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Pre-recorded PII Redaction automatically detects and replaces sensitive entities (names, emails, addresses, etc.) in your transcript output. This feature is only available for pre-recorded transcription.

Handling audio data often involves processing conversations that contain personally identifiable information such as names, phone numbers, or financial details. Redacting PII helps you comply with privacy regulations like GDPR and CCPA/CPRA, protect your users’ sensitive data, and reduce the risk of data breaches when storing or sharing transcripts.

Usage

Add "pii_redaction": true to your request to redact all detected PII in the transcript. Sensitive entities will be replaced with markers in the output.

Optional configuration

You can customize the behavior with pii_redaction_config:
string[]
Preset or list of PII entity types to redact (e.g. ["GDPR"]). See Supported entity types for available presets.
  • Omitted or empty: the full default detection set is redacted.
  • Non-empty: only the listed presets and entity types are redacted. The list replaces the default set, it is not added to it. Everything else stays in the clear, and the job still completes with status: done.
To combine coverage, list several values, e.g. ["HIPAA_SAFE_HARBOR", "HEALTH_INFORMATION"].
enum
default:"MARKER"
How to replace detected PII:
  • MASK: Each character replaced by a mask (e.g. “John Smith” → #### #####)
  • MARKER: Placeholder labels like [NAME_1], [EMAIL_1]. Same entity will have same ID.

Example body

Pre-recorded

Example output

Without PII redaction (raw transcript):
Hi, I’m calling about the order for John Smith. Can you confirm the delivery to john.smith@company.com? Yes, John Smith placed it yesterday.
With PII redaction (processed_text_type="MASK"):
Hi, I’m calling about the order for #### #####. Can you confirm the delivery to ######################? Yes, #### ##### placed it yesterday.
With PII redaction (processed_text_type="MARKER"):
Hi, I’m calling about the order for [NAME_1]. Can you confirm the delivery to [EMAIL_1]? Yes, [NAME_1] placed it yesterday.
The same entity mentioned multiple times receives the same marker ID (e.g. “John Smith” becomes [NAME_1] both times), so you can track references across the transcript while keeping sensitive data redacted.
This consistency is also useful for downstream tasks using LLMs, which can reason about entities (e.g. “the person in [NAME_1]”) without ever seeing the raw PII.

Supported entity types

When using entity_types, you can pass a preset group or a list of specific types. Each preset only covers the entity types listed under it: pick the preset matching your requirement, or list several of them.

Preset groups

Personal data entities covered by the EU GDPR.Includes: AGE, DRIVER_LICENSE, DOB, EMAIL_ADDRESS, HEALTHCARE_NUMBER, IP_ADDRESS, LANGUAGE, LOCATION, LOCATION_ADDRESS, LOCATION_ADDRESS_STREET, LOCATION_CITY, LOCATION_COORDINATE, LOCATION_COUNTRY, LOCATION_STATE, LOCATION_ZIP, NAME, NAME_FAMILY, NAME_GIVEN, NAME_MEDICAL_PROFESSIONAL, NUMERICAL_PII, PASSPORT_NUMBER, PHONE_NUMBER, SSN, URL, USERNAME, VEHICLE_ID, BANK_ACCOUNT, CREDIT_CARD, CREDIT_CARD_EXPIRATION, CVV, BLOOD_TYPE, CONDITION, DRUG, INJURY, MEDICAL_PROCESS
Sensitive personal data as defined by GDPR Article 9.Includes: GENDER, LANGUAGE, ORIGIN, PHYSICAL_ATTRIBUTE, POLITICAL_AFFILIATION, RELIGION, SEXUALITY
De-identification standard for US healthcare data under HIPAA. Use this preset to redact patient identifiers (names, contact details, SSN, record numbers, etc.).It does not include clinical content such as conditions or drugs. Add HEALTH_INFORMATION to redact those too.Includes: ACCOUNT_NUMBER, AGE, DATE, DATE_INTERVAL, DOB, DRIVER_LICENSE, EMAIL_ADDRESS, HEALTHCARE_NUMBER, IP_ADDRESS, LOCATION, LOCATION_ADDRESS, LOCATION_ADDRESS_STREET, LOCATION_CITY, LOCATION_COORDINATE, LOCATION_ZIP, NAME, NAME_FAMILY, NAME_GIVEN, NAME_MEDICAL_PROFESSIONAL, NUMERICAL_PII, PASSPORT_NUMBER, PHONE_NUMBER, SSN, URL, VEHICLE_ID, BANK_ACCOUNT, CREDIT_CARD, CREDIT_CARD_EXPIRATION, CVV
Clinical content only: medical conditions, drugs, doses, injuries, procedures, etc.
HEALTH_INFORMATION is not a HIPAA preset. It does not redact names, contact details, SSN or other identifiers. Use HIPAA_SAFE_HARBOR (or GDPR) for those, e.g. ["HIPAA_SAFE_HARBOR", "HEALTH_INFORMATION"] to redact both.
Includes: BLOOD_TYPE, CONDITION, DOSE, DRUG, INJURY, MEDICAL_PROCESS, STATISTICS
PCI-specific financial entities only.Includes: BANK_ACCOUNT, CREDIT_CARD, CREDIT_CARD_EXPIRATION, CVV, ROUTING_NUMBER
Personal data entities covered by the California Privacy Rights Act.Includes: DOB, DRIVER_LICENSE, EMAIL_ADDRESS, GENDER, HEALTHCARE_NUMBER, IP_ADDRESS, LOCATION_ADDRESS, LOCATION_ADDRESS_STREET, LOCATION_CITY, LOCATION_COORDINATE, LOCATION_ZIP, NAME, NAME_FAMILY, NAME_GIVEN, NAME_MEDICAL_PROFESSIONAL, NUMERICAL_PII, ORIGIN, PASSPORT_NUMBER, PASSWORD, PHONE_NUMBER, PHYSICAL_ATTRIBUTE, POLITICAL_AFFILIATION, RELIGION, SEXUALITY, SSN, URL, USERNAME, VEHICLE_ID, BANK_ACCOUNT, CREDIT_CARD, CREDIT_CARD_EXPIRATION, CVV, BLOOD_TYPE, CONDITION, DRUG, INJURY, MEDICAL_PROCESS
Personal data entities covered by Japan’s APPI.Includes: AGE, DOB, DRIVER_LICENSE, EMAIL_ADDRESS, HEALTHCARE_NUMBER, IP_ADDRESS, LOCATION, LOCATION_ADDRESS, LOCATION_ADDRESS_STREET, LOCATION_CITY, LOCATION_COORDINATE, LOCATION_COUNTRY, LOCATION_STATE, LOCATION_ZIP, NAME, NAME_FAMILY, NAME_GIVEN, NAME_MEDICAL_PROFESSIONAL, NUMERICAL_PII, OCCUPATION, ORGANIZATION, ORGANIZATION_MEDICAL_FACILITY, PASSPORT_NUMBER, PASSWORD, PHONE_NUMBER, SSN, VEHICLE_ID, BANK_ACCOUNT, CREDIT_CARD, CREDIT_CARD_EXPIRATION, CVV, DRUG
Sensitive data subset under Japan’s APPI.Includes: CONDITION, DRUG, GENDER, INJURY, LANGUAGE, MARITAL_STATUS, MEDICAL_PROCESS, ORIGIN, PHYSICAL_ATTRIBUTE, POLITICAL_AFFILIATION, RELIGION, SEXUALITY
Personal data entities covered by Quebec’s Privacy Act / Law 25.Includes: AGE, DATE, DATE_INTERVAL, DOB, DRIVER_LICENSE, EMAIL_ADDRESS, HEALTHCARE_NUMBER, IP_ADDRESS, LOCATION_ADDRESS, LOCATION_ADDRESS_STREET, LOCATION_CITY, LOCATION_COORDINATE, LOCATION_ZIP, NAME, NAME_FAMILY, NAME_GIVEN, NAME_MEDICAL_PROFESSIONAL, NUMERICAL_PII, OCCUPATION, ORGANIZATION, ORGANIZATION_MEDICAL_FACILITY, ORIGIN, PASSPORT_NUMBER, PHONE_NUMBER, POLITICAL_AFFILIATION, RELIGION, SSN, BANK_ACCOUNT, CREDIT_CARD, CREDIT_CARD_EXPIRATION, CVV, BLOOD_TYPE, CONDITION, DRUG, INJURY, MEDICAL_PROCESS, STATISTICS
Core set of the most commonly used PII entity types across all regulations.
Business and corporate data entities.Includes: ACCOUNT_NUMBER, DATE, DATE_INTERVAL, EMAIL_ADDRESS, FILENAME, LOCATION, LOCATION_ADDRESS, LOCATION_ADDRESS_STREET, LOCATION_CITY, LOCATION_COUNTRY, LOCATION_STATE, LOCATION_ZIP, MONEY, NUMERICAL_PII, OCCUPATION, ORGANIZATION, PASSWORD, TIME, URL, VEHICLE_ID, BANK_ACCOUNT, CREDIT_CARD, CREDIT_CARD_EXPIRATION, CVV, ROUTING_NUMBER, DRUG, MEDICAL_PROCESS
Entities that can identify individuals within a local context.
Numerical PII entities, excluding PCI-specific types.Includes: ACCOUNT_NUMBER, NUMERICAL_PII, SSN, etc.Excludes: CREDIT_CARD, CVV, BANK_ACCOUNT, ROUTING_NUMBER

Individual entity types

Core PII

Financial / PCI

Sensitive / GDPR Article 9

Healthcare