Securing n8n Agentic Workflows in Real-time with Operant AI скачать в хорошем качестве

Securing n8n Agentic Workflows in Real-time with Operant AI

AI automation is accelerating. Platforms like n8n, RAG systems, and Model Context Protocol (MCP) let teams build powerful AI workflows for finance, customer service, and operations. In this demo, we walk through a real n8n agentic workflow powered by RAG (Milvus via MCP) in two states: vulnerable by default, then secured with Operant AI Gatekeeper and MCP Gateway. You'll see exactly how sensitive data like passport numbers, credit card details, and personal identifiers escape your trust boundary before LLM guardrails even have a chance to act. And then you'll see how Operant stops it. What we cover: How user input containing PII gets sent raw to third-party AI providers How the MCP tool responses inject sensitive financial data directly into the model context Why RAG and vector databases create a hidden, persistent data exposure risk Why LLM output guardrails miss the point entirely How Operant's n8n node secures every edge of your workflow without rebuilding it As AI agents, tools, and RAG systems become production-critical, security must move upstream. You can’t secure AI with prompt engineering alone. You can’t rely on model behavior to protect sensitive data. You must control what reaches the model. This demo shows exactly how. #aisecurity #agenticai #n8n #mcp #RAG #llmsecurity #cybersecurity #aiinfrastructure #ZeroClickAttacks #OperantAI