Security & Encryption
Quantum-Safe Encryption
Kyber512 post-quantum cryptography defends against both classical and future quantum threats.
End-to-End Encrypted Pipeline
Data is encrypted on the client, never stored, and decrypted only within your secured runtime for processing.
No Data Retention by Default
We do not log, store, or persist your data. Your inputs and outputs are ephemeral—gone after processing.
AI Models & Performance
Secure Access to LLMs
Use OpenAI models (GPT-3.5, GPT-4o) or deploy Mistral-7B in isolated containers with full encryption in transit.
Low Encryption Overhead
Our hybrid encryption adds less than 50ms per request for real-time use cases.
Self-Hosted Option (Enterprise)
Deploy AI inside private, air-gapped environments for maximum control. No OpenAI contact required.
Compliance & Governance
HIPAA-Conscious Design
Designed with encryption-first principles and isolation by default. We do not sign BAAs or handle PHI.
Granular Audit Trails
All API activity is logged with metadata and usage context for internal governance and debugging.
SOC 2 Alignment (Non-Certified)
Our infrastructure follows SOC 2 design patterns, though no official certification is claimed at this time.
Developer Experience
OpenAI-Compatible API
Drop-in secure replacement for OpenAI APIs—no code rewrite required.
SDKs for Any Stack
Use Python, Node.js, Go, or build your own using our reference implementations.
Live Usage Dashboard
Track throughput, latency, and encrypted request logs in real time.
QuantmLayer vs Traditional Solutions
See how our encryption-first design compares to legacy security methods
Seamless Integration
Drop-in replacement for OpenAI API with enterprise security built-in
Before: OpenAI Direct
import openai
openai.api_key = "sk-..."
response = openai.chat.completions.create(
model="gpt-4",
messages=[{
"role": "user",
"content": "Analyze patient data..."
}]
)
# ❌ Data sent to OpenAI unencrypted
# ❌ Non-compliance aligned design
# ❌ Data may be retainedAfter: QuantmLayer
from quantmlayer import SecureGPT
client = SecureGPT(api_key="ql_...")
response = client.chat.completions.create(
model="gpt-4",
messages=[{
"role": "user",
"content": "Analyze patient data..."
}]
)
# ✅ Quantum-safe encryption
# ✅ Compliance aligned design
# ✅ Zero data retention