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Privacy Controls

OpenInference provides comprehensive privacy controls to help you maintain security and compliance while still benefiting from observability. You can mask PII, filter sensitive data, and selectively suppress tracing.

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Overview

Privacy controls in OpenInference allow you to:
  • Mask sensitive data - Replace PII with "__REDACTED__" placeholder
  • Filter specific content - Hide images, text, or embeddings selectively
  • Suppress tracing - Completely disable tracing for specific code blocks
  • Control payload size - Limit large data like base64-encoded images

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Redacted Value Placeholder

When content is hidden, OpenInference uses the constant "__REDACTED__" as a placeholder. This allows trace consumers to identify that content was intentionally hidden rather than missing or empty. The redacted value constant is available for import: 
from openinference.instrumentation import REDACTED_VALUE
print(REDACTED_VALUE)  # "__REDACTED__"

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PII Masking

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Hiding Text Content

Protect user messages and LLM responses while preserving trace structure: 
from openinference.instrumentation import TraceConfig

config = TraceConfig(
    hide_input_text=True,
    hide_output_text=True,
)
This configuration:
  • Replaces message text with "__REDACTED__"
  • Preserves message structure and metadata
  • Maintains trace flow and timing information

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Hiding Complete Messages

Remove entire message objects from traces: 
config = TraceConfig(
    hide_input_messages=True,
    hide_output_messages=True,
)

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Hiding All Inputs/Outputs

Hide both values and messages completely: 
config = TraceConfig(
    hide_inputs=True,
    hide_outputs=True,
)
Note: Setting hide_inputs=True automatically hides input messages. Setting hide_input_messages=True hides only messages but not the input value.

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Data Filtering

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Hiding Images

Remove images from traces while keeping text: 
config = TraceConfig(
    hide_input_images=True,
)
This is useful when:
  • Images may contain sensitive or identifying information
  • You want to reduce storage costs
  • Compliance requires removing visual data

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Limiting Image Size

Truncate large base64-encoded images: 
config = TraceConfig(
    base64_image_max_length=8000,  # Limit to 8KB
)
Images exceeding this length are replaced with "__REDACTED__".

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Hiding Embeddings

Protect embedding vectors and their associated text: 
config = TraceConfig(
    hide_embeddings_vectors=True,
    hide_embeddings_text=True,
)

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Suppressing Tracing

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Context Manager (Python)

Completely disable tracing for specific code blocks: 
from openinference.instrumentation import suppress_tracing

# Normal tracing occurs here
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)

# No tracing occurs within this block
with suppress_tracing():
    sensitive_response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "SSN: 123-45-6789"}]
    )

# Normal tracing resumes here

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Async Support (Python)

from openinference.instrumentation import suppress_tracing

async def process_sensitive_data():
    async with suppress_tracing():
        # No tracing occurs in this async block
        result = await async_llm_call()
    return result

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JavaScript Suppression

Use OpenTelemetry’s built-in suppression: 
import { suppressTracing } from "@opentelemetry/core";
import { context } from "@opentelemetry/api";

// Normal tracing
const response1 = await openai.chat.completions.create({
  model: "gpt-4",
  messages: [{ role: "user", content: "Hello" }],
});

// Suppress tracing for this call
await context.with(suppressTracing(context.active()), async () => {
  const sensitiveResponse = await openai.chat.completions.create({
    model: "gpt-4",
    messages: [{ role: "user", content: "SSN: 123-45-6789" }],
  });
});

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Common Privacy Scenarios

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GDPR Compliance

Minimize personal data collection: 
config = TraceConfig(
    hide_input_text=True,
    hide_output_text=True,
    hide_embeddings_text=True,
    hide_input_images=True,
)

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HIPAA Compliance

Protect healthcare information: 
config = TraceConfig(
    hide_inputs=True,
    hide_outputs=True,
    hide_embeddings_vectors=True,
    hide_embeddings_text=True,
)

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Financial Services

Hide transaction details and account numbers: 
config = TraceConfig(
    hide_input_text=True,
    hide_output_text=True,
    hide_llm_invocation_parameters=True,
)

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Development vs. Production

Use environment-specific configurations: 
import os
from openinference.instrumentation import TraceConfig

if os.getenv("ENVIRONMENT") == "production":
    config = TraceConfig(
        hide_input_text=True,
        hide_output_text=True,
    )
else:
    # Development: full observability
    config = TraceConfig()

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Cost Optimization

Reduce storage costs while maintaining observability: 
config = TraceConfig(
    hide_input_images=True,
    base64_image_max_length=4000,
    hide_embeddings_vectors=True,
)

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Advanced Patterns

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Selective Message Filtering

Hide text but keep images for debugging: 
config = TraceConfig(
    hide_input_text=True,
    hide_output_text=True,
    hide_input_images=False,  # Keep images
)

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Embedding Privacy

Hide vectors but keep text for searchability: 
config = TraceConfig(
    hide_embeddings_vectors=True,
    hide_embeddings_text=False,
)

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LLM-Specific Controls

Hide only invocation parameters: 
config = TraceConfig(
    hide_llm_invocation_parameters=True,  # Hide model config
    hide_inputs=False,  # Show inputs
    hide_outputs=False,  # Show outputs
)

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Completions API Privacy

For legacy completions API: 
config = TraceConfig(
    hide_prompts=True,  # Hide prompt strings
    hide_choices=True,  # Hide completion choices
)

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Best Practices

  1. Start restrictive - Begin with more privacy controls and relax as needed
  2. Test in development - Verify traces contain expected information before deploying
  3. Document your choices - Keep a record of which privacy controls are enabled and why
  4. Use environment variables - Make privacy settings configurable per environment
  5. Review regularly - Audit your privacy settings as requirements change
  6. Combine methods - Use both TraceConfig and suppress_tracing for comprehensive control
  7. Monitor impact - Ensure privacy controls don’t hide critical debugging information

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Validation

Verify your privacy controls are working: 
from openinference.instrumentation import TraceConfig, REDACTED_VALUE
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter

# Set up tracing with privacy controls
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

config = TraceConfig(hide_input_text=True)

# Instrument and test
from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider, config=config)

# Make a call and check console output for REDACTED_VALUE

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