The AI Encryption Layer
Run AI on your most sensitive data without ever exposing it in plaintext.
FHEnom for AI™ keeps your datasets, model weights, prompts, and outputs encrypted throughout the AI lifecycle. Deploy across cloud, on-prem, and edge infrastructure while retaining control of the data and IP that create your advantage.
100% Encrypted. 0% Plaintext. Confidential AI.
FHEnom for AI™01
Encrypt the Model
Model weights, parameters, tensors, and architecture are encrypted using FHE. Existing pre-trained models or new foundation models can be encrypted and deployed to any infrastructure — cloud, on-prem, or edge.
02
Encrypt the Data
Prompts and queries are encrypted at the trust boundary using an ephemeral session key. Data never leaves your logical control in plaintext.
03
Compute on Ciphertext
Optimized FHE engine processes data directly on GPU cores – no decryption at any stage. Tensors and KV cache remain encrypted throughout inference. Optional TEE integration available for hardware-hardened key custody.
04
Return Encrypted Results
Inference outputs are generated in encrypted form and returned to the client. Only the session key holder decrypts. Zero exposure at any point.
of employee AI inputs now contain sensitive data – up from 11% in 2023
enterprise AI credentials found on the dark web in 2025
of IT leaders report concern about data exposure through AI tools
Every dataset you feed AI – customer data, financial records, internal documents — contributes to a model that represents your organization’s collective knowledge. That makes AI models one of the most concentrated assets in the enterprise. Yet most AI systems process that data without encryption. The model, the prompts that query it, and the outputs it generates all exist unprotected during inference — the exact moment they’re most valuable and most vulnerable.
Not all data requires the same level of protection. But when the classification demands it, only one tier delivers zero plaintext exposure.
TIER 1 – PUBLIC
Standard Cloud AI
DATA TYPES
Open-source datasets, public knowledge bases, anonymized marketing data.
ACCEPTED SECURITY
Encryption at Rest and in Transit only.
THE PLAINTEXT REALITY
Data is plaintext in CPU, GPU, Bus, and Memory.
VENDOR FIT
OpenAI, Standard AWS / Azure / GCP
TIER 2 – INTERNAL
Basic Hardware CC
DATA TYPES
Routine operational analytics, internal communications, low-risk business logic.
ACCEPTED SECURITY
Intel TDX, AMD SEV – CPU-level TEE isolation.
THE PLAINTEXT REALITY
Decrypted inside the CPU enclave. Plaintext on the bus.
VENDOR FIT
Standard TEE Wrappers
Confidential Computing
TIER 3 – CONFIDENTIAL
Advanced Hardware CC
DATA TYPES
Standard PII, generalized financial metrics, basic SOC2 compliance workloads.
ACCEPTED SECURITY
Advanced Confidential Computing with GPU-level encryption.
THE PLAINTEXT REALITY
Encrypted in VRAM, but still decrypted at the cores for computation.
VENDOR FIT
Elite CC Providers
Confidential Computing
TIER 4 – RESTRICTED
Encrypted Execution
DATA TYPES
Regulated PHI (HIPAA), cross-border finance, sovereign data, pharma R&D, model IP
ACCEPTED SECURITY
FHEnom for AI™ – Continuous Fully Homomorphic Encryption.
THE PLAINTEXT REALITY
Zero Plaintext. Computation occurs entirely on ciphertext.
VENDOR FIT
DataKrypto
Business logic, PII, and strategic questions – decrypted during every inference cycle.
Your most valuable AI asset, sitting unprotected in GPU memory
Predictions, classifications, and generated responses – produced in the clear
Datasets used fine-tuning and RAG, decrypted for processing
Cross-organizational data requiring every party to trust the enclave provider
A CISO classifies their organization’s most sensitive assets — sovereign data, pharmaceutical IP, trade secrets — as Restricted. They evaluate AI security solutions and select a Tier 3 provider: advanced Confidential Computing with hardware-level GPU encryption.
The CISO authorized Restricted data to leave the organization under Tier 3 protection. The data required Tier 4. The breach isn’t a technology failure — it’s a classification mismatch.
Tier 4 eliminates this risk entirely. With FHEnom for AI™, the data never exists in plaintext — not in memory, not at the cores, not anywhere on the infrastructure. Even if the third party is fully compromised, the attacker gets ciphertext. There is nothing to steal.
Follow a single inference request across the three architectures. See how hardware isolation fails at the software layer – and how encrypted execution solves it.
Tier 2
The standard Confidential Computing model (Intel TDX, AMD SEV). Data is decrypted at the CPU. The entire downstream pipeline is exposed.
⚠ Data leaks across the PCIe bus, VRAM, and GPU execution cores.
Tier 3
Blackwell (B200) architectures encrypt the PCIe bus and VRAM physically, but the data is still decrypted for the software layer to process it.
⚠ Hardware is secure, but a breached application or OS yields pure plaintext.
Tier 4
FHEnom for AI™ replaces hardware isolation with math. The prompt and the model weights remain ciphertext inside the memory, inside the OS, and inside the GPU cores.
✔ Zero Plaintext. A breached application yields only mathematically secure ciphertext.
Tier 2
The standard Confidential Computing model (Intel TDX, AMD SEV). Data is decrypted at the CPU. The entire downstream pipeline is exposed.
⚠ Data leaks across the PCIe bus, VRAM, and GPU execution cores.
Tier 3
Blackwell (B200) architectures encrypt the PCIe bus and VRAM physically, but the data is still decrypted for the software layer to process it.
⚠ Hardware is secure, but a breached application or OS yields pure plaintext.
Tier 4
FHEnom for AI™ replaces hardware isolation with math. The prompt and the model weights remain ciphertext inside the memory, inside the OS, and inside the GPU cores.
✔ Zero Plaintext. A breached application yields only mathematically secure ciphertext.
Tier 2
The standard Confidential Computing model (Intel TDX, AMD SEV). Data is decrypted at the CPU. The entire downstream pipeline is exposed.
⚠ Data leaks across the PCIe bus, VRAM, and GPU execution cores.
Tier 3
Blackwell (B200) architectures encrypt the PCIe bus and VRAM physically, but the data is still decrypted for the software layer to process it.
⚠ Hardware is secure, but a breached application or OS yields pure plaintext.
Tier 4
FHEnom for AI™ replaces hardware isolation with math. The prompt and the model weights remain ciphertext inside the memory, inside the OS, and inside the GPU cores.
✔ Zero Plaintext. A breached application yields only mathematically secure ciphertext.
TECH NOTE Why aren’t Tiers 2 & 3 quantum safe? TEE security relies on RSA/ECDSA for attestation and Diffie-Hellman for key exchange — all vulnerable to Shor’s algorithm. These cryptographic primitives are baked into the hardware at manufacturing and cannot be upgraded to NIST post-quantum standards without new silicon — a multi-year hardware refresh cycle. FHEnom for AI™’s encryption is quantum-resistant by mathematical design. No new hardware. No migration. No NIST retrofit required.
Data sovereignty regulations require that sensitive data never leave jurisdictional control — but on-prem GPU capacity is finite. Cloud burst is the answer, except it means trusting a third-party provider with your plaintext.
FHEnom for AI™ eliminates the trade-off. Data is encrypted before it leaves your premises and stays encrypted throughout processing on any cloud, in any jurisdiction. The cloud provider processes ciphertext – they cannot see the data, the model, or the results. Sovereignty is maintained mathematically, not by hardware attestation in a foreign datacenter.
Jurisdiction-Agnostic
Process data in any cloud, any country. Jurisdictional access laws are irrelevant when there’s no plaintext to access.
On-Prem to Cloud Burst
Extend capacity to cloud without re-architecting. FHEnom for AI™ handles encryption transparently at the boundary.
No Hardware Trust Required
Sovereignty doesn’t depend on Intel, AMD, or NVIDIA attestation. Mathematical protection works on any hardware, anywhere.
No decryption. No plaintext window. No side-channel exposure. Mathematical certainty, not hardware trust.
FHEnom for AI™ delivers bit-exact results—in FP32 deterministic mode, the encrypted model produces word-for-word identical outputs to its plaintext counterpart. Encryption adds security, not noise.
Cloud, on-prem, edge, or multi-party environments. Infrastructure becomes trusted by design—because it never sees your data.
Encryption is provable at every stage — audit trails are cryptographically verifiable
Agents only process encrypted data. What they can’t see, they can’t leak.
The only added latency is ~0.6ms for encryption and decryption of a 2K-token prompt and response (4K tokens total). That’s not a trade-off. That’s a rounding error.
Process data globally while remaining legally local. Encryption at the source allows you to use any cloud jurisdiction without violating localization mandates or sovereign data laws.
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