Your AI model never sees sensitive data. Your customers never worry.
Models memorize fragments of their training data. Adversarial prompts can extract PII, medical records, and proprietary information directly from model weights. Your training pipeline is a liability.
Model outputs can contain PII from training data, even when the input is benign. Membership inference attacks reveal whether specific records were in the training set. Outputs are evidence.
Prompt injection extracts sensitive context from system prompts, RAG documents, and conversation history. Your carefully guarded context window is one adversarial input away from full exposure.
KV caches, CDN edges, and observability pipelines store intermediate results in plaintext. Your logging infrastructure captures every token your model processes, creating a complete record of sensitive data.
Every LLM API call transmits sensitive data across network boundaries. Request payloads, response bodies, and error messages all contain user data in plaintext. Every API call is a potential data leak.
Attackers use model APIs to reconstruct proprietary models through systematic querying. Your model weights encode your competitive advantage and your training data. Both are extractable.
Request and response payloads carry user data in the clear across every network hop.
Context windows and conversation history hold sensitive data in plaintext RAM throughout inference.
KV stores, CDN edges, and embedding caches persist sensitive intermediate results in the clear.
Observability pipelines capture everything. Every token, every prompt, every response is logged in plaintext.
Training data memorization means sensitive records are baked into the model itself. Extraction is a known attack vector.
User data is encrypted client-side with FHE before it reaches your infrastructure.
Your AI model receives ciphertext and performs the full inference pipeline on encrypted data.
The model returns encrypted results. Only the data owner can decrypt the output.
A zero-knowledge proof attests the computation was performed correctly on encrypted data.
Not in memory. Not in cache. Not in logs. The plaintext never exists on your servers. This is not tokenization or masking — it is computation on ciphertext.
FHE-encrypted inference monitoring. Every AI decision logged with ZK-STARK proofs. Policy enforcement for EU AI Act, HIPAA, GDPR, SOX, and CCPA.
Explore AI Compliance →FHE-powered search over encrypted databases. Query encrypted embeddings, run boolean operations on ciphertext, build encrypted indexes. Zero plaintext exposure.
Explore Encrypted Search →Deepfake and synthetic identity detection. Identify AI-generated images, audio, and video. Protect your platform from synthetic identity fraud.
Explore AI Detection → FreeProof-of-work bot prevention for AI APIs. No CAPTCHAs, no tracking, no third-party data. One script tag. Free for 2,500 challenges/month.
Explore BotShield →High-risk AI must prove that training and inference data is handled with appropriate governance controls. H33's FHE wrapper provides cryptographic proof that sensitive data was never exposed during processing — the strongest data governance control that exists.
H33: FHE data separationOperators must maintain logs sufficient to allow authorities to assess compliance. H33's Decision Logger creates ZK-STARK-verified records of every AI decision, with Dilithium-signed timestamps and Merkle tree compression. Immutable, cryptographically verifiable.
H33: ZK-STARK decision logsThe Act requires effective human oversight mechanisms. H33's Policy Engine enforces governance rules as executable code with SHA3-fingerprinted audit trails. Human reviewers get cryptographic proof of what the AI processed and what it decided, without accessing raw data.
H33: Policy Engine + audit trailsHigh-risk AI providers must produce conformity assessment documentation. H33's Audit Report Generator produces assessment bundles with proof packages — every claim backed by a cryptographic proof that auditors can independently verify at h33.ai/verify.
H33: Verifiable proof bundlesWhen your customers ask if their data trains your model, the answer with H33 is mathematically provable: the model never saw their data in plaintext.
FHE makes this a cryptographic guarantee, not a policy promise. A Dilithium-signed attestation proves the data was processed encrypted end-to-end. Send one link. Replace the security questionnaire. Ship the proof instead of the promise.
The engineering cost of rolling your own PQC vs. using a hardened API. Real numbers from production deployments.
Read article →A comprehensive overview of companies building with fully homomorphic encryption, from startups to enterprise platforms.
Read article →The landscape of zero-knowledge proof companies and how ZK technology is being applied across industries.
Read article →How fully homomorphic encryption enables ML inference on encrypted data. Architecture, performance, and production considerations.
Read article →A technical introduction to FHE: how it works, why it matters, and where it is headed. From lattice math to production APIs.
Read article →Where FHE is going next: encrypted AI inference, private auctions, confidential computing, and sovereign data processing.
Read article →Full product page for H33 AI Compliance: encrypted inference, ZK-proof logging, policy engine, and conformity assessment.
View product →FHE-powered search over encrypted databases. Keyword, boolean, and similarity queries on ciphertext.
View product →H33's four FHE engines: BFV, CKKS, BFV-32, and FHE-IQ. Architecture, parameters, and benchmarks.
View overview →Your AI model processes data it cannot see. Your customers get cryptographic proof. One API call.
1,000 free units/month · No credit card required · Zero plaintext exposure