Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The goal of AI data security isn't to remove data. It's to control it. For every request, Protecto decides in real time whether data is allowed, masked, anonymized, rewritten, or selectively revealed. Those decisions change with the user, the task, the destination, and the policy.

A sales executive can view contracts, pricing, discounts, and account details. Ask an AI assistant to summarize a customer meeting, and it needs the customer name. Ask the same assistant to draft a marketing email, and it must not include confidential pricing or discounts. Same user, same data, different task, different answer.

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.

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.

Sensitive data detection was much easier when information lived inside structured databases. Tables, columns, field names, and predictable data types gave security teams a clear map of where sensitive information lived. But when sensitive data moved into documents and PDFs, that map disappeared. Names, addresses, phone numbers, credit card numbers, and other sensitive information could be buried inside natural language.

Most healthcare AI systems rely on Retrieval-Augmented Generation (RAG) and vector databases to search millions of insurance claims, physician notes, lab reports, billing documents, and customer records. But what happens before those documents become searchable? In this Short, we explain how embedding models convert raw healthcare documents into vectors—and why patient names, medical record numbers (MRNs), diagnoses, and Social Security numbers can end up stored inside your vector database if data isn't protected before ingestion.

Why Your Healthcare RAG Pipeline Is Leaking PHI Most healthcare organizations believe their AI assistant is secure once they restrict who can log in. Unfortunately, that's only part of the story. Modern healthcare AI applications rely on Retrieval-Augmented Generation (RAG), where patient records, physician notes, insurance claims, and medical documents are embedded into vector databases to power intelligent search.