Post-quantum AI means AI systems protected by NIST post-quantum cryptography. Every model weight signature, every inference attestation, and every training data seal uses algorithms that resist both classical and quantum attacks. H33 applies three independent PQ signature families to every AI operation, encrypts inference with fully homomorphic encryption, and compresses proof to a 74-byte attestation.
AI model weights, training data provenance, inference results, and agent decisions are all protected by RSA, ECDSA, or ECDH. A quantum computer running Shor's algorithm breaks all of them. The threat is not theoretical — it is a timeline.
AI model registries sign weights with RSA or ECDSA. Once a quantum computer breaks these signatures, any model can be silently replaced with a backdoored version. Supply chain integrity collapses entirely.
Data provenance chains that prove what an AI was trained on rely on classical hash-based signatures. Quantum forgery of training data provenance means you can no longer prove what your model learned or didn't learn.
Every AI inference result transmitted over TLS is vulnerable to harvest-now-decrypt-later attacks. Adversaries collecting encrypted inference traffic today will decrypt it when quantum hardware matures. Medical diagnoses, financial predictions, and classified analyses are all at risk.
H33 does not add a single post-quantum algorithm and call it done. Every AI operation is signed under three independent mathematical hardness assumptions. Breaking the attestation requires simultaneously breaking MLWE lattices, NTRU lattices, and stateless hash functions.
Fully Homomorphic Encryption for AI inference. H33's BFV and CKKS engines allow AI models to compute on encrypted data without ever seeing the plaintext. The model processes ciphertexts. The result is returned encrypted. Data exposure is not an access control problem — it is a mathematical impossibility.
H33-74 attestation for every operation. Every AI inference, model update, agent delegation, and governance decision produces a 74-byte post-quantum attestation. 32 bytes anchored on-chain, 42 bytes in Cachee. Any third party can independently verify without contacting H33.
NIST-compliant PQ signatures. ML-DSA-65 (FIPS 204), FALCON-512, and SLH-DSA provide three independent hardness assumptions. This is not defense in depth — it is defense in independence. Each family falls to a different class of mathematical attack, and no known algorithm threatens all three simultaneously.
Input data encrypted under BFV/CKKS before AI model sees it
Model computes on ciphertexts — plaintext never exposed
Inference result fingerprinted with quantum-resistant hash
ML-DSA + FALCON + SLH-DSA sign independently
Compressed proof: 32 bytes on-chain + 42 bytes in Cachee
Post-quantum cryptography must protect every layer of the AI stack, from training to inference to autonomous agent governance.
Model weights signed with three PQ families. Any tampering — poisoning, backdoor injection, or silent replacement — is cryptographically detectable and provable in court for 30+ years.
FHE-encrypted inference means the AI model never sees plaintext. Not gated by access control. Protected by the mathematical hardness of the Ring-LWE problem. Quantum-resistant by construction.
Autonomous AI agents operate under cryptographically enforced authority scopes. Every delegation, every scope change, every inter-agent handoff is PQ-attested and independently verifiable.
Post-quantum AI refers to AI systems whose cryptographic protections remain secure against both classical and quantum computer attacks. This includes signing model weights, encrypting training data, attesting inference results, and governing agent behavior using NIST-approved post-quantum algorithms such as ML-DSA (FIPS 204), ML-KEM (FIPS 203), and SLH-DSA (FIPS 205).
AI systems rely on classical cryptography (RSA, ECDSA, ECDH) for model signing, API authentication, TLS transport, and data encryption. A sufficiently powerful quantum computer running Shor's algorithm can break all of these in polynomial time. AI models deployed today with classical signatures will be forgeable once quantum computers mature — a harvest-now-decrypt-later threat.
H33 applies three independent post-quantum signature families (ML-DSA-65, FALCON-512, SLH-DSA) to every AI operation. Each inference, delegation, and decision produces a 74-byte attestation. H33 also provides FHE-encrypted inference so the AI model never sees plaintext data, and ZK-STARK proofs for verifiable governance — all quantum-resistant.
NIST finalized FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), and FIPS 205 (SLH-DSA) in 2024. The NSA's CNSA 2.0 guidance requires all national security systems to begin post-quantum migration by 2025 and complete it by 2033. Enterprises deploying AI today should treat post-quantum readiness as a current requirement, not a future one.
Yes. H33 integrates via a single REST API call. No changes to model architecture, training pipelines, or deployment infrastructure are required. The post-quantum attestation layer sits alongside your existing AI stack and adds cryptographic proof without modifying inference behavior.
See post-quantum AI attestation, encrypted inference, and agent governance running live. Your data, your AI endpoint. No commitment required.