Every major platform shift creates a new attack surface. AI agents are moving faster than any security team can see. We started Privent to fix that before the damage compounds.
Every organization deploying AI today has the same problem: the tools moved faster than the controls. Employees are sharing sensitive data with ChatGPT, Claude, and Gemini. Autonomous pipelines are pulling from internal systems and chaining tool calls with no human in the loop.
The attack surface is no longer theoretical. It's operational, and it's growing every time a new agent goes live or a new AI tool gets adopted.
We built Privent to be the security layer that sits between enterprise data and every AI model that touches it, whether that's a browser, an agent, or an autonomous pipeline running without any eyes on it at all.
Blocking creates shadow AI. When security gets in the way, employees route around it. We transform data by anonymizing, substituting, and fragmenting so AI stays useful and your data stays inside.
A single field is meaningless. An agent's full runtime state, including message history, tool outputs, and accumulated session context, is everything. ACARS reads both. External gateways read neither.
The best security is invisible to users. APE's 6-stage transformation happens in milliseconds. Pipelines keep running. Employees don't hit walls. The protected path is the easy path.
We validated Privent through 50+ direct CISO conversations before writing a line of product code. Every design decision in ACARS and APE came from a real security lead telling us what they actually needed.
We started where the exposure was most visible: the browser. Employees sharing sensitive data with ChatGPT, Claude, and Gemini, and no control layer in between. Privent's browser extension gave security teams their first real signal: a structured, 30-day picture of what was leaving the organization, and from where.
But the bigger problem was always the pipeline. Autonomous AI agents don't have a submit button. They pull from internal systems, compose prompts from live context, and execute tool calls without a human in the loop. The same data risk (PII, credentials, source code, financial data) flows through these pipelines at machine speed, invisibly.
Privent embeds directly into your n8n workflows as native security nodes. LangGraph, CrewAI, and custom SDK pipelines are next. It reads full agent state, scores risk with ACARS across six signals, and transforms sensitive data with APE before it reaches any external model. The security layer moves with the pipeline, not around it.
The end state is a unified control plane: one policy engine, one audit trail, one risk score, across every point where enterprise data meets AI, whether that's a browser tab or an autonomous pipeline running at 3am.
Submit-time interception across ChatGPT, Claude, and Gemini. MDM-deployable. No employee friction. 30-day Enterprise AI Risk Report.
n8n community nodes shipped: session-scoped tokenization, risk scoring, Trust Map auditing. LangGraph & CrewAI next.
One policy configuration, one audit trail, and one risk baseline across browser and agent surface, managed from a single dashboard.
Whether you're running agent pipelines today or planning your AI rollout, we want to understand your exposure before you discover it the hard way.
