One proxy protects every AI workflow — from a single chatbot to a fleet of autonomous agents across multiple providers.
Chatbots, summarizers, and any application making LLM calls. Every request is an opportunity for PII or secrets to leak.
Every chat completion, summary, or AI-generated response sends user context to an external model. That context contains names, emails, account numbers, and sometimes credentials. Without a security layer, sensitive data leaves your infrastructure with every API call — a compliance incident waiting to happen.
Grepture sits in the request path and scans every outbound payload. PII is masked with reversible tokens, secrets are redacted, and prompt injections are blocked — all before the request reaches the model. On the way back, masked tokens are restored so your app delivers complete, personalized responses. The LLM never sees real data.
Autonomous agents with tool access, multi-step workflows, and MCP servers. You can’t predict every request an agent will make — but you can secure the network path.
AI agents call tools, chain LLM requests, and make autonomous decisions. They pull data from databases, call internal APIs, and send context to external models in ways that are hard to predict or audit. Traditional application-level controls can’t keep up with agentic workflows.
The proxy sits on the network path — between the agent and every external service. No matter what the agent does, every outbound request is scanned for PII, secrets, and sensitive patterns. Every inbound response is logged. One security layer covers every tool call, every LLM request, every MCP interaction.
Knowledge bases pulling from internal docs, wikis, and databases. Retrieved chunks often contain data that should never reach an external model.
Retrieval-augmented generation pulls chunks from internal documents, knowledge bases, and databases. These chunks contain secrets (API keys left in docs), personal data (employee info, customer records), and proprietary content. Every retrieved chunk is a potential data leak when sent to an external model.
Grepture scans every chunk in the request payload before it leaves your network. Secrets are blocked, PII is masked, and proprietary patterns trigger alerts. The AI model works with clean context. Your knowledge base stays private.
Teams using OpenAI, Anthropic, Google, and other providers need consistent security. One proxy, one policy layer, one audit trail.
Teams using multiple AI providers end up with inconsistent security controls. Each integration has different logging, different policies, and different risk exposure. There’s no single place to enforce data protection rules — and no unified view of what data is leaving your organization.
Route every model call through one proxy with consistent detection rules, unified audit logging, and a single dashboard. Same policies across OpenAI, Anthropic, Google AI, Azure, and any other provider. Add or switch providers without rebuilding your security stack.
Grepture isn’t limited to AI providers. Wrap any outbound HTTP call with grepture.fetch() and apply the same detection rules to webhooks, payment APIs, third-party integrations — anything.
Sensitive data doesn’t only leak through AI calls. Webhooks send customer data to third-party services. Payment integrations pass PII to processors. Analytics platforms receive user context. Every outbound HTTP call is a potential data leak — and most have zero scanning or controls.
Use grepture.fetch() as a drop-in replacement for fetch(). Every outbound request flows through the proxy, scanned against the same detection rules you use for AI traffic. Same PII detection, same secret scanning, same audit trail — for any HTTP call to any external service.
Deploy Grepture in minutes. No code changes required.
Free for up to 1,000 requests/month · No credit card required
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