ABOUT CYPHERAI
From Trust-Based to Math-Based Security
CypherAI was founded to solve the fundamental paradox of the AI age: organizations cannot use cloud AI on their most sensitive data because it requires exposing that data. We eliminate this trade-off with mathematically-enforced encrypted inference.
Proven in Production
Empirical evidence of mathematically-enforced encrypted inference operating at enterprise scale. Verified across national defense, Tier-1 financial infrastructure, and security agencies.
Deployments & Engagements
Encrypted OSINT Platform
Millions of records, <0.4s query time. Zero-knowledge keyword indexing deployed via AWS CloudFormation. 60% cost reduction vs. isolated on-premises systems.
Encrypted Fraud Detection
Billions of transactions. Real-time encrypted inference pipeline (<100ms target). Full PII protection throughout entire model inference path.
Independent Validation
Validated by 2 Independent Security Agencies
Live Deployment with Government Agency (2025)
NVIDIA Inception Program Member
Post-Quantum Resilient (TFHE)
Field Report: Encrypted National MOD OSINT
“Our OSINT analysts were paralyzed. We couldn't run sensitive queries in the cloud due to OPSEC risks. Confidential computing still decrypts inside enclaves - not sufficient for our classified workloads. CypherAI's mathematical guarantee was the only approach that met our requirements.”
- Lead Intelligence Architect, Government Agency
The Deadlock
Intelligence analysts needed to search for specific keywords and geolocation markers in public data streams but could not expose their “search intent” to the cloud provider or host nation infrastructure. This is a math problem, not a policy problem.
The Solution
Deployment of Scouter (CY-ADQ) via AWS CloudFormation. Enabled zero-knowledge keyword indexing on a database of millions of posts within the client's sovereign AWS perimeter. Mathematical guarantee - not contractual promise. 60% cost reduction vs isolated on-premises systems.
Report: Encrypted Large-Scale Biometric Matching
“We had a gallery of 5 million biometric templates too sensitive to upload to the cloud. Our on-premises environment lacked the massive compute power for real-time matching. CypherAI enabled computation on ciphertext with mathematically-enforced privacy - not just contractual promises.”
- Head of Biometric Security, National Infrastructure Authority
The Conflict
Facial recognition at scale requires high-compute instances, but data privacy mandates forbade storing plaintext templates in any untrusted cloud environment. Confidential computing still decrypts inside enclaves - not sufficient for biometric data.
The Result
MORFIX (CY-FRP) enabled real-time identification of up to 8 faces per second with mathematically-enforced privacy. Matching latency under 5 seconds across a 5M template gallery. Zero plaintext exposure.
Standard Benchmarks
| Capability | Scale / Environment | Latency | Throughput |
|---|---|---|---|
| Encrypted Keyword Search | Millions of Records (OSINT) | 0.82s | 150/sec |
| Biometric Vector Matching | 5M Gallery Templates | 4.2s | 8/sec |
| Encrypted LLM Inference | GPT-4 / Claude / Llama | Similar to standard | Model-dependent |
| FHE Query Generation | Standard x86 HW | 22ms | 45/sec |
The Sovereign Compute Model
Key Management
Encryption keys never leave customer control. Raw data remains inside the perimeter. Mathematical guarantee.
Encrypted Processing
Data is processed as ciphertext. The host provider has zero visibility into underlying values. From trust-based to math-based security.
Our Story
For decades, homomorphic encryption promised to allow computation on encrypted data without ever needing a key. But it was too slow. 100-1000× overhead made production deployment impossible.
Our breakthrough: optimizing TFHE at the representation level achieved a 400× speedup over industry standard libraries - shifting encrypted compute from theoretical possibility to deployed reality.
Today, CypherAI delivers cryptographically-enforced LLM inference for defense, intelligence, finance, healthcare, and government - shifting from trust-based to math-based security.
The 400× Breakthrough
The Team
FULL BIOS
Etzik Bega
Co-Founder & CEO
Serial Entrepreneur · Lt. Colonel (Ret.), IDF
25 years encryption and cybersecurity expertise from the highest levels of defense intelligence. Former Head of Cybersecurity Strategic Planning, IDF. Serial entrepreneur with deep understanding of defense procurement and enterprise security.
Gery Biran
Co-Founder & CTO
FHE Pioneer · 40+ Years Algorithm Expertise
One of the world's leading experts in production homomorphic encryption. Architect of CypherAI's breakthrough 400× performance improvement over academic HE standards. Pioneer in making FHE work at enterprise scale.
The Market Opportunity
Defense, intelligence, finance, healthcare, and government are currently paralyzed. $75B+ in high-value AI workloads remain stranded because data exposure risks prevent cloud AI adoption.
Defense & Intel
$15B+ TAM
Classified intelligence processing and encrypted AI deployment for national security.
Tier-1 Finance
$35B+ TAM
Encrypted fraud detection, AML analytics, and zero-exposure customer intelligence.
Healthcare
$25B+ TAM
Mathematically-enforced HIPAA AI and encrypted clinical insights.
Sovereign Cloud
$20B+ TAM
67 countries mandating sovereign data infrastructure by 2026.
Market Inflection Points
GenAI Surge
Massive demand for encrypted LLM inference and zero-trust AI processing across regulated industries.
Regulation
EU AI Act, GDPR Article 32, HIPAA, and sovereign data mandates require cryptographic - not contractual - privacy guarantees.
From Trust-Based to Math-Based Security
Traditional enterprise security relies on policies, access controls, and perimeter defense. But when data must be processed in the cloud, it is decrypted - creating an unavoidable exposure point. This is why defense and financial institutions cannot leverage AI on their most sensitive datasets.
Homomorphic encryption changes the equation entirely. It enables computation directly on encrypted data - without ever decrypting it. The cloud operator processes ciphertext and returns encrypted results. Only the data owner holds the keys. The security guarantee is mathematical, not contractual.
This is the shift CypherAI delivers: mathematically-enforced encrypted LLM inference that enables classified and regulated data to flow through AI infrastructure with zero plaintext exposure.
Traditional Encryption
CypherAI Encrypted Inference
Zero plaintext exposure at any stage - mathematical guarantee
Ask Anything. Reveal Nothing.
Encrypted Infrastructure for the AI Age
We're unlocking $75B+ in regulated AI workloads that cannot be deployed today. The only mathematically-enforced encrypted LLM inference platform in production. Join us in building the mathematical layer of cloud trust.