How to Redact Sensitive PII from Voice AI Call Recordings and Transcripts (2026)

The Complete Dual-Channel Redaction Pipeline:

Caller Spoken Audio (WAV Stream): "My Social Security Number is 4 9 2 - 1 8 - 9 9 2 1"
                                 │
                 ┌───────────────┴───────────────┐
                 ▼                               ▼
Path A: Text Transcription (STT)       Path B: Audio Buffer Sync
- Audio word timestamps:               - Target time interval:
  "4 9 2" [T=3.4s - 4.1s]                Start: 3.400s | End: 5.600s
  "1 8"   [T=4.1s - 4.8s]                        │
  "9 9 2 1" [T=4.8s - 5.6s]                      │
                 │                               │
                 ▼                               ▼
[Transformer NER Entity Classifier]    [Digital Zeroing / Audio Hum Mute]
- Replaces text with:                  - Audio sliced and overwritten with
  "My SSN is [REDACTED_SSN]"             flat 0.0 amplitude silence
                 │                               │
                 └───────────────┬───────────────┘
                                 ▼
[Safe Redacted Transcript + Safe Redacted Audio Exported to CRM / S3]

The 7 Standard PII Entity Types:

1. Financial Identifiers:
   - 16-Digit Credit Card Numbers, CVV Codes, Bank Account & Routing Numbers.

2. Government Identity Numbers:
   - Social Security Numbers (SSN), Aadhaar Numbers, PAN, National Insurance Numbers.

3. Telephony & Contact Data:
   - Personal Mobile Numbers, Unlisted Numbers, Private Email Addresses.

4. Dates of Birth & Age Identifiers:
   - Full dates of birth (*"January 14th, 1984"*).

5. Healthcare Identifiers:
   - Health Insurance Policy Numbers, Medicare Numbers, Prescription RX Numbers.

6. Residential Addresses:
   - Street names, apartment numbers, and private residential locations.

7. Passwords & Access Credentials:
   - Spoken PIN codes, telephone banking passwords, security question answers.

Real-Time vs Post-Call Redaction Comparison:

Option 1: Real-Time Stream Redaction (In-Flight)
- Execution: Performed frame-by-frame during the live phone conversation.
- Advantage: Sensitive data is never written to temporary memory or logs.
- Trade-off: Requires low-latency lightweight regex models (<15ms).

Option 2: Asynchronous Post-Call Redaction (High Accuracy)
- Execution: Performed within 3 seconds of call disconnection.
- Advantage: Deep bidirectional transformer models (RoBERTa / DeBERTa) evaluate
  entire conversational context with 99.8% precision.
- Best For: Legal compliance audits, long-term archival storage.

import asyncio
import re

class AudioPIIRedactionEngine:
    """
    Synchronously redacts sensitive PII entities from both written transcripts
    and raw PCM audio buffers.
    """
    def __init__(self):
        # Entity detection regex patterns
        self.ssn_pattern = re.compile(r"\b\d{3}-\d{2}-\d{4}\b|\b\d{9}\b")
        self.card_pattern = re.compile(r"\b(?:\d{4}[ -]?){3}\d{4}\b")

    def redact_transcript(self, raw_text: str) -> str:
        """Masks PII tokens in written transcript."""
        text = self.ssn_pattern.sub("[REDACTED_SSN]", raw_text)
        text = self.card_pattern.sub("[REDACTED_CREDIT_CARD]", text)
        return text

    def redact_audio_slice(self, audio_buffer_bytes: bytearray, start_ms: int, end_ms: int, sample_rate_hz: int = 8000) -> bytearray:
        """Zeros out audio samples in the target millisecond range."""
        bytes_per_ms = (sample_rate_hz * 2) // 1000 # 16-bit PCM = 2 bytes per sample
        start_byte = start_ms * bytes_per_ms
        end_byte = min(end_ms * bytes_per_ms, len(audio_buffer_bytes))
        
        # Overwrite with digital silence (0x00)
        for i in range(start_byte, end_byte):
            audio_buffer_bytes[i] = 0
            
        print(f"[Audio Zeroing]: Muted {end_ms - start_ms}ms of audio (Bytes {start_byte} to {end_byte}).")
        return audio_buffer_bytes

if __name__ == "__main__":
    engine = AudioPIIRedactionEngine()
    
    # 1. Text Redaction Test
    raw_transcript = "My customer account SSN is 492-18-9921, please verify."
    clean_transcript = engine.redact_transcript(raw_transcript)
    print("=== Text Redaction Test ===")
    print(f"Original: '{raw_transcript}'")
    print(f"Redacted: '{clean_transcript}'")
    
    # 2. Audio Muting Test (Simulates 10-second 8kHz audio buffer)
    fake_audio = bytearray(160000) # 10s * 8000 * 2 bytes
    engine.redact_audio_slice(fake_audio, start_ms=3400, end_ms=5600)