Data Masking

Masking sensitive data is crucial for compliance (GDPR, HIPAA, PCI DSS) and user privacy when using LLM observability. Langfuse provides two complementary approaches to data masking:

For maximum security, consider using both approaches together.

Client-Side Masking

Client-side masking allows you to redact sensitive information directly in your application before data is sent to Langfuse. This ensures sensitive data never leaves your application.

Key benefits:

• Data is masked before transmission—sensitive information never reaches Langfuse
• Configured per SDK instance by application developers
• No additional infrastructure required

For comprehensive documentation including code examples, advanced patterns, and integration guides, see the Client-Side Masking documentation.

Server-Side Ingestion Masking (EE)

Server-side ingestion masking allows self-hosted Langfuse administrators to define custom callback logic for masking or redacting sensitive data from tracing events as they are ingested. This feature operates at the Langfuse Web container level, providing centralized data masking across all clients.

Key benefits:

• Single point of configuration for all tracing data
• Platform administrator control
• Safety net for data that bypasses client-side masking

How It Works

1. When a tracing event hits the Langfuse Web container, it checks if a masking callback URL is configured.
2. If configured, Langfuse sends the OpenTelemetry trace object to your callback endpoint via HTTP POST.
3. Your callback service processes the data and returns the masked object.
4. Langfuse continues processing with the masked data.

Configuration

Configure the masking callback using the following environment variables on the Langfuse Web container:

Callback Interface

Request

Langfuse sends a POST request to your callback URL with:

Headers:

Body:

The request body contains the OpenTelemetry trace object in JSON format. This is the raw tracing data that would be stored in Langfuse.

Response

Your callback must return:

• HTTP Status: 200 OK for successful masking
• Body: The masked OpenTelemetry object in the exact same schema as the input

Error Handling

Error handling behavior is configured via LANGFUSE_INGESTION_MASKING_CALLBACK_FAIL_CLOSED:

Example Implementation

Here’s an example masking callback service in Python using FastAPI:

from fastapi import FastAPI, Request, Header
from typing import Optional
import re
 
app = FastAPI()
 
def mask_pii(data):
    """Recursively mask PII in the data structure."""
    if isinstance(data, str):
        # Mask email addresses
        data = re.sub(r'\b[\w.-]+?@\w+?\.\w+?\b', '[REDACTED_EMAIL]', data)
        # Mask phone numbers
        data = re.sub(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b', '[REDACTED_PHONE]', data)
        # Mask credit card numbers
        data = re.sub(r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b', '[REDACTED_CC]', data)
        return data
    elif isinstance(data, dict):
        return {k: mask_pii(v) for k, v in data.items()}
    elif isinstance(data, list):
        return [mask_pii(item) for item in data]
    return data
 
@app.post("/mask")
async def mask_trace(
    request: Request,
    x_langfuse_org_id: Optional[str] = Header(None),
    x_langfuse_project_id: Optional[str] = Header(None)
):
    """
    Masking callback endpoint for Langfuse ingestion.
 
    Receives OpenTelemetry trace objects and returns masked versions.
    """
    body = await request.json()
 
    # Apply masking logic
    masked_body = mask_pii(body)
 
    # Optionally, apply different rules based on org/project
    # if x_langfuse_project_id == "specific-project-id":
    #     masked_body = apply_special_masking(masked_body)
 
    return masked_body

Deploy this service and configure Langfuse to use it:

LANGFUSE_INGESTION_MASKING_CALLBACK_URL=https://your-masking-service.internal/mask
LANGFUSE_INGESTION_MASKING_CALLBACK_TIMEOUT_MS=500
LANGFUSE_INGESTION_MASKING_CALLBACK_FAIL_CLOSED=true

Performance Considerations

• Latency: The masking callback adds latency to the ingestion path. Keep your callback service fast (ideally < 100ms).
• Timeout: The default 500ms timeout is designed to balance reliability with performance. Adjust based on your masking complexity.
• Availability: Your masking service should be highly available, especially with fail-closed mode enabled.
• Colocation: Deploy your masking service close to your Langfuse deployment to minimize network latency.

Security Recommendations

• Network Security: Deploy your masking callback within the same VPC or private network as Langfuse.
• Authentication: Consider adding authentication to your callback endpoint (e.g., via a shared secret in a custom header).
• HTTPS: Use HTTPS for the callback URL in production environments.
• Logging: Implement logging in your masking service for auditability, but be careful not to log sensitive data.

Troubleshooting

Events are being dropped unexpectedly

1. Check that your masking service is responding within the configured timeout.
2. Verify the response schema matches the input schema exactly.
3. Review Langfuse Web container logs for warning messages.
4. Temporarily set LANGFUSE_INGESTION_MASKING_CALLBACK_FAIL_CLOSED=false to diagnose issues.

High latency on trace ingestion

1. Monitor your masking service response times.
2. Consider increasing LANGFUSE_INGESTION_MASKING_CALLBACK_TIMEOUT_MS if your masking logic requires more time.
3. Optimize your masking logic or add caching where appropriate.
4. Ensure network latency between Langfuse and your masking service is minimal.

Masking not being applied

1. Verify LANGFUSE_INGESTION_MASKING_CALLBACK_URL is correctly set on the Langfuse Web container.
2. Check that your masking service is reachable from the Langfuse Web container.
3. Ensure your masking logic is correctly modifying the data and returning it.