Autonomous pipelines have no eyes. Privent does.
When LangGraph, CrewAI, or n8n pipelines pull from internal systems and compose prompts autonomously, sensitive data — customer records, internal configs, strategic documents — crosses into external models with no human in the loop. Privent embeds directly inside the execution graph and reads everything before it leaves your infrastructure.
Pipeline builds payload from internal sources
Your agent queries databases, calls internal APIs, and chains LLM responses across multiple steps. Sensitive data accumulates in the prompt context as the pipeline progresses — invisible to any external gateway.
Privent intercepts at the execution node
PriventSecurityNode sits inside the agent graph and reads the full runtime state before any external call. Unlike proxy-based tools, it sees tool call arguments, accumulated memory, and inter-step context — not just the final prompt string.
Risk scored, payload transformed
ACARS scores the payload across 6 signals. If risk exceeds threshold, APE transforms the data — masking, fragmenting, or blocking — before the pipeline continues. The agent receives a coherent, safe version. Full audit trail recorded.
Teams building on LangGraph, CrewAI, or n8n who need security embedded in the graph without rebuilding their orchestration.
Security teams responsible for ensuring that AI pipeline outputs comply with data classification policies before reaching external vendors.
Executives who need provable evidence that autonomous pipelines do not expose regulated or confidential data to third-party models.
We integrate in under 30 minutes. No orchestration changes required. Your pipelines keep running — Privent keeps watching.
