Privacy-Protected Agentic AI Architecture
Agents

Sanitizing PII in LLM Observability Traces: LangSmith, Langfuse & OpenTelemetry

Sanitizing PII in LLM Observability Traces: Prevent secondary data breaches by automatically redacting PII, secrets, and database credentials from LLM observability traces, LangSmith dashboards, and Langfuse logs. Includes Flat-rate TEAMS pricing and Zero-server architecture.

100% Local Processing ✈ Airplane Mode Verified⊘ No Server Logs

AI Summary / Key Takeaways

Verified Zero-Trust Logic

"PrivacyScrubber provides the essential de-identification layer for Agents professionals using generative AI. By sanitizing sensitive identifiers locally, we ensure absolute data sovereignty without sacrificing the power of LLM reasoning."

Paste real Agents data into ChatGPT — only scrubbed tokens reach the model. Names, IDs, and emails stay on your machine.
Works offline: disconnect the network mid-session and it keeps running. Zero cloud dependency.
Your AI gets full context. Your clients' real identities never leave your browser tab.

Enterprise-Grade AI Privacy

Add custom redaction rules and priority support with PRO.

GO PRO
Live Simulation

Zero-Trust Data Sanitization

Watch PrivacyScrubber's local engine transform sensitive Agents data instantly in your browser, without any API calls.

Automated Detection Classes:
USER_IDAGENT_MEMORYRAG_CHUNKCONTEXT_PIISESSION_TOKEN
100% Client-Side Execution
Wasm_Engine
AGENT CONTEXT > user_id=user_8xKmN2 | session=sess_T7vZ1pQ RAG chunk: "Client Aisha Okonkwo (acct #00412) called re: invoice INV-2026-0332 for $4,500"
AGENT CONTEXT > user_id=[ID_1] | session=[ID_2] RAG chunk: "Client [NAME_1] (acct [ID_3]) called re: invoice [ID_4] for [VALUE_1]"
Click any token above to test False Positive reveal

AI Risk Calculator

50
Risk● Critical
Leaks/yr
9,000
Max Fine
€20M

Get Your Risk Estimate

Provide company details to generate your personalized Shadow AI risk estimate.

The Zero-Trust Imperative: Stop leaking sensitive client data to public LLMs and protect your organizational privacy. PrivacyScrubber ensures you can leverage GenAI safely by neutralizing risks 100% offline in your browser.

What AI Engineers and Agent Builders Send to AI — and What They Should Be Sending Instead

Protecting workflows for Sanitizing PII in LLM Observability Traces is a major technical objective for modern organizations. Utilizing platforms like LangChain, LlamaIndex, AutoGPT, CrewAI, and custom RAG infrastructure without input filtering creates immediate liabilities regarding proprietary records. Our agents AI privacy guides outlines critical defense strategies to secure the agents boundary, resolving autonomous agents that accumulate PII across memory, tool calls, and vector store indexes — creating persistent privacy liabilities impossible to manually audit before any external API receives the prompt.

Submitting business records or pasting internal roadmaps, API keys, or financial metrics into cloud AI systems can lead to NDA violations. Standard security toggles cannot identify contextual PII or ensure SOC 2 logging compliance. For AI engineers, LLM application developers, and enterprise AI architects, raw prompt inputs represent the primary leak vector. Prevent secondary data breaches by automatically redacting PII, secrets, and database credentials from LLM observability traces, LangSmith dashboards, and Langfuse logs. Includes Flat-rate TEAMS pricing and Zero-server architecture.

Privacy Insight: Engineering teams investing in AI observability often create secondary compliance liabilities by streaming unmasked user prompts and system logs to third-party SaaS dashboards. Pre-trace sanitization guarantees SOC 2 and HIPAA compliance.

Through Zero-Trust Data Sanitization, PrivacyScrubber secures prompt entry points locally via our Secure Workspace and the PrivacyScrubber Chrome Extension.

How to Use AI on Real Agents Data — Without Sending a Single Real Name

Through Zero-Trust Data Sanitization, PrivacyScrubber secures prompt entry points locally via our Secure Workspace and the PrivacyScrubber Chrome Extension. The system tokenizes customer and business identifiers (such as [ID_1]) before exfiltration, aligning with standard procedures for scaling agent architectures. The Chrome Extension inserts a secure shield button inside ChatGPT, Claude, and Gemini to automate prompt redaction and in-place restoration. Processing data through browser-based Named Entity Recognition allows safe integration of LangChain, LlamaIndex, AutoGPT, CrewAI, and custom RAG infrastructure for complex tasks while preserving client privacy.

This client-side execution model is verifiable via the Airplane Mode Standard. Turn off your network interface, run a sanitization cycle, and confirm that all processing is completed locally. This aligns with agentic data loss prevention, proving that no database or server logs receive unmasked data.

Why AI Safety and Security Teams Flag Unmasked AI Prompts

Compliance auditors look for explicit safeguards: GDPR data minimization principles, NIST AI RMF (Risk Management Framework), and emerging agentic AI governance guidance. However, shadow AI usage often bypasses static network tools. Implementing the protocols in claude desktop local pii sanitization helps organizations build a secure, compliant workflow that satisfies audit requirements. Verifiable security means stripping identifiers offline. Establishing local technical controls represents the only path to satisfy these criteria without adding server-side processing overhead.

Deploy Zero-Trust DLP for Developer Fleets

Protecting code logs or system stack traces from leaking to public models? With PrivacyScrubber TEAMS, security teams can distribute custom regex rules globally via Chrome MDM policies. Protect proprietary API keys, database URLs, and UUIDs across your entire developer fleet without centralizing user telemetry.

Zero-Trust Configuration & Threat Model

Establishing a secure runtime boundary for generative AI workflows is key to compliance. PrivacyScrubber accomplishes this by processing all unstructured strings directly inside browser memory. The engine's local regex patterns parse prompts in real time and swap them with secure identifiers before transmission. This offline tokenization scheme ensures that third-party LLMs cannot reconstruct the original identities from raw conversation logs.

Verification Protocol

  • Analyze input patterns to detect personal and proprietary entities in real time.
  • Apply local Named Entity Recognition to tokenize primary identifiers.
  • Map sensitive strings to deterministic, tab-isolated volatile variables.
  • Verify Zero-Server transmission by testing the workflow in Airplane Mode.

Parser Specifications

Encryption AlgorithmXChaCha20-Poly1305 (Argon2id)
Detection MethodContext-Aware Regex + NER (99.5% Accuracy)
Data Egress RuleZero-Server Egress (Airplane Mode Verifiable)
Classification StandardMaximum Privacy Guard
Associated Threat LevelLow (Inference Risk)

The AI Observability Compliance Blindspot

To monitor model latency, token costs, and hallucinations, modern engineering teams stream complete application logs to AI observability platforms (LangSmith, Langfuse, Arize Phoenix, Honeycomb, OpenTelemetry). However, this creates a major compliance loophole: raw customer conversations, patient symptoms, and database credentials captured in prompt traces are stored in third-party SaaS dashboards, creating direct audit violations under Agentic AI Data Leak Prevention Standards.

The Solution: In-Memory Trace Sanitization

By integrating sanitize() from the PrivacyScrubber AI Agents Ecosystem into your observability pipeline, trace spans and prompt logs are scrubbed in volatile memory before they are dispatched over the network.

Langfuse / OpenTelemetry Trace Masking Hooknpm i @privacyscrubber/sdk langfuse
import { sanitize } from '@privacyscrubber/sdk';
import { Langfuse } from 'langfuse'; const langfuse = new Langfuse({ publicKey: process.env.LANGFUSE_PUBLIC_KEY, secretKey: process.env.LANGFUSE_SECRET_KEY
}); // Custom secure trace exporter hook
export function logSecureGeneration(traceId, name, rawPrompt, rawCompletion) { // 1. Sanitize prompt and completion in RAM before logging const { scrubbedText: cleanPrompt } = sanitize(rawPrompt, { profile: 'Security', // Healthcare, Legal, DevOps, Finance detectSecrets: true // Strips AWS keys, JWTs, DB passwords }); const { scrubbedText: cleanCompletion } = sanitize(rawCompletion, { profile: 'Security', detectSecrets: true }); // 2. Dispatch sanitized telemetry to observability platform const trace = langfuse.trace({ id: traceId, name }); trace.generation({ name: 'model-completion-step', input: cleanPrompt, // Zero raw PII in Langfuse dashboard output: cleanCompletion, model: 'gpt-4o' });
}

Enterprise Data Governance & DevOps Log Scrubbing

Securing telemetry traces is a critical prerequisite for passing enterprise security audits. Combine trace sanitization with our guides on Cleaning Production Logs for AI Debugging and enterprise data controls in our AI Data Governance Blueprint.

Instant Simulation

Sanitizing PII in LLM Observability Traces Sanitizer

Watch our zero-trust engine neutralize sensitive identifiers 100% locally. No data ever leaves your device.

Local processing 0 Server logs
ZTDS_ENGINE_V1.5.0
PROMPT INPUT > Review application access logs for user Richard Branson (richard@branson.co.uk), phone number: 555-0111.
PROMPT INPUT > Review application access logs for user [NAME_1] ([EMAIL_1]), phone number: [PHONE_1].

Agents Detection Profile

Our zero-trust engine is pre-hardened for Agents workflows, automatically identifying and tokenizing the following parameters 100% locally.

USER_ID
Active Protection
AGENT_MEMORY
Active Protection
RAG_CHUNK
Active Protection
CONTEXT_PII
Active Protection
SESSION_TOKEN
Active Protection

Zero-Trust Architecture

PrivacyScrubber operates entirely on your device. Unlike other platforms, our local PII masking engine never transmits your sensitive prompts or documents to external servers. All detection and restoration happens in your computer's local RAM.

  • No Backend Connection: Zero API calls, zero tracking, zero logs.
  • Temporary Memory: Your data exists only for the duration of your tab's life.
  • Verification Ready: Built for professionals who need to audit their security layer with agentic data loss prevention.

Hardware-Level Verification

We encourage you to audit our zero-trust claims directly in your browser using the Airplane Mode Test:

1

Open your browser's Network Monitor before you start scrubbing.

2

Switch to Airplane Mode (physical or simulated) and protect your text.

3

Verify that no data packets ever leave your machine.

LLM Code Assistants & Database Agents Integration

Step-by-Step Integration Guide: Sanitizing PII in LLM Observability Traces

PrivacyScrubber operates entirely client-side. Whether using the copy-paste dashboard, the browser extension, or the MCP Server, your sensitive records stay on your local device. Follow these instructions to safely use LLM Code Assistants & Database Agents:

1 Method A: Syntax-Preserving Web Workspace

For JSON payloads, SQL dumps, YAML manifests, and server logs:

  1. Paste your raw JSON, SQL export, or Syslog/Nginx trace into the PrivacyScrubber dashboard.
  2. Click Protect PII: sensitive values, IPs, and tokens are replaced while preserving quotes, commas, and schema syntax via Custom Rules Engine.
  3. Copy the sanitized code and safely query AI for debugging, query optimization, or log analysis.
  4. Reveal the AI's generated patch or SQL query locally using Reveal Originals.

2 Method B: Chrome Extension & MCP Server

For automated prompt masking & IDE agents (Cursor / Cline / Claude Desktop):

  1. Use the PrivacyScrubber Extension to scrub code directly in AI web chats.
  2. Or connect the PrivacyScrubber MCP Server to Cursor, Cline, or Claude Code for agentic workflows.
  3. Credentials and hostnames are intercepted locally in RAM before leaving your workstation.
  4. Debug architectures without leaking production connection strings or API secrets.

Local Redaction & Risk Matrix for Agents

Detection EntityToken PlaceholderRisk LevelSecurity Action
USER_ID Details[USER_ID]Medium (PII Exposure)Deterministic local swap
AGENT_MEMORY Details[AGENT_MEMORY]Medium (PII Exposure)Deterministic local swap
RAG_CHUNK Details[RAG_CHUNK]Medium (PII Exposure)Deterministic local swap
CONTEXT_PII Details[CONTEXT_PII]Medium (PII Exposure)Deterministic local swap
SESSION_TOKEN Details[SESSION_TOKEN]Medium (PII Exposure)Deterministic local swap

Ready-to-Use AI Prompt Template

Role: Database Administrator / API Security Lead · Target: LLM Code Assistants & Database Agents
Syntax-Preserving JSON & SQL Sanitization (Zero Schema Drift)Token-Preserving Protocol
Act as a senior database administrator. Analyze the following sanitized JSON payload and SQL schema export for [DB_RECORD_1]:
1. Review the data structure for query optimization and indexing efficiency.
2. Generate refactored SQL queries with optimized JOIN operations.
3. Ensure output adheres strictly to standard schema syntax. CRITICAL COMPLIANCE INSTRUCTION: Preserve all token placeholders ([DB_RECORD_1], [API_KEY_1], [IP_ADDRESS_1]) exactly as formatted.
Statutory Defense: ISO/IEC 27001:2022 Control A.8.11 (Data Masking) & GDPR Art. 32Payload formatting, JSON keys, SQL tables, and database constraints remain syntactically identical while all record-level PII is converted to deterministic tokens.

Agents Adoption Use Cases

Principal Cloud Security ArchitectSECRET PROTECTION
Zero-Trust Verified
Prevents accidental leaks of AWS keys, JWTs, database connection strings, and private GitHub tokens into public LLM training datasets.
VP of Infrastructure & DevOpsDEVOPS & SRE
Zero-Trust Verified
Sanitizes stack traces, internal IP ranges, and Kubernetes cluster configs in developer terminal clipboards prior to debugging with AI assistants.
Head of Application Security (AppSec)APP SECURITY
Zero-Trust Verified
Enforces automated local redaction of production API keys and customer payloads in developer browser extensions.
Lead Software ArchitectSYSTEM ARCHITECTURE
Zero-Trust Verified
Masks proprietary algorithm logic and confidential code comments before querying generative code assistants.

Scrub it before it reaches the AI — right from your toolbar

The free PrivacyScrubber Chrome Extension replaces names, emails, and IDs with safe tokens directly inside ChatGPT, Claude, and Gemini — before you hit send. Nothing leaves your browser.

Zero-Trust Data Sanitization (ZTDS) — Verified Architecture

Independently auditable facts for Agents compliance teams

Data transmission
0 bytes sent to any server
Processing location
100% browser RAM (volatile memory)
Session map persistence
Destroyed on tab close — never written to disk
Key derivation
Argon2id (memory-hard, server-independent)
Encryption cipher
XChaCha20-Poly1305 (authenticated encryption)
Offline verification
Airplane Mode Standard — full function without network
BAA / DPA required
No — zero PHI/PII reaches PrivacyScrubber servers
Audit method
Chrome DevTools → Network tab — zero outbound requests

How to audit: Open PrivacyScrubber, enable Airplane Mode, paste any agents text, click Protect PII. Open Chrome DevTools → Network tab. Zero outbound requests will confirm 100% local execution. The session token map ([NAME_1], [EMAIL_1]…) lives only in browser tab memory and is permanently destroyed when the tab is closed.

COMPLIANCE FAQ

Frequently Asked Questions

Common questions about deploying zero-trust AI for Agents Teams.

Why do LLM observability tools like LangSmith and Langfuse create compliance risks?
Observability platforms capture full trace payloads—including raw prompt templates, system prompts, retrieval context, user inputs, and model responses—to enable performance debugging. If users paste personal health records, credit card numbers, or AWS secrets into prompts, those raw secrets are streamed to third-party observability servers, creating secondary data breaches under GDPR and SOC 2.
How do I sanitize traces without breaking latency and token usage metrics?
PrivacyScrubber SDK tokenizes payload text in-memory before exporting trace spans. The sanitized text maintains identical token length approximations and structure, ensuring telemetry metrics (latency, token counts, error rates) remain 100% accurate in dashboards while all sensitive entity strings are replaced with tokens.
Can PrivacyScrubber automatically redact infrastructure secrets in traces?
Yes. When detectSecrets: true is configured, the engine scans all span attributes, prompt variables, and tool outputs for AWS Access Keys (AKIA...), GitHub tokens (ghp_...), JWT Bearer tokens, and database connection strings (postgres://, mongodb://), masking them before trace export.
Does this integrate with standard OpenTelemetry (OTel) collectors?
Yes. PrivacyScrubber can be wrapped inside custom OpenTelemetry SpanProcessors or exporter middleware, automatically scrubbing span attributes and event logs before they are dispatched to Datadog, Dynatrace, New Relic, Honeycomb, or self-hosted OTel collectors.
Does protecting data with PrivacyScrubber before AI processing satisfy GDPR data minimization principles?
Yes. Processing pseudonymized data for a secondary purpose (AI analysis or drafting) aligns with GDPR data minimization principles because no personally identifiable data is transmitted to the AI provider. The session map that maps tokens back to real values never leaves your browser.
What specific PII does PrivacyScrubber detect for agents workflows?
The engine detects names, email addresses, phone numbers (US and international formats), Social Security Numbers, EINs, credit card numbers, and custom identifiers. PRO users can add custom regex rules to match agents-specific patterns such as proprietary account IDs, MRNs, or internal project codes.
Can I reverse the redaction if I use PrivacyScrubber to mask agents data?
Yes. If you copy the AI's response and paste it back into PrivacyScrubber, it automatically maps the tokens (like [NAME_1] or [ID_1]) back to the original values using the ephemeral session map stored in your browser's memory.
Can PrivacyScrubber be used 100% offline without network requests?
Yes. All processing runs in your browser's local JavaScript engine, with no external server calls. Once the page loads, you can enable Airplane Mode and verify in Chrome DevTools (Network tab) that zero outbound requests occur. All cryptographic operations (including client-side pseudonymization and reverse-revealing) utilize hardware-accelerated XChaCha20-Poly1305 encryption and Argon2id key derivation running entirely inside browser RAM, ensuring your agents data stays 100% on your device.
How can I verify that PrivacyScrubber sends zero data to servers?
Use the 5-step Airplane Mode audit: (1) Open PrivacyScrubber in your browser. (2) Disconnect your network connection (enable Airplane Mode). (3) Paste a text sample containing names, emails, and phone numbers. (4) Click "Protect PII" — all tokens are generated instantly in local browser RAM. (5) Open Chrome DevTools → Network tab and confirm zero outbound requests were made. This test works because PrivacyScrubber uses a Wasm-based regex engine that runs 100% client-side. The session token map (e.g. [NAME_1] → "John Doe") exists only in browser tab memory and is destroyed when the tab is closed.
Do I need a HIPAA Business Associate Agreement (BAA) or GDPR Data Processing Agreement (DPA) with PrivacyScrubber?
No. PrivacyScrubber is designed to run entirely on the client side, meaning no Protected Health Information (PHI) or personally identifiable data is ever transmitted to our infrastructure. Since your data is not processed or stored on our servers, PrivacyScrubber is not acting as a HIPAA Business Associate or a GDPR Data Processor. Consequently, organizations typically determine that standard Business Associate Agreements (BAAs) or Data Processing Agreements (DPAs) are not applicable to PrivacyScrubber. However, you should consult with your compliance officer or legal counsel to verify compliance requirements for your specific workflows.
Can I customize detection rules for industry-specific data formats?
Yes. In the PRO edition of PrivacyScrubber, you can configure custom regular expression (regex) rules designed to target unique patterns associated with your sector and internal taxonomy. This allows you to extend the standard Named Entity Recognition (NER) model to cover proprietary account formats, internal project identifiers, or custom data attributes while keeping all execution client-side.
Is pasting sensitive data into ChatGPT safe?
Pasting sensitive data directly into ChatGPT can expose it to OpenAI's servers and model training unless you use zero-trust client-side scrubbing like PrivacyScrubber, which tokenizes data before it leaves your browser. Protect your workflows for $15/mo with PRO.
How does client-side PII redaction work?
Client-side PII redaction executes directly in your browser's RAM, intercepting and masking sensitive identifiers before they are transmitted over the internet, ensuring true zero-trust security.
How does the Secure Workspace differ from the Browser Extension?
The Secure Workspace allows bulk offline file processing (PDFs, DOCX) and team handoffs, while the Browser Extension injects native masking directly into ChatGPT or Claude's UI. Both are included in our zero-trust ecosystem.
What is the PII MCP Server used for?
The local Model Context Protocol (MCP) Server allows developers to automate PII sanitization in CI/CD pipelines, agentic workflows, and IDEs like Cursor—all executing 100% locally.
What AI Engineers and Agent Builders Send to AI — and What They Should Be Sending Instead
How to Use AI on Real Agents Data — Without Sending a Single Real Name
Through Zero-Trust Data Sanitization, PrivacyScrubber secures prompt entry points locally via our Secure Workspace and the PrivacyScrubber Chrome Extension. The system tokenizes customer and business identifiers (such as [ID_1]) before exfiltration, aligning with standard procedures for scaling agent architectures. The Chrome Extension inserts a secure shield button inside ChatGPT, Claude, and Gemini to automate prompt redaction and in-place restoration. Processing data through browser-based Named Entity Recognition allows safe integration of LangChain, LlamaIndex, AutoGPT, CrewAI, and custom RAG infrastructure for complex tasks while preserving client privacy.
Why AI Safety and Security Teams Flag Unmasked AI Prompts
Compliance auditors look for explicit safeguards: GDPR data minimization principles, NIST AI RMF (Risk Management Framework), and emerging agentic AI governance guidance. However, shadow AI usage often bypasses static network tools. Implementing the protocols in claude desktop local pii sanitization helps organizations build a secure, compliant workflow that satisfies audit requirements. Verifiable security means stripping identifiers offline. Establishing local technical controls represents the only path to satisfy these criteria without adding server-side processing overhead.
Is PrivacyScrubber safe for mask PII in LangSmith traces, Langfuse PII redaction, sanitize LLM telemetry traces, OpenTelemetry AI privacy masking, LLM prompt logging compliance?
Yes, absolutely. PrivacyScrubber operates on a 100% Zero-Trust Data Sanitization (ZTDS) architecture, meaning all redaction happens locally within your browser. When working with mask PII in LangSmith traces, Langfuse PII redaction, sanitize LLM telemetry traces, OpenTelemetry AI privacy masking, LLM prompt logging compliance, no sensitive data ever leaves your device or touches a cloud server.
How does it handle custom data structures for agents?
Our engine includes 22+ built-in industry profiles optimized for agents data. Furthermore, our Flat-rate TEAMS tier allows you to define unlimited custom Regular Expressions that process data securely in offline memory.
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