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Agent Security for n8n workflows and AI Monitoring for enterprise AI usage, deployed in our cloud or fully on-prem.

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Use case

What one agent knows, another shouldn't.

Cross-agent

memory leakage

In multi-agent pipelines, sensitive data such as patient records, lab results, and internal configs builds up across steps and gets passed between agents as shared memory. No gateway sees this internal traffic. Privent does, and controls what is allowed to cross from one agent to the next.

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How it works

How Privent solves this

01.

Data builds up as agents hand off work

Each agent adds sensitive data, so the prompt soon holds several sources combined.

02.

Privent checks every handoff between agents

At each handoff, Privent reads everything passing between agents.

03.

Sensitive fields are masked before the next agent

Privent masks the sensitive fields before the next agent receives them.

Detection

What Privent detects

01
Shared memoryEverything collected across earlier agent steps, including retrieved documents and AI outputs
02
Data passed between agentsTask outputs, tool results, and handoff context moving from one agent to the next
03
Tool & query resultsRaw output of database queries, API calls, and file reads carried into later steps
04
Context passed into other agentsCases where one agent's output is fed straight into another agent's instructions
Audience

Who this is for

01

Multi-agent platform engineers

Teams building workflows where multiple agents collaborate and share state, n8n today, CrewAI and LangGraph next.

02

Security architects

Teams setting trust boundaries in agent systems who need enforcement at the handoff, not just the LLM gateway.

03

AI product teams in healthcare

Teams chaining agents across patient, billing, and operations data, where keeping roles isolated is a HIPAA requirement.

Get started

Stop cross-agent memory leakage before it happens

We integrate in under 30 minutes. No orchestration changes required. Your pipelines keep running. Privent keeps watching.

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