Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

What if masking sensitive data actually broke your AI application? Most AI security strategies start with a simple rule: mask sensitive information before it reaches the LLM. But real AI workflows are more complex. An AI agent might need to search a CRM, retrieve a contract, check account details, call external tools, and generate a response. Each step may require a different level of access to sensitive data.

Can enterprises use powerful AI models with sensitive data without forcing everything into a self-hosted model? That's the problem this demo explores. In this session, we demonstrate Protecto Privacy Gateway integrated with LibreChat and show how sensitive enterprise data can be protected across AI chat, RAG, agents, MCP/tool calls, and long-running conversations.

Most DLP tools can mask a PII value on the way into a model. Almost none of them can make that value usable again when the AI needs to call a tool with it, so the masking breaks the workflow instead of protecting it. This is Protecto Privacy Gateway for AI Chat, live in a self-hosted LibreChat deployment. Watch what happens when a masked account number needs to resolve at a tool boundary — and comes back correct.