your data's confidentiality.
Seamlessly extend on-prem capacity to cloud without re-architecting. The FHEnom for AI™ gateway handles encryption/decryption transparently at the boundary.
Sovereignty doesn't depend on trusting Intel, AMD, or NVIDIA attestation. FHEnom for AI™'s protection is mathematical — it works on any hardware, anywhere.
Your proprietary AI model is your most valuable asset — and it's exposed the moment you deploy it. FHEnom for AI™ encrypts the model so it runs on any infrastructure without the infrastructure ever seeing the weights.
Your AI model encodes years of R&D, proprietary training data, and competitive advantage. The moment you deploy it — on cloud, on a partner's infrastructure, or even on shared on-prem — the weights sit in plaintext in GPU memory. A memory dump, a side-channel attack, or a compromised admin can extract the complete model — architecture, weights, and parameters. TEE-based solutions still decrypt the model inside the enclave, leaving it exposed to microarchitectural exploits and insider threats. A single model extraction event can collapse your competitive advantage overnight.
The model owner encrypts the model — architecture, weights, and parameters — before deployment. The encrypted model runs on any GPU infrastructure, cloud or on-prem, without the infrastructure ever seeing the model in plaintext. The cryptographic shield protects the model from the infrastructure, the OS, and the application layer. Share encrypted models with partners, CROs, cloud providers, or licensees — they can run it but never inspect, copy, or reverse-engineer it.
Deploy proprietary models to the most cost-effective infrastructure — any GPU, any cloud, any region — without compromising the confidentiality of your IP.
Share encrypted models with partners, research collaborators, or cloud compute providers. They can run the model but never inspect, copy, or reverse-engineer it.
License AI models to third parties without handing over the weights. The licensee runs the encrypted model — they get the outputs, you keep the IP.
Every industry that uses AI on sensitive data has the same fundamental problem — the AI infrastructure sees your data in plaintext. The regulations differ. The risk doesn't.
PHI, clinical trial data, and drug discovery IP require HIPAA-grade protection throughout the AI pipeline — not just at rest and in transit.
Trading algorithms, risk models, and customer PII are high-value targets. Regulatory scrutiny from SOX, GLBA, and DORA demands provable data protection.
Government agencies and regulated organizations need AI capabilities on classified or sovereignty-controlled data — without exposing it to cloud providers or foreign jurisdictions.
Service providers need to offer enterprise-grade AI security on shared infrastructure — a differentiated capability that TEE-only solutions can't deliver.
AI models encode billions in R&D — molecular structures, binding affinities, and competitive intelligence. Loss of the model means loss of the pipeline.
Inference speed identical to plaintext — independently verified. Only overhead: ~0.6ms encrypt/decrypt for 4K tokens.
FP32 deterministic mode produces word-for-word identical outputs. Encryption adds security — not noise.
Security reduces to CVP — a well-studied lattice problem inherently resistant to quantum attack. No post-quantum migration required.
No dependency on Intel TDX, AMD SEV, or NVIDIA CC. Runs on any GPU, any cloud, any infrastructure.
Gateway VM replaces your AI endpoint. Zero code changes. Zero retraining. Admin CLI configures. Users point to FHEnom for AI™.
FIPS 140-2 validated (140-3 pending). ISO 27001:2022 certified. Provable encryption — not attestation-dependent.
Tell us your workload. We'll show you encrypted inference running live — on your architecture, with your constraints.
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