Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Your AI gateway doesn’t solve this. Neither does your identity platform. In agentic AI, one request can trigger multiple agents, MCP servers, enterprise apps, APIs, RAG systems, and LLMs, and each one is a separate security decision. The old question was “Can this user access this application?” The new question is “Can this task use this piece of enterprise data?”

Your AI gateway and identity platform don’t decide whether a task can use a piece of enterprise data. In agentic AI, one request can trigger multiple agents, MCP servers, APIs, RAG systems, and LLMs, and each one is a separate security decision. This video explains why enterprise AI needs a runtime data control plane. It covers why identity alone can’t make the call, why masking PII isn’t enough when business context is sensitive, and how Protecto applies allow, mask, anonymize, rewrite, or selective reveal decisions across the AI workflow. It ends with a Fortune 100 multi-agent example.

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.