H33 wraps your AI in Fully Homomorphic Encryption — it computes on encrypted data, returns the answer, and never once sees the patient record, the financial document, or the privileged communication inside.
Not "our logs say it was private." Cryptographically blind. Mathematically provable. Auditor-verifiable without system access.
Your existing AI call, wrapped. FHE encrypts sensitive fields before they touch the model.
# Before: Unprotected AI call response = openai.chat.completions.create(model="gpt-4", messages=messages) # After: H33-wrapped — AI is now blind to sensitive data from h33 import comply response = comply( model="gpt-4", messages=messages, frameworks=["hipaa", "eu_ai_act"], fhe_mode=True # encrypt sensitive fields before they reach the model ) # response.answer — the AI's output (same quality as before) # response.proof — ZK-STARK proof the policy was followed # response.attestation — Dilithium-signed proof data was encrypted # response.cert_url — h33.ai/verify/yourcompany
Governance proof, data separation, and long-term audit validity are distinct requirements. Most platforms address one. Regulators are starting to require all three.
AI processes patient records, legal documents, financial data. Regulators don't just want to know what the AI decided — they want proof of what data it accessed. Logs aren't proof.
Proving your AI followed a policy is necessary but not sufficient. Healthcare, legal, and finance buyers need proof the AI never exposed sensitive data. Those are different requirements.
A PDF audit report is stale the day it's generated. Regulations change monthly. Your compliance posture needs to be live, verifiable, and mathematically provable — not a document someone signed last quarter.
Infrastructure security and governance proof are necessary. Data separation and quantum-resistant audit trails are what regulators are starting to require next. H33 is the only platform that delivers all four.
Most AI compliance tools add a log entry after the fact. H33 changes the physics of how data flows through your AI pipeline.
Drop-in integration. No model changes. No inference pipeline rewrites. Your AI keeps working the same way — except now every decision has mathematical proof.
Nine stages execute in sequence. Total added latency: under 50ms. Every stage produces independently verifiable output.
Each module works standalone or together. Start with the Policy Engine and Decision Logger. Add FHE Inference Wrapper when data separation becomes a requirement.
Visual editor + code DSL for defining AI governance policies. Every version is SHA3-fingerprinted and immutable. Version policies like software — diff, rollback, branch.
Every AI inference gets a ZK proof binding the decision to the policy that governed it. Merkle tree compression delivers 5000x storage reduction. Sub-50ms writes.
The AI computes on encrypted data. Sensitive fields are FHE-encrypted before they reach the model. The model never sees plaintext. The proof is cryptographic, not a log entry.
Real-time compliance score from 0–100 across every active framework. Gap detection surfaces missing controls before auditors do. Board-ready executive view.
One-click reports with portable proof bundles. Auditors can independently verify every claim. PDF + machine-readable JSON. Evidence is mathematical, not testimonial.
8 frameworks mapped to specific technical controls. Monthly updates by regulatory counsel. Framework changes trigger gap analysis automatically.
3 lines of code. Python, Node.js, Rust. OpenAI-compatible proxy mode — point your existing OpenAI calls at H33 and compliance wraps transparently.
h33.ai/verify/yourcompany. Public-facing compliance certificate. Live status, framework coverage, last audit date. One link replaces security questionnaires. The growth engine.
Every compliance tool says "we prove compliance." The question is what the proof actually is. A log entry written by the system being audited, or independent cryptographic verification?
Every framework maps to specific H33 modules and technical controls. Compliance is not a checkbox — it's a continuously verified cryptographic state.
High-risk AI system requirements: transparency, human oversight, data governance. H33 provides cryptographic evidence for every obligation.
New York's AI regulation bans autonomous AI decisions in healthcare, law, education, and 11 other professions. H33 proves human oversight was in the loop.
Right not to be subject to automated decision-making. H33 logs every decision with the policy that governed it and provides subject access on demand.
Protected health information processed by AI must be safeguarded. FHE Inference Wrapper ensures the AI never sees PHI in plaintext. Cryptographic proof, not just BAA language.
AI-driven financial controls require internal control attestation. H33 produces Dilithium-signed evidence of every AI decision in the financial reporting chain.
Automated decision-making profiling rights. Consumer opt-out enforcement. H33 blocks non-compliant inference and produces deletion proofs for consumer data.
Electronic records and signatures for pharmaceutical AI. H33's Dilithium signatures and immutable audit trail satisfy Part 11 requirements natively.
Financial Conduct Authority AI governance for UK financial services. Consumer Duty obligations met with cryptographic decision provenance and fairness proofs.
Every tier includes the Policy Engine and Decision Logger. The FHE Inference Wrapper — the module no competitor has — ships with Business and above.
The same FHE infrastructure adapts to the specific regulatory and data sensitivity requirements of each vertical.
H33 Makes Your AI Blind to Patient Records
Your model processes encrypted PHI, returns the clinical insight, and never once decrypts the record. HIPAA-compliant by math, not by policy. Breach risk assessment: FHE ciphertext = no PHI exposure.
H33 Makes Your AI Blind to Privileged Documents
AI reviews contracts, NDAs, and litigation files on fully encrypted data. Attorney-client privilege stays intact because the model is cryptographically incapable of seeing the plaintext.
H33 Makes Your AI Blind to Client Financials
Risk models, fraud detection, and trading algorithms run on encrypted portfolios. SOX 404 attestation backed by Dilithium-signed ZK proofs, not a quarterly PDF.
H33 Makes Your AI Blind to Employee Data
Resume screening, performance analysis, and compensation modeling on encrypted records. The AI makes decisions without seeing names, demographics, or compensation history.
Fully homomorphic encryption (FHE) allows computation on encrypted data without decrypting it. When applied to AI inference, the model processes encrypted inputs and produces encrypted outputs. The plaintext data is never exposed to the model, the infrastructure, or any intermediary. H33 uses BFV lattice-based FHE with post-quantum security to wrap AI models so they are cryptographically blind to the sensitive data they process.
H33 encrypts Protected Health Information (PHI) before it reaches the AI model using fully homomorphic encryption. The model processes encrypted patient records, returns encrypted results, and never accesses plaintext PHI. This satisfies the HIPAA Security Rule's technical safeguard requirements and means that a breach of the AI processing infrastructure exposes no PHI — the ciphertext is indistinguishable from random noise without the healthcare organization's private key. Every inference is logged with a ZK proof and Dilithium signature for the HIPAA accounting of disclosures requirement.
The EU AI Act requires conformity assessments, risk classification, human oversight, and auditable decision records for high-risk AI systems. H33's Policy Engine enforces governance rules as executable code. The Decision Logger creates ZK-proof-verified records of every AI decision. The FHE Inference Wrapper provides the data separation that demonstrates privacy-by-design. The Audit Report Generator produces conformity assessment documents with portable proof bundles. Penalties under the EU AI Act can reach 7% of global revenue — H33 provides mathematical evidence of compliance, not a policy document.
AI governance proof demonstrates that a specific policy governed a specific AI decision at a specific moment. This answers "did the AI follow the rules?" AI data separation proves that the AI never had access to the underlying sensitive data in plaintext form. This answers "did the AI touch the data?" Both are required for full compliance in regulated industries. Governance proof alone does not protect against data exposure claims. H33 is the only platform that provides both — governance proof via the Policy Engine and Decision Logger, and data separation via the FHE Inference Wrapper.
Yes. H33's FHE Inference Wrapper is a drop-in SDK that wraps any AI endpoint — OpenAI, Anthropic, HuggingFace, or custom models. For API-based models, H33 encrypts sensitive fields in the input before they reach the model provider, ensuring that PHI, PII, financial data, or privileged information never leaves your control in plaintext. For self-hosted models, H33 can run full FHE inference where the model computes directly on encrypted data. In both cases, a Dilithium-signed attestation is generated proving the data separation.
Zero-knowledge proofs (ZK proofs) allow one party to prove a statement is true without revealing the underlying data. In AI compliance, ZK proofs enable H33 to prove that a specific policy governed a specific AI decision at a specific time — without exposing the input data, output data, or internal model state. An auditor can independently verify compliance using only the proof, without accessing your systems or data. H33 uses ZK-STARK proofs compressed into Merkle trees for 5000x storage efficiency.
Post-quantum cryptography uses algorithms that are secure against both classical and quantum computers. H33 uses NIST-standardized CRYSTALS-Dilithium for all digital signatures and CRYSTALS-Kyber for key encapsulation. Your AI audit trail from 2026 needs to hold up in court in 2055. If that audit trail is signed with RSA or ECC, a future quantum computer could forge the signatures and invalidate your entire compliance record. Dilithium signatures remain secure in a post-quantum world. H33 makes this the default — no extra configuration required.
Vanta and Drata own infrastructure security compliance (Layer 0) — they prove your servers are secure using server logs and configuration scanning. Sanna is building AI governance proof (Layer 1) — verifiable evidence that policies governed AI decisions. H33 covers Layer 2 (data separation via FHE — proof the AI never saw the plaintext) and Layer 3 (quantum-resistant audit trails valid for 30+ years). These are complementary, not competing. H33's SOC 2 evidence feeds directly into Vanta and Drata. The technical depth that neither competitor has is the FHE inference layer — cryptographic proof that the AI never touched the data it processed.
H33's FHE Inference Wrapper encrypts privileged documents before they reach the AI model. The model processes the encrypted content — performing review, classification, or extraction tasks — and returns encrypted results. At no point does the AI, the AI provider, or H33 have access to the plaintext of privileged documents. A Dilithium-signed attestation proves this cryptographically. This means law firms can use AI for contract review, due diligence, and litigation support while maintaining a defensible position that attorney-client privilege was never breached.
SOX Section 404 requires management to assess and report on the effectiveness of internal controls over financial reporting. When AI is used in financial controls — revenue recognition, risk assessment, fraud detection — the AI decisions become part of the internal control framework and must be auditable. H33's Decision Logger creates ZK-proof-verified records of every AI financial decision, the Policy Engine enforces financial control policies, and the Audit Report Generator produces SOX-ready evidence packages with Dilithium-signed proof bundles that carry criminal liability protection.
Every H33 customer gets a public verification URL at h33.ai/verify/yourcompany. This page shows a live compliance certificate signed with quantum-resistant Dilithium signatures, covering all active regulatory frameworks with real-time scores. When a prospect or partner sends a security questionnaire asking about your AI compliance posture, you send this link instead. The certificate is independently verifiable — any third party can confirm its validity without contacting H33. This replaces weeks of security review with a single link. Enterprise sales cycles get shorter every time.
Under 10 minutes for basic compliance scoring. Three lines of SDK code to wrap an existing AI endpoint with policy enforcement, decision logging, and FHE data separation. The SDK is available for Python, Node.js, and Rust, with an OpenAI-compatible proxy that requires zero refactoring of existing AI integrations. Local dev mode allows full compliance testing without sending data to any external service. GitHub Actions integration puts compliance gates directly in your CI/CD pipeline.
NY S7263 is a New York state bill that restricts AI from providing substantive responses across 14 licensed professions: attorneys, physicians, nurses, pharmacists, engineers, architects, accountants, veterinarians, dentists, optometrists, psychologists, social workers, physical therapists, and chiropractors. H33's Policy Engine includes a pre-built NY S7263 template that automatically detects queries falling within these professional domains and blocks non-compliant AI responses before they are generated. Every block event is logged with a ZK proof for regulatory examination.
Watch sensitive data get encrypted, processed by a blind AI, and verified with cryptographic proof. Not a video — live cryptography.
Connect your AI endpoint. H33 analyzes your inference pipeline against 8 regulatory frameworks and returns a compliance score with specific gaps identified. No commitment required.