Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Planning an enterprise AI rollout in 90 days? Establishing a robust AI Gateway Architecture is the critical first step to ensuring data compliance, enforcement, and security. Letting application traffic run straight to LLMs exposes your organization to severe security and compliance liabilities.

Understanding the nuances of AI Traffic Security is critical because traditional firewalls and API gateways are fundamentally ill-equipped to inspect unstructured natural language. In the past, enterprise data remained safely within the corporate perimeter. Today, employees are pasting highly sensitive information directly into external models, creating a massive vulnerability where the risk lies in the meaning and content, rather than syntax or connections.

Sensitive data used to live in predictable places database columns, known field names, structured rows. That changed when data moved into documents. And it changed again when AI workflows arrived. In this video, we walk through why detecting sensitive data in AI pipelines is fundamentally different from traditional data discovery, and why the old approaches break. We cover the four failure modes that make detection hard in AI workflows.

AI agents don't just answer questions—they access enterprise data, call APIs, interact with MCP servers, and trigger workflows. That means sensitive information like PII, PHI, HR records, pricing data, financial information, and confidential business data can flow through AI systems. In this YouTube Short, Amar Kanagaraj explains why AI governance, data security, and data sovereignty are essential for enterprise AI deployments—and how the NetScaler × Protecto integration helps organizations secure AI workflows.

As we move deeper into 2026, enterprise AI has shifted from simple chat interfaces to autonomous agents executing complex workflows. If you aren't routing this traffic through a dedicated AI Gateway, your sensitive data is already at risk.

ChatGPT is read-only, but AI Agents take action on your behalf. What happens when they go rogue? Discover the hidden cybersecurity risks of Agentic AI and unauthorized remote execution. AI gateways were built for a world where AI meant "prompt in, response out." That world is gone. Today, AI agents call APIs, trigger workflows, and take actions across your enterprise systems autonomously. This massive shift from passive data exfiltration to active, unauthorized execution requires a completely new security model where every input is treated as potentially hostile.