Privent

Privent is a runtime security layer for agentic AI and n8n workflows, masking sensitive data before external models and tracking risk decisions.

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Runtime security for agentic AI

Privent is a runtime security layer for agentic AI, with a particular focus on n8n workflows. It embeds as a native node in the workflow so sensitive data can be detected, masked, and restored at trusted destinations before it reaches external models.

The product is built around two related workflows: Agent Security for protecting data moving through n8n agent flows, and AI Monitoring for observing AI usage across an organization. The site says Privent can run in Privent Cloud, a dedicated environment, or fully on-prem/private cloud, including deployments where the full stack and ML inference stay inside the customer network.

Core capabilities

Native n8n workflow node

Privent runs as a native n8n node inside the workflow canvas, so security checks happen between steps rather than around the agent. The product is positioned as a drop-in addition with no proxy or rewrites.

Session-aware risk scoring

The ACARS engine scores data in the context of the whole agent session, using signals such as entity sensitivity, semantic risk, contextual amplification, destination risk, behavioral velocity, and policy overrides.

Adaptive data protection

The APE engine can mask, swap, or route sensitive data based on risk level. Supported responses include tokenization and re-injection, semantic substitution, noise injection, org identity decoupling, structural decomposition, and risk-conditioned routing.

Workflow nodes for tokenization and audit

Privent’s n8n package includes Session, Tokenize, Detokenize, Handoff, and Audit Event nodes, along with a credential type. The docs say tokenization handles kinds such as EMAIL, SSN, CREDIT_CARD, IBAN, AWS_KEY, JWT, and API_KEY.

Flexible deployment options

The product supports multiple deployment modes, including Privent Cloud, a dedicated environment, and fully on-prem / private cloud deployment. The docs also describe a self-hosted n8n setup and cloud-based monitoring extension deployment.

AI monitoring and dashboard visibility

The monitoring docs say Privent can be rolled out to browser environments via MDM and can surface detection events in the dashboard in real time. The site also mentions AI Monitoring for organization-wide usage with ChatGPT, Claude, and Gemini.

Common use cases

Protect n8n agent workflows
Teams using n8n to build intake, triage, scheduling, or similar agent flows can mask sensitive fields before prompts reach an external LLM. The workflow keeps running while the model receives a tokenized or substituted version of the data.
Monitor organization-wide AI usage
Organizations that want to track how employees use ChatGPT, Claude, or Gemini can roll out the monitoring extension and feed detection events into the dashboard. The docs say deployment can be managed through MDM tools such as Google Workspace or Intune.
Support audits and policy review
Security and compliance teams can use the audit trail to review detections, decisions, timestamps, risk categories, and policy snapshots. The source says raw prompt text is not stored, while audit evidence can be exported for review.
Handle regulated data in AI workflows
Teams handling patient or other regulated data can use masking and trusted-destination controls to reduce exposure before AI calls and restore values only at approved sinks. The site specifically references PHI and HIPAA-oriented workflows.
Inspect agent handoffs and trust changes
Teams coordinating multiple agents can track handoffs and see when data moves between agents or sinks. The integration notes that handoff events and trust-score changes are surfaced in the Trust Map.

Pros and Cons

Pros

Security runs inside the workflow, so data can be masked before it reaches an external model.
The product offers context-aware risk scoring rather than simple block/allow filtering.
The n8n integration is drop-in and does not require a proxy, sidecar, or workflow rewrite.
The docs describe detailed audit data and exportable evidence for review workflows.
Multiple deployment options are available, including fully on-prem/private cloud.

Cons

The most detailed, documented workflow is for n8n; other integrations are limited in the docs and some are listed as coming soon.
Several capabilities are described at a high level, but the source does not provide exhaustive limits, performance characteristics, or pricing details for custom deployments.
AI Monitoring is mentioned in the site and docs, but the collected evidence is thinner than the n8n agent-security workflow.

FAQ

How do you set up Privent in n8n?

Privent is installed as a native n8n community node. The docs describe installing the package, adding a Privent API credential, and placing Session, Tokenize, and Detokenize nodes into the workflow so data can be transformed before it reaches external models.

Does Privent block or interrupt running workflows?

No. The n8n integration page says Privent is audit-only for the workflow: it does not proxy traffic, use a sidecar, or require orchestration changes, and the workflow keeps running while Privent watches and transforms sensitive data.

Does Privent store raw prompt content?

Privent’s documentation says raw prompt or agent payload text is processed only in-memory during scoring and is never written to disk or used for model training. The stored data is limited to audit and policy metadata such as scores, decisions, timestamps, and workflow identifiers.

Which agent frameworks does Privent support?

The product site says Privent supports n8n today, with LangGraph, CrewAI, MCP, and SDK integrations listed as coming soon in the docs.

Can Privent be deployed on-prem?

Yes. The site states that Privent can be deployed in the cloud, in a dedicated environment, or fully on-prem / private cloud, including a fully on-prem stack with ML inference inside your network.

Quick Facts

Category: Runtime security / AI security
Primary platform: n8n
Other AI monitoring targets: ChatGPT, Claude, Gemini
Deployment options: Cloud, dedicated environment, on-prem / private cloud
Pricing model: Free, Pro, and Teams plans listed; Enterprise is scoped with sales

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