System Architecture Overview
QBITEL Bridge is a distributed, cloud-native platform built with a four-layer polyglot architecture for quantum-safe protocol intelligence.
Design Principles
- Polyglot by design -- each component uses the language best suited to its workload
- Cloud-native -- Kubernetes-first deployment with Helm, service mesh, and operator support
- Quantum-safe -- post-quantum cryptography at every layer (ML-KEM, ML-DSA, Falcon)
- Zero-trust -- mutual TLS, identity-based access, and microsegmentation throughout
- AI-first -- autonomous multi-agent system with on-premise LLM intelligence
Four-Layer Architecture
QBITEL Bridge comprises four primary layers, each implemented in the language best suited for its role:
Layer 1: AI Engine (Python)
The intelligence core. Handles protocol discovery, ML classification, multi-agent orchestration, compliance automation, and LLM-powered analysis. Built with PyTorch, FastAPI, and LangGraph.
Path: ai_engine/
Layer 2: Data Plane (Rust)
Wire-speed packet processing with PQC-TLS termination, deep packet inspection, and DPDK integration. Provides the performance-critical path for traffic analysis.
Path: rust/dataplane/
Layer 3: Control Plane (Go)
Service orchestration, OPA policy enforcement, device management, and gRPC-based inter-service communication. Built with Gin, OPA, and HashiCorp Vault integration.
Path: go/
Layer 4: UI Console (React/TypeScript)
Enterprise admin console with real-time dashboards, protocol marketplace, and compliance reporting. Built with React, Vite, Material UI, and TypeScript.
Path: ui/console/
Agentic AI Ecosystem
The platform implements a multi-agent architecture where specialized AI agents autonomously manage security operations:
- Zero-Touch Decision Agent -- autonomous security analysis and response with LLM-powered reasoning
- Protocol Discovery Agent -- learns unknown protocol grammars from raw traffic
- Security Orchestrator -- coordinates multi-agent collaboration and escalation
- Threat Analyzer -- ML-based threat classification with MITRE ATT&CK mapping
- Compliance Monitor -- automated compliance assessment against regulatory frameworks
Data Flow
A typical request flows through the system as follows:
- Network traffic enters the Rust Data Plane for wire-speed capture and PQC-TLS termination
- Packets are forwarded to the Python AI Engine for protocol discovery and threat analysis
- The Go Control Plane enforces OPA policies and orchestrates service responses
- Results are displayed in the React UI Console with real-time dashboards
- All communication is protected by post-quantum cryptography (ML-KEM-1024, ML-DSA-87)
Technology Stack
| Category | Technologies |
|---|---|
| AI/ML | PyTorch, scikit-learn, LangGraph, Ollama, RAG |
| APIs | FastAPI, gRPC, Gin (Go) |
| Data Plane | Rust, DPDK, liboqs, PQC-TLS |
| Orchestration | Kubernetes, Helm, Istio, Envoy |
| Databases | PostgreSQL, TimescaleDB, Redis, Qdrant |
| Observability | Prometheus, Grafana, OpenTelemetry, Sentry |
| Security | OPA, HashiCorp Vault, mTLS, PQC (ML-KEM, ML-DSA) |
| Streaming | Apache Kafka with PQC-encrypted producers |
| Frontend | React, TypeScript, Vite, Material UI |
Cloud-Native Integration
QBITEL Bridge integrates natively with cloud security services:
- AWS -- Security Hub integration for centralized findings
- Azure -- Sentinel integration for SIEM correlation
- GCP -- Security Command Center for threat detection
- Kubernetes -- Custom operator, admission webhooks, and service mesh integration
Next Steps
- AI Engine -- deep dive into the Python AI/ML layer
- Rust Data Plane -- wire-speed processing and PQC-TLS
- Go Control Plane -- policy enforcement and service orchestration
- UI Console -- React admin dashboard