DEV Community: Asil Ozyildirim

Privent v2.2: An Open-Source Firewall for Data Leaks on Agentic AI

Asil Ozyildirim — Thu, 02 Jul 2026 21:32:39 +0000

Repo: github.com/privent-ai/n8n-nodes-privent, MIT licensed, 573 local detectors, no API key required to try the local mode. If you find a hole in it, that's exactly what the last section of this post is asking you to do.

Every time an AI agent workflow sends a prompt to a model, whatever's in that prompt goes to a third party, full stop. Customer emails, phone numbers, card numbers, home addresses sitting in a support ticket: if it's in the input, it's in the request body of an API call nobody reviews per execution. That's not a hypothetical, it's just how the plumbing works, and almost nobody is checking what's actually flowing through it.

We hit this wall directly building AI agent workflows that touch real user data. n8n's built-in Guardrails mask PII by replacing values with [REDACTED], but that's destructive. Once an email becomes [REDACTED], every downstream node that needs to send a confirmation, write to a CRM, or generate an audit log has nothing left to work with. You've traded one leak for a broken workflow, not actually fixed anything.

So we built something that doesn't destroy the data, it just hides it from the one component that shouldn't see it raw: the model.

Try it in about a minute, no account needed:

npm install n8n-nodes-privent

Full source, issues, and the threat model doc are all in the repo: github.com/privent-ai/n8n-nodes-privent.

Reversible tokenization, not redaction

user@acme.com becomes [EMAIL_001] before the LLM ever sees the prompt. The agent reasons, plans, and calls tools using tokens the whole way through, it never has the raw value. Only at egress points you explicitly mark as trusted does the original value get restored. Redaction throws information away. Tokenization just moves where the raw value is allowed to exist.

The primitives

We designed this as six composable pieces, not one big opaque node:

Session: a scoped token vault. Every node in a workflow run shares a session, so [EMAIL_001] means the same address everywhere in that execution, not a new random token per node.
Tokenize: scans the payload, detects sensitive entities against 573 local regex patterns, replaces matches with deterministic tokens. Runs entirely inside n8n. Nothing leaves the instance for this step.
Risk Check: optional. Sends the payload to our ML scoring backend, gets back a risk level (LOW/MEDIUM/HIGH/CRITICAL) with an entity breakdown. Skip it entirely and you're fully local-detection-only.
Detokenize: resolves tokens back to real values, but only at points the workflow designer placed intentionally. It's not tool-callable by the agent (usableAsTool: false), and Strict Mode blocks resolution unless the destination URL is on an explicit Trusted Sinks list.
Audit: structured events for every tokenize/detokenize action, risk scores, session IDs, sent to our backend or to a webhook you configure yourself.
Handoff: marks a trust boundary when a workflow delegates to a sub-agent or external system, so the audit trail stays coherent across agent hops instead of going dark at the handoff.

All six are implemented in the open, nothing here is described differently than it's actually coded. Privent.node.ts in the repo is the exact source: github.com/privent-ai/n8n-nodes-privent.

573 detectors, and why the number matters more than it sounds

We didn't stop at emails, phones, cards, SSNs, IBANs, that's maybe a day of regex work and every DLP tool on earth has it. The detectors that actually mattered came from building real workflows for real industries:

Regional government IDs: Dutch BSN, Indian Aadhaar, Polish PESEL, Brazilian CPF, Singapore NRIC, South Korean RRN, Turkish ID, Emirati ID
Cloud and platform credentials: AWS Access Key, AWS ARN, GitHub tokens, OpenAI keys, Stripe, Heroku, Firebase, Azure Resource ID, GCP service accounts
Crypto addresses: Bitcoin, Ethereum, Solana, Cardano, Monero, Tezos, Algorand
Shipping tracking numbers across 15+ carriers
Gig-economy identifiers: Uber trip IDs, DoorDash refs, Upwork job IDs, because fintech and HR automation workflows run into these constantly and nothing else detects them

573 entity types, spanning 90+ countries and industries, all evaluated locally inside n8n.

The architecture, without the marketing gloss

We'd rather you know exactly where the line is than find out later:

Detection (regex, all 573 patterns): inside n8n. Nothing leaves your instance for this.
Token vault (the value to token mapping): server-side. Either Privent Cloud (api.privent.ai) or self-hosted; point the Base URL credential at your own deployment and it's yours end to end.
ML risk scoring: server-side, optional, same backend.
Audit ingestion: server-side, or a webhook pointed wherever you want.

The n8n nodes themselves are MIT licensed. The backend is what we run as a hosted service, and you can replace it with your own. Verified integration partner on n8n.io/integrations/privent, 3,000+ npm installs, currently at v2.2.1.

What we can't solve alone

We'd rather list our actual open problems than pretend the repo is finished:

Detector coverage skews Western and major-Asian. We're missing Balkan national ID formats, Central Asian passport formats, MENA tax ID formats, and probably a dozen others we don't know we're missing because we haven't built for those regions yet. Adding a detector is genuinely one PR: kind, regex, confidence, category, and it ships to every user immediately.

Prompt injection against Detokenize is a real, undertested threat. The node is non-tool-callable and gated by Strict Mode and Trusted Sinks, but a sufficiently crafted payload instructing the agent to hit our detokenize API directly is a documented threat model we haven't stress-tested nearly enough. If you do offensive security work, this is the single highest-value place to look, and we'd genuinely rather you find the hole than someone else does.

Context-free detection has a real cost. A 16-digit number gets flagged as a possible credit card even when it's actually a hardware order ID. We're working on a context layer, but there's no clean fix yet that doesn't trade away coverage to buy back precision. If you've solved this problem elsewhere, we want to hear how.

⭐ Where to look

GitHub (source, issues, threat model doc): github.com/privent-ai/n8n-nodes-privent
npm: https://www.npmjs.com/package/n8n-nodes-privent

If you're building AI agents that touch real user data, clone the repo and try to break the Detokenize path, that's the part we most want tested by people who aren't us. Open an issue if you find something; we'll fix real ones and say so in the issue thread, publicly, including anything embarrassing.

If you find a regional ID format we're missing, the PR is smaller than you think: kind, regex, confidence, category, done.

And if none of that applies to you right now but this is still useful to know about: starring the repo is genuinely the thing that gets it in front of the next person building agent workflows before their first data leak instead of after. That's not a formality, that's the actual distribution mechanism for a project like this.

n8n nodes: MIT licensed. Backend: hosted service or self-hosted, your choice.

n8n's Guardrails node masks your PII. It doesn't give it back. Here's why that matters.

Asil Ozyildirim — Tue, 30 Jun 2026 10:34:32 +0000

n8n shipped a native Guardrails node back in November. PII detection, jailbreak protection, content filtering — all built into the platform, zero external services. It's a genuinely good default, and if you're building AI workflows in n8n and not using it yet, you should be.

But we kept hitting the same wall with it, and once you see the wall, you can't unsee it.

The wall

Here's a workflow almost everyone building support automation eventually writes:

[Webhook: support ticket] → [Guardrails: Sanitize Text] → [LLM: summarize]
                                                          → [write summary to CRM]

A ticket comes in: "Hi, I'm Sarah Chen, my account email is sarah.chen@acme.com, and I was charged twice for my subscription."

Guardrails' Sanitize Text mode catches the PII before it hits the LLM:

"Hi, I'm [NAME], my account email is [EMAIL], and I was charged twice for my subscription."

Good. The LLM never sees Sarah's real email. It summarizes: "[NAME] reports a duplicate subscription charge tied to [EMAIL]."

Now write that to the CRM.

And here's the wall: you can't. [NAME] and [EMAIL] are gone. Not encrypted, not hashed — gone. Guardrails redacts, and redaction is a one-way door. There's no way to ask "what was [EMAIL] actually?" because the node never kept a record. It just deleted the substring and moved on.

So now you're stuck writing "[NAME] reports a duplicate charge tied to [EMAIL]" into a customer record that's supposed to say Sarah Chen. You either accept a useless CRM entry, or you bypass Guardrails for this step and send the raw ticket to the LLM anyway — which defeats the entire point of having a guardrail in the first place.

Why this isn't a Guardrails bug

I want to be fair to n8n here, because this isn't a flaw in their implementation — it's the design goal. Guardrails is a content moderation primitive. Its job is: does this text violate a policy, and if so, neutralize it. Jailbreak detection, NSFW filtering, keyword blocking — for all of those, throwing the violating content away is exactly correct. You don't want the jailbreak attempt preserved anywhere.

PII just happens to share a node with those other checks, and inherits the same throw-it-away behavior. That's fine for compliance scanning ("did this text contain a credit card number, yes/no"). It's not fine the moment your workflow needs the value of that PII downstream — which, if you're doing anything beyond pure logging, is most real workflows.

The piece that was missing: reversibility

This is the exact gap we built Privent to close. Same idea as Guardrails' sanitize mode — mask sensitive values before they reach an LLM — but with one architectural difference: the mapping between a token and its original value is kept, not destroyed.

[Webhook] → [Privent: Tokenize] → [LLM: summarize] → [Privent: Detokenize] → [write to CRM]

The ticket becomes:

"Hi, I'm [NAME_001], my account email is [EMAIL_002], and I was charged twice for my subscription."

The LLM summarizes using the tokens, same as before — it still never sees Sarah Chen or her real email. But at the trusted egress point (right before you write to the CRM), Detokenize swaps the tokens back:

"Sarah Chen reports a duplicate subscription charge tied to sarah.chen@acme.com."

That's the whole difference. Mask going in, restore coming out, and the LLM is never in the loop for either direction.

"Isn't that just... storing the PII somewhere else?"

Fair question, and worth answering directly instead of hand-waving it. Yes — somewhere, a token has to map back to a real value, or detokenization is impossible by definition. The question that actually matters is where that mapping lives and who can reach it.

We built two modes specifically because the answer to that question depends on your workflow:

Tokenless mode — no API key, no account. The token↔value map lives in n8n's own workflow static data, scoped to a single execution's sessionId. Nothing leaves your n8n instance. This is the mode for "I just need tokenize→LLM→detokenize inside one workflow," which covers the support-ticket example above completely.

npm install n8n-nodes-privent
# set Authentication = Tokenless (Visitor) on the node, done

Cloud mode — for when you need the mapping to survive across multiple workflow runs, or across different workflows entirely (multi-agent handoffs, async processes where tokenize happens in one execution and detokenize happens in another, hours later). That needs a real persisted vault, which means a backend, which means an API key.

If your workflow is "one webhook in, one response out," you don't need Cloud mode. Tokenless mode covers it, and the mapping never crosses your network boundary.

Where Guardrails is still the right tool

I don't want to oversell this. If your use case is purely "block this if it contains a jailbreak attempt" or "don't let credit card numbers reach this LLM, full stop" — Guardrails is simpler, it's zero-install, and you should use it. We're not trying to replace it.

The line is pretty clean once you draw it out:

Need	Use
Block/flag content that violates a policy	Guardrails
Mask PII and never need it again	Guardrails (Sanitize mode)
Mask PII, then restore the real value downstream	Privent
Jailbreak / prompt injection detection	Guardrails
Audit trail of which agent touched which token	Privent (Cloud mode)

A lot of workflows genuinely only need the left column. Some — anything that writes an LLM-processed result back into a system of record with the subject's real identity — need the right one, and that's the gap nobody else in the n8n ecosystem is covering yet.

Try it

cd ~/.n8n
npm install n8n-nodes-privent

Tokenless mode, no signup: github.com/privent-ai/n8n-nodes-privent

If you've hit this same wall with Guardrails — or solved it some other way — I'd genuinely like to hear how. Drop it in the comments.

5 Ways to Stop Data from Leaking Out of Your n8n AI Workflows

Asil Ozyildirim — Fri, 26 Jun 2026 11:58:56 +0000

If you're running AI workflows in n8n that touch real customer data — emails, phone numbers, account IDs, health records — that data is almost certainly reaching external LLM APIs in plain text. n8n execution history stores every node's input and output by default, which means anyone with instance access can read raw PII from your logs.

This post covers five concrete approaches, from zero-dependency quick fixes to production-grade solutions, with real tools, install instructions, and honest tradeoffs for each.

Why this matters before we start

A typical n8n AI workflow looks like this:

Webhook → Pull customer record → Build prompt → OpenAI → Send response

By the time that prompt hits OpenAI, it might contain:

Summarize the support case for john.doe@company.com,
SSN 999-88-7777, account #48291, phone 555-304-8821.
Issue: {{ $json.description }}

Every field there is PII. It's going to OpenAI's infrastructure. It's sitting in your n8n execution logs. And unless you've taken specific steps to prevent it, it will keep doing that silently.

Option 1: Manual tokenization with a Code node

What it is: Write JavaScript in an n8n Code node to replace sensitive fields with tokens before the LLM node, then reverse it afterward.

Setup: No installation needed. Add a Code node before your LLM node.

// "Tokenize" Code node — Run Once for All Items
const map = {};
let counter = 1;

function token(value, kind) {
  const t = `[${kind}_${String(counter++).padStart(3, '0')}]`;
  map[t] = value;
  return t;
}

const input = $input.first().json;

return [{
  json: {
    safe_prompt: `Summarize the case for ${token(input.email, 'EMAIL')},
      account ${token(input.account_id, 'ACCT')},
      phone ${token(input.phone, 'PHONE')}.
      Issue: ${input.description}`,
    _pii_map: map
  }
}];

Then after your LLM node, a second Code node to restore values:

// "Detokenize" Code node
let response = $input.first().json.message.content;
const map = $('Tokenize').first().json._pii_map;

for (const [token, value] of Object.entries(map)) {
  response = response.replaceAll(token, value);
}

return [{ json: { response } }];

What it actually covers:

Fields you explicitly list in the code
PII in the final prompt string

What it misses:

Anything you forget to include — every new workflow needs this written again from scratch
No detection of implicit sensitive content (proprietary project names, M&A context, etc.)
_pii_map still appears in execution logs if you're not careful
No audit trail per execution
Breaks immediately if your data schema changes

Best for: Prototyping. One-off workflows where you know exactly which 2–3 fields carry PII and you won't forget to update the code when the schema changes.

Option 2: n8n's built-in Guardrails node

What it is: A native n8n node (available since v1.113.3, November 2025) that sits between your data and your LLM node. No external services required for pattern-based checks.

Setup: Update n8n to at least v1.113.3. The Guardrails node appears in the node search — no installation needed.

The node has two modes:

Check Text for Violations — scans text against selected policies and routes to a Fail branch if anything triggers. You then decide what to do: halt the workflow, log the attempt, return a safe fallback.

Sanitize Text — redacts detected content in-place and replaces it with placeholders like [EMAIL_ADDRESS] or [PHONE_NUMBER]. The workflow keeps running with the cleaned text.

A typical pattern:

Webhook → Guardrails (Sanitize) → OpenAI → Response

Available guardrails include: PII detection (20+ entity types: emails, phones, credit cards, SSNs, IBANs, passports, driver's licenses, medical licenses, and country-specific formats), Secret Keys, Keywords, URLs, Custom Regex, Jailbreak detection (LLM-based), NSFW detection (LLM-based), and Topical Alignment.

For PII specifically, the Sanitize mode catches structured entities via pattern matching — no API call required, no latency added. Jailbreak and NSFW detection require a connected LLM node and add one API call per check.

What it actually covers:

Structured PII in a single text field you configure
API key patterns, common credential formats
Jailbreak/injection attempts on user-facing inputs
No external service dependency for pattern-based checks

What it misses:

No detokenization — once redacted, the value is gone. The response back to the user will contain [EMAIL_ADDRESS], not the original. For workflows where you need the real value restored after the LLM call, you'll need additional logic.
No cross-node visibility — it sees the text field you point it at, not what accumulated across multiple upstream nodes
No audit trail in an external system
One documented limitation: consistent detokenization (same entity → same token across a long session) requires extra workflow logic; the node doesn't handle this automatically

Best for: Adding a first layer of protection to user-facing chatbots and intake workflows. Excellent for blocking jailbreaks and catching structured PII on input. Not enough on its own if your workflow composes prompts from data pulled across multiple nodes.

Option 3: n8n-nodes-rehydra (community node)

What it is: An open-source n8n community node (GitHub, npm) built on the Rehydra SDK. Handles both anonymization and rehydration — meaning it can restore original values after the LLM responds.

Setup:

Self-hosted n8n → Settings → Community Nodes → Install → enter n8n-nodes-rehydra.

Or via CLI:

npm install n8n-nodes-rehydra

Three nodes in the package:

Rehydra: Anonymize — replaces detected PII with XML-style tags: <PII type="EMAIL" id="1"/>. Supports Pseudonymize mode (reversible, default) and Anonymize mode (irreversible, for when you never need the value back). Outputs: anonymizedText, piiMap (encrypted), entities.

Rehydra: Rehydrate — takes the piiMap from a prior Anonymize step and restores original values. Requires the same encryption key.

Rehydra: Inspect — dry run mode. Returns detected entities without modifying the text. Useful for testing what would be caught before going to production.

Configuration options:

NER Mode: Disabled (regex only, fast), Quantized (~280 MB ONNX model, auto-downloaded on first run), or Standard (~1.1 GB model). The ONNX model runs locally — no API calls, works offline, PII never leaves your machine.
PII Types: Email, phone, IBAN, names, organizations, and more — select which to detect.
Locale: affects detection patterns for country-specific formats.

A typical workflow:

Database → Rehydra: Anonymize → Claude → Rehydra: Rehydrate → Save result

What it actually covers:

Structured PII + soft PII (names, organizations) when NER mode is enabled
Reversible pseudonymization — the LLM works with stable placeholders, the response gets real values restored
Fully local when using NER mode — no external API calls for detection
Works on any text field you configure

What it misses:

NER mode requires a ~280 MB model download on first execution (quantized) or ~1.1 GB (standard) — adds startup latency to first run
No cross-node data movement visibility — you configure which field it processes
No audit trail or dashboard
Unverified community node — requires self-hosted n8n with N8N_COMMUNITY_PACKAGES_ENABLED=true; not available on n8n Cloud

Best for: Self-hosted teams who need reversible PII masking with no external service dependency, and specifically need name/organization detection beyond regex-only approaches.

Option 4: Microsoft Presidio via HTTP Request node

What it is: An open-source PII detection and anonymization engine from Microsoft, designed for production use. You deploy it as a local service and call it from n8n via HTTP Request nodes.

Setup:

Deploy with Docker:

docker pull mcr.microsoft.com/presidio-analyzer
docker pull mcr.microsoft.com/presidio-anonymizer

docker run -d -p 5001:3000 mcr.microsoft.com/presidio-analyzer
docker run -d -p 5002:3000 mcr.microsoft.com/presidio-anonymizer

In n8n, add an HTTP Request node:

POST http://localhost:5001/analyze
Body: {
  "text": "{{ $json.prompt }}",
  "language": "en"
}

This returns detected entities with positions. Send those to the anonymizer:

POST http://localhost:5002/anonymize
Body: {
  "text": "{{ $json.prompt }}",
  "analyzer_results": "{{ $json.analyzerResults }}"
}

Presidio supports 50+ entity types, custom recognizers, and multiple anonymization operators (replace, redact, hash, encrypt, mask). It's the basis for many enterprise PII pipelines and supports English and a growing list of other languages.

What it actually covers:

Broad entity detection (50+ types out of the box)
Custom recognizers for domain-specific entities
Multiple anonymization strategies per entity type
Fully local — nothing leaves your infrastructure
Encryption-based anonymization for reversible workflows

What it misses:

Requires running and maintaining a separate Docker service
No native n8n node — you're wiring HTTP Request nodes together, which means more workflow complexity and error handling to build yourself
Sees only the text field you send it — no visibility into cross-node data flow
No n8n-specific audit trail
You're responsible for the anonymization/deanonymization map storage if you need reversibility

Best for: Teams with existing DevOps capacity who want maximum control over entity detection, custom recognizers for industry-specific PII, and no dependency on third-party SaaS.

Option 5: n8n-nodes-privent (community node)

What it is: A native n8n community package (npm, privent.ai) that runs inside your workflow graph — not as an external proxy. 2,000+ installs on npm.

The architectural difference from everything above: Privent nodes read node input/output JSON and cross-node data movement directly, the same way any other n8n node does. It sees what accumulated across your entire workflow before the prompt is composed, not just the final text field you point at.

Setup:

Self-hosted:

N8N_COMMUNITY_PACKAGES_ENABLED=true

Then Settings → Community Nodes → Install → n8n-nodes-privent.

n8n Cloud Pro/Enterprise: same UI path — no environment variable needed.

Create a Privent API credential with your pv_live_… key (vault backend is configured automatically based on your deployment type).

Six nodes in the package:

Privent Session — generates a sessionId and prewarms the in-memory vault. Keeps token mappings consistent when the same value appears across multiple nodes in one session.

Privent Tokenize — replaces detected sensitive data with deterministic [KIND_NNN] placeholders. Detects 10 categories: EMAIL, SSN, CREDIT_CARD, IBAN, AWS_KEY, JWT, API_KEY, and more. The detection engine (ACARS) evaluates six weighted signals simultaneously — entity sensitivity, semantic risk, contextual amplification, destination risk, behavioral velocity, and policy overrides — rather than pattern-matching alone.

Privent Detokenize — resolves placeholders back to real values, but only at sinks you declare as trusted. With strict: true, it hashes the downstream sink URL and checks it against your trustedSinks prefix list. An HTTP node targeting an unknown endpoint keeps the placeholder — the cleartext value stays in the vault regardless of what downstream logic does.

Privent Risk Check — scores the prompt before it reaches the model, with the full ACARS breakdown per execution.

Privent Handoff — emits agent_handoff audit events when one agent delegates to another. Flags unauthorized scope expansions.

Privent Audit Event — emits custom observability events into the Privent dashboard.

A typical workflow:

Webhook → [your nodes] → Session → Tokenize → OpenAI → Detokenize → Response

The workflow JSON:

{
  "nodes": [
    { "name": "Webhook", "type": "n8n-nodes-base.webhook" },
    { "name": "Session", "type": "n8n-nodes-privent.priventSession" },
    {
      "name": "Tokenize",
      "type": "n8n-nodes-privent.priventTokenize",
      "parameters": {
        "sessionId": "={{ $('Session').item.json.sessionId }}",
        "textField": "prompt"
      }
    },
    { "name": "OpenAI", "type": "n8n-nodes-base.openAi" },
    {
      "name": "Detokenize",
      "type": "n8n-nodes-privent.priventDetokenize",
      "parameters": {
        "strict": true,
        "trustedSinks": "https://internal.yourcompany.com"
      }
    }
  ]
}

What it actually covers:

Dynamic runtime PII + structured entities + implicit sensitive content (semantic risk scoring catches proprietary context, M&A language, etc.)
Cross-node data movement — because it runs inside the graph, it sees data as it flows between nodes, not just at the egress point
Egress gating at the vault level — strict mode prevents cleartext from reaching untrusted destinations even if downstream workflow logic tries to send it
Consistent token mapping across a session
Full audit trail per execution in the Privent dashboard
Credential and API key detection (AWS_KEY, JWT, API_KEY)
Multi-agent delegation auditing via the Handoff node

What it requires:

n8n Cloud Pro or Enterprise for n8n Cloud usage; self-hosted with community packages enabled for everything else
A Privent account and API key
Privent processes data ephemerally — raw prompt text is never written to disk, never stored, never used for training

Deployment options: Privent Cloud (managed, API key), Dedicated (isolated environment), or fully on-prem (detection engine, rules, and AI models all run inside your network — nothing leaves).

Best for: Production workflows handling real customer data across multiple nodes, multi-agent architectures where data moves between agents, healthcare (HIPAA) and financial (GDPR, CCPA) environments, or any setup where you need to know exactly what left your infrastructure and where it went.

Comparison

	Code Node	n8n Guardrails	Rehydra	Presidio	Privent
Installation	None	None	Community node	Docker service	Community node
Detokenization	Manual	❌ redact-only	✅	✅ (custom)	✅
Detects names/orgs	❌	⚠️ limited	✅ (NER mode)	✅	✅
Implicit/semantic PII	❌	❌	❌	❌	✅
Cross-node visibility	❌	❌	❌	❌	✅
Egress gating	❌	❌	❌	❌	✅
Audit trail	❌	❌	❌	❌	✅
Works on n8n Cloud	✅	✅	❌	❌	✅ (Pro+)
External service req.	❌	❌	❌	Docker	API key
On-prem option	✅	✅	✅	✅	✅

How to choose

Start with n8n Guardrails if you're on n8n Cloud or want zero configuration overhead and your main concern is protecting user-submitted input on a chatbot or intake form. It's already there, costs nothing to set up, and catches the most common cases.

Add Rehydra if you need reversible anonymization on self-hosted n8n and can't send detection to an external service. The local NER model handles names and organizations that regex-only approaches miss.

Use Presidio if you have DevOps capacity, need 50+ entity types or custom recognizers for industry-specific PII, and want maximum control over anonymization strategy.

Use Privent if your workflow composes prompts from data pulled across multiple nodes, you're running multi-agent flows, or you need an audit trail that shows you exactly what left your infrastructure. The graph-state visibility gap is real — other approaches protect the field you point them at; Privent watches the entire execution.

What approach are you using in production? Curious especially about edge cases with multi-node workflows — drop it in the comments.