As enterprises rapidly adopt LLMs across departments, the risk of sensitive data leakage grows exponentially. AI Privacy Gateway provides an on-premise, zero-trust data protection layer for all AI API traffic — masking PII, credentials, and business-critical data before it reaches any LLM provider.
Organizations deploying AI across their workforce face a unique set of data protection challenges that consumer-grade solutions cannot address:
When multiple departments use different AI tools (ChatGPT, Claude, Cursor, internal APIs), IT and security teams lose visibility into what data leaves the organization through AI prompts.
Enterprises operating in multiple regions must comply with GDPR in Europe, PIPL in China, CCPA in California, and LGPD in Brazil — each with different requirements for data transfer and processing.
Employees bypass IT-approved tools and use personal accounts for AI assistants, creating unauthorized data flows that security teams cannot monitor or control.
Building data protection into a single AI provider creates dependency. Enterprises need a provider-agnostic solution that works across OpenAI, Anthropic, DeepSeek, Google, and Azure.
AI Privacy Gateway is designed as a transparent, stateless proxy that integrates into your existing network architecture without requiring changes to your applications or workflows.
The gateway deploys entirely on your infrastructure — bare metal, VM, Docker, or Kubernetes. No data ever leaves your network for the masking engine. The encrypted vault stores PII mappings locally with AES-256-GCM encryption:
Define data protection policies centrally and enforce them across all AI API traffic:
{
"timestamp": "2026-06-21T10:30:00Z",
"client_ip": "10.0.1.42",
"target_llm": "openai/gpt-4",
"user_agent": "Cursor/0.45",
"entities_detected": {
"email": 2,
"phone": 1,
"api_key": 1
},
"action": "masked",
"request_size_bytes": 2842
}All masking events are logged with full metadata for compliance reporting. Logs can be exported to your SIEM via syslog, JSON file, or HTTP webhook.
AI Privacy Gateway is designed for production-scale workloads:
The gateway helps enterprises achieve compliance with major data protection regulations:
docker run -d \
--name ai-privacy-gw \
-p 9999:9999 \
-v ./vault_data:/app/vault_data \
-e LOG_LEVEL=info \
-e AUDIT_LOG=syslog \
ghcr.io/gunxueqiu6/ai-privacy-gateway:litehelm repo add ai-privacy https://gunxueqiu6.github.io/helm-charts
helm upgrade --install ai-privacy-gw \
ai-privacy/ai-privacy-gateway \
--namespace ai-security \
--set replicas=3 \
--set resources.requests.cpu=500m \
--set resources.requests.memory=256MiData is masked before it leaves your network. The AI provider never sees raw PII, credentials, or business-sensitive data.
Works with OpenAI, Anthropic, DeepSeek, Google Vertex AI, Azure OpenAI, and any OpenAI-compatible API. No vendor lock-in.
Integrate by changing a single base URL in your application configuration. No SDKs, no libraries, no code modifications.
Audit logging, encryption at rest and in transit, RBAC policies, and comprehensive compliance documentation.
99.9% uptime SLA with 24/7 support for enterprise customers. On-premise deployment with no external dependencies.
Yes. Each instance handles 10K+ QPS with under 5ms added latency. The gateway is stateless and scales horizontally behind any load balancer. Kubernetes Helm chart is available for production deployments with auto-scaling.
The gateway integrates via standard protocols: Prometheus metrics for monitoring, syslog/JSON for logging, environment variables for configuration, and HTTP headers for API management. No proprietary agents or agents required.
Yes. The gateway is stateless, so multiple instances can run behind any load balancer. There is no shared state dependency between instances. Each instance has its own encrypted vault with configurable sync options.
The gateway deploys entirely on your infrastructure. You choose where it runs — on-premise, your own cloud VPC, or a hybrid setup. No data processing dependencies on external services.
Yes. The Lite version supports unlimited requests with the same masking engine used in the enterprise deployment. Start with Docker on a single machine, test with your workloads, then scale to enterprise when ready.
Yes. Enterprise customers receive dedicated onboarding support including architecture review, policy configuration, integration testing, and team training. Custom policy rules and compliance documentation are also provided.
Deploy AI Privacy Gateway in 30 seconds. No code changes. No vendor lock-in.