Darktrace monitors your network. CrowdStrike monitors your endpoints. SentinelOne monitors your devices. None of them encrypt your data. H33 does — with fully homomorphic encryption, zero-knowledge proofs, and three native AI agents that detect threats at sub-microsecond speed without ever seeing plaintext.
The threat is not just malware anymore. It is quantum computers breaking every key in your infrastructure. It is nation-state adversaries running harvest-now-decrypt-later campaigns against your TLS traffic right now, banking on a cryptanalytically relevant quantum computer within the decade. Traditional AI security platforms have no answer for this.
Darktrace watches your network and learns what "normal" looks like. CrowdStrike sits on your endpoints and hunts threats. SentinelOne automates the response. All three are valuable. But all three share the same fatal assumption: they need access to your plaintext data to protect it. If an attacker is already inside the perimeter — or the data is intercepted in transit — these platforms are watching a breach happen, not preventing one.
H33 operates in a fundamentally different category. Your data is encrypted with BFV fully homomorphic encryption before it ever leaves the client. Fully homomorphic encryption runs computation over encrypted data (ciphertext) without decrypting it — the primitive owned by the FHE Platform, which computes on encrypted data. The FHE inside this AI-security comparison is a supporting expression of that platform. The server performs matching, scoring, and fraud detection entirely on ciphertexts. Zero-knowledge proofs attest results without revealing inputs — a distinct primitive: FHE runs computation over encrypted data without decrypting, while the ZK Platform proves a statement is true without revealing the underlying data. Dilithium signatures seal every transaction for a post-quantum audit trail. The plaintext never exists on the server. There is nothing to steal.
Three native Rust AI agents — harvest detection, side-channel detection, and crypto health monitoring — run concurrently inside the authentication pipeline at a combined ~2.35µs. No Python. No external ML libraries. No network hop to an inference service. They operate on encrypted metadata and cryptographic telemetry, catching timing attacks, bulk exfiltration patterns, and parameter degradation in real time. This is not bolted-on AI. It is fused into the cryptographic pipeline itself.
Cryptographic prevention vs. behavioral detection. Ten dimensions that define your security posture.
| Capability | H33 | Darktrace | CrowdStrike | SentinelOne |
|---|---|---|---|---|
| Approach | Cryptographic prevention | Behavioral AI | Endpoint detection (EDR) | Autonomous AI (XDR) |
| Data exposure | Zero — FHE encrypted | Needs plaintext network flows | Needs plaintext endpoint access | Needs plaintext endpoint access |
| Post-quantum secure | Yes — Dilithium + Kyber (NIST) | No | No | No |
| Harvest-now-decrypt-later | Immune (lattice-based FHE) | Vulnerable | Vulnerable | Vulnerable |
| AI fraud detection | 0.69µs harvest + 1.14µs side-channel + 0.52µs crypto health | Seconds to minutes | Seconds to minutes | Seconds to minutes |
| Zero-knowledge proofs | Yes — 0.059µs verification | No | No | No |
| Cryptographic attestation | Dilithium ML-DSA (189µs) | No | No | No |
| Deployment | REST API — one call | Network appliance / cloud | Agent on every endpoint | Agent on every endpoint |
| Pricing | from $0.001/auth at scale (credit-based) | $100K+/yr enterprise | $8–15/endpoint/month | $6–12/endpoint/month |
| Throughput | 2,293,766 auth/sec sustained | N/A (passive monitoring) | N/A (agent-based) | N/A (agent-based) |
Graviton4 (c8g.metal-48xl, 192 vCPUs). Every operation is post-quantum secure. Every number links to the benchmark page.
Native Rust AI agents fused into the cryptographic pipeline. No Python. No external ML. No additional latency — they execute in parallel with FHE and ZK operations.
Identifies harvest-now-decrypt-later attack patterns in real time. Flags anomalous collection behavior, unusual query patterns, and bulk data exfiltration attempts — all while operating on encrypted data via FHE. The adversary collecting your ciphertexts today for quantum decryption tomorrow gets caught before the first batch completes.
Catches timing attacks, cache attacks, and power analysis during authentication. Monitors statistical deviations in authentication timing, memory access patterns, and computational signatures. If someone is probing your cryptographic pipeline for side-channel leakage, this agent flags it before they extract a single bit.
Validates cryptographic parameters and detects degradation in real time. Monitors FHE noise budgets, key freshness, RNG quality, and lattice parameter integrity. Alerts before any cryptographic weakness can be exploited — not after. Your lattice parameters do not silently rot on this platform.
Authenticate, detect threats, and get AI agent verdicts — all in a single request. The response includes FHE match results, ZK proofs, and real-time AI fraud scoring.
// 1. Initialize the H33 client const h33 = new H33Client({ apiKey: "h33_pk_..." }); // 2. Capture biometric & encrypt client-side (FHE) const embedding = await h33.biometric.capture("face"); const encrypted = await h33.fhe.encrypt(embedding); // 3. Authenticate with full AI agent pipeline const result = await h33.auth.verify({ userId: "user_abc123", biometric: encrypted, // FHE ciphertext, never plaintext agents: true, // Enable AI fraud detection }); // result.match = true // result.zkProof = "0x..." (verifiable ZK attestation) // result.dilithiumSig = "..." (post-quantum audit trail) // result.agents.harvest = { safe: true, latency: "0.69µs" } // result.agents.sidechannel = { safe: true, latency: "1.14µs" } // result.agents.cryptoHealth = { healthy: true, latency: "0.52µs" } // result.latency = "35.25µs"
H33's prevention model rests on FHE. Five questions define exactly what that means.
| What does FHE compute? | It runs computation directly over encrypted data (ciphertext) — matching, scoring, fraud detection — and returns an encrypted result without ever decrypting. This is the primitive owned by the FHE Platform; the FHE here is a supporting expression of it. |
| What does it not do? | It does not prove a statement without revealing data (that is zero-knowledge), and it does not decrypt during computation. The plaintext is never exposed on the server. |
| How is it different from ZK? | FHE runs computation over encrypted data without decrypting; the ZK Platform proves a statement is true without revealing the underlying data. H33 uses both, for different jobs. |
| How does H33-74 fit in? | FHE produces the encrypted computation; H33-74 anchors the evidence of it as a portable post-quantum attestation. Neither owns the other — each remains verifiable on its own. |
| When should you choose FHE? | When sensitive data must stay encrypted while it is processed — authentication, biometric matching, fraud analytics across parties. Reach for FHE when the data cannot be exposed during computation; do NOT reach for it when you only need to prove a fact is true without revealing the data (that is zero-knowledge) or when plaintext processing is acceptable. Behavioral detection (Darktrace, CrowdStrike, SentinelOne) still needs plaintext; FHE removes that requirement. It complements, rather than replaces, endpoint protection. |
Free tier includes 1,000 authentications per month with full FHE + ZK + AI agent pipeline. No credit card required. Post-quantum secure from your first API call.