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Overview

PII Redaction is a CrewAI AMP feature that automatically detects and masks Personally Identifiable Information (PII) in your crew and flow execution traces. This ensures sensitive data like credit card numbers, social security numbers, email addresses, and names are not exposed in your CrewAI AMP traces. You can also create custom recognizers to protect organization-specific data.
PII Redaction is available on the Enterprise plan. Deployment must be version 1.8.0 or higher.
PII Redaction Overview

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Why PII Redaction Matters

When running AI agents in production, sensitive information often flows through your crews:
  • Customer data from CRM integrations
  • Financial information from payment processors
  • Personal details from form submissions
  • Internal employee data
Without proper redaction, this data appears in traces, making compliance with regulations like GDPR, HIPAA, and PCI-DSS challenging. PII Redaction solves this by automatically masking sensitive data before it’s stored in traces.

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How It Works

  1. Detect - Scan trace event data for known PII patterns
  2. Classify - Identify the type of sensitive data (credit card, SSN, email, etc.)
  3. Mask/Redact - Replace the sensitive data with masked values based on your configuration
Original: "Contact [email protected] or call 555-123-4567"
Redacted: "Contact <EMAIL_ADDRESS> or call <PHONE_NUMBER>"

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Enabling PII Redaction

You must be on the Enterprise plan and your deployment must be version 1.8.0 or higher to use this feature.
1

Navigate to Crew Settings

In the CrewAI AMP dashboard, select your deployed crew and go to one of your deployments/automations, then navigate to Settings → PII Protection.
2

Enable PII Protection

Toggle on PII Redaction for Traces. This will enable automatic scanning and redaction of trace data.
You need to manually enable PII Redaction for each deployment.
Enable PII Redaction
3

Configure Entity Types

Select which types of PII to detect and redact. Each entity can be individually enabled or disabled.
Configure Entities
4

Save

Save your configuration. PII redaction will be active on all subsequent crew executions, no redeployment is needed.

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Supported Entity Types

CrewAI supports the following PII entity types, organized by category.

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Global Entities

EntityDescriptionExample
CREDIT_CARDCredit/debit card numbers”4111-1111-1111-1111”
CRYPTOCryptocurrency wallet addresses”bc1qxy2kgd…”
DATE_TIMEDates and times”January 15, 2024”
EMAIL_ADDRESSEmail addresses”[email protected]”
IBAN_CODEInternational bank account numbers”DE89 3704 0044 0532 0130 00”
IP_ADDRESSIPv4 and IPv6 addresses”192.168.1.1”
LOCATIONGeographic locations”New York City”
MEDICAL_LICENSEMedical license numbers”MD12345”
NRPNationalities, religious, or political groups-
PERSONPersonal names”John Doe”
PHONE_NUMBERPhone numbers in various formats”+1 (555) 123-4567”
URLWeb URLs”https://example.com”

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US-Specific Entities

EntityDescriptionExample
US_BANK_NUMBERUS Bank account numbers”1234567890”
US_DRIVER_LICENSEUS Driver’s license numbers”D1234567”
US_ITINIndividual Taxpayer ID”900-70-0000”
US_PASSPORTUS Passport numbers”123456789”
US_SSNSocial Security Numbers”123-45-6789”

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Redaction Actions

For each enabled entity, you can configure how the data is redacted:
ActionDescriptionExample Output
maskReplace with the entity type label<CREDIT_CARD>
redactCompletely remove the text(empty)

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Custom Recognizers

In addition to built-in entities, you can create custom recognizers to detect organization-specific PII patterns.
Custom Recognizers

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Recognizer Types

You have two options for custom recognizers:
TypeBest ForExample Use Case
Pattern-based (Regex)Structured data with predictable formatsSalary amounts, employee IDs, project codes
Deny-listExact string matchesCompany names, internal codenames, specific terms

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Creating a Custom Recognizer

1

Navigate to Custom Recognizers

Go to your Organization Settings → Organization → Add Recognizer.
2

Configure the Recognizer

Configure Recognizer
Configure the following fields:
  • Name: A descriptive name for the recognizer
  • Entity Type: The entity label that will appear in redacted output (e.g., EMPLOYEE_ID, SALARY)
  • Type: Choose between Regex Pattern or Deny List
  • Pattern/Values: Regex pattern or list of strings to match
  • Confidence Threshold: Minimum score (0.0-1.0) required for a match to trigger redaction. Higher values (e.g., 0.8) reduce false positives but may miss some matches. Lower values (e.g., 0.5) catch more matches but may over-redact. Default is 0.8.
  • Context Words (optional): Words that increase detection confidence when found nearby
3

Save

Save the recognizer. It will be available to enable on your deployments.

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Understanding Entity Types

The Entity Type determines how matched content appears in redacted traces: 
Entity Type: SALARY
Pattern: salary:\s*\$\s*\d+
Input: "Employee salary: $50,000"
Output: "Employee <SALARY>"

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Using Context Words

Context words improve accuracy by increasing confidence when specific terms appear near the matched pattern: 
Context Words: "project", "code", "internal"
Entity Type: PROJECT_CODE
Pattern: PRJ-\d{4}
When “project” or “code” appears near “PRJ-1234”, the recognizer has higher confidence it’s a true match, reducing false positives.

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Viewing Redacted Traces

Once PII redaction is enabled, your traces will show redacted values in place of sensitive data: 
Task Output: "Customer <PERSON> placed order #12345.
Contact email: <EMAIL_ADDRESS>, phone: <PHONE_NUMBER>.
Payment processed for card ending in <CREDIT_CARD>."
Redacted values are clearly marked with angle brackets and the entity type label (e.g., <EMAIL_ADDRESS>), making it easy to understand what data was protected while still allowing you to debug and monitor crew behavior.

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Best Practices

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Performance Considerations

1

Enable Only Needed Entities

Each enabled entity adds processing overhead. Only enable entities relevant to your data.
2

Use Specific Patterns

For custom recognizers, use specific patterns to reduce false positives and improve performance. Regex patterns are best when identifying specific patterns in the traces such as salary, employee id, project code, etc. Deny-list recognizers are best when identifying exact strings in the traces such as company names, internal codenames, etc.
3

Leverage Context Words

Context words improve accuracy by only triggering detection when surrounding text matches.

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Troubleshooting

Possible Causes:
  • Entity type not enabled in configuration
  • Pattern doesn’t match the data format
  • Custom recognizer has syntax errors
Solutions:
  • Verify entity is enabled in Settings → Security
  • Test regex patterns with sample data
  • Check logs for configuration errors
Possible Causes:
  • Overly broad entity types enabled (e.g., DATE_TIME catches dates everywhere)
  • Custom recognizer patterns are too general
Solutions:
  • Disable entities that cause false positives
  • Make custom patterns more specific
  • Add context words to improve accuracy
Possible Causes:
  • Too many entities enabled
  • NLP-based entities (PERSON, LOCATION, NRP) are computationally expensive as they use machine learning models
Solutions:
  • Only enable entities you actually need
  • Consider using pattern-based alternatives where possible
  • Monitor trace processing times in the dashboard

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Practical Example: Salary Pattern Matching

This example demonstrates how to create a custom recognizer to detect and mask salary information in your traces.

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Use Case

Your crew processes employee or financial data that includes salary information in formats like:
  • salary: $50,000
  • salary: $125,000.00
  • salary:$1,500.50
You want to automatically mask these values to protect sensitive compensation data.

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Configuration

Salary Recognizer Configuration
FieldValue
NameSALARY
Entity TypeSALARY
TypeRegex Pattern
Regex Patternsalary:\s*\$\s*\d{1,3}(,\d{3})*(\.\d{2})?
ActionMask
Confidence Threshold0.8
Context Wordssalary, compensation, pay, wage, income

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Regex Pattern Breakdown

Pattern ComponentMeaning
salary:Matches the literal text “salary:“
\s*Matches zero or more whitespace characters
\$Matches the dollar sign (escaped)
\s*Matches zero or more whitespace characters after $
\d{1,3}Matches 1-3 digits (e.g., “1”, “50”, “125”)
(,\d{3})*Matches comma-separated thousands (e.g., “,000”, “,500,000”)
(\.\d{2})?Optionally matches cents (e.g., “.00”, “.50”)

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Example Results

Original: "Employee record shows salary: $125,000.00 annually"
Redacted: "Employee record shows <SALARY> annually"

Original: "Base salary:$50,000 with bonus potential"
Redacted: "Base <SALARY> with bonus potential"
Adding context words like “salary”, “compensation”, “pay”, “wage”, and “income” helps increase detection confidence when these terms appear near the matched pattern, reducing false positives.

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Enable the Recognizer for Your Deployments

Creating a custom recognizer at the organization level does not automatically enable it for your deployments. You must manually enable each recognizer for every deployment where you want it applied.
After creating your custom recognizer, enable it for each deployment:
1

Navigate to Your Deployment

Go to your deployment/automation and open Settings → PII Protection.
2

Select Custom Recognizers

Under Mask Recognizers, you’ll see your organization-defined recognizers. Check the box next to the recognizers you want to enable.
Enable Custom Recognizer
3

Save Configuration

Save your changes. The recognizer will be active on all subsequent executions for this deployment.
Repeat this process for each deployment where you need the custom recognizer. This gives you granular control over which recognizers are active in different environments (e.g., development vs. production).