Architecting Advanced Redaction of PCI/PII Data in Real-Time Speech Transcriptions

How-To-Articles
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Architecting Advanced Redaction of PCI/PII Data in Real-Time Speech Transcriptions

What This Guide Covers

You are implementing a multi-layer, real-time speech transcription redaction pipeline integrated with Genesys Cloud’s native transcription services and external speech analytics platforms. When complete, your architecture will redact credit card numbers, Social Security Numbers, and other regulated PII from live transcripts as they are generated (not retrospectively), ensuring that sensitive data never enters your transcript database, downstream analytics systems, or agent screen recordings in plaintext-reducing your PCI-DSS and HIPAA compliance scope dramatically.

Prerequisites, Roles & Licensing

  • Genesys Cloud: CX 2 or 3 with Real-Time Speech Transcription (RTST) or Speech Analytics.
  • Permissions required:
    • Architect > Flow > Edit (for configuring Secure Pause integration)
    • Analytics > Conversation > View
    • Recording > Recording > Edit (for recording policy configuration)
  • Infrastructure:
    • AWS Lambda (or Azure Function) for intercepting and processing transcript WebSocket streams.
    • AWS Transcribe or Genesys Cloud native transcription.
    • A SIEM for redaction event logging.

The Implementation Deep-Dive

1. The Three Transcription Redaction Layers

Real-time PCI/PII redaction requires addressing three distinct layers, because a failure at any single layer exposes the data:

  1. Layer 1 - Native Platform Redaction: Genesys Cloud’s built-in Data Masking for digital channels applies regex to transcribed text. For voice transcription, AWS Transcribe Medical and standard AWS Transcribe have native RedactionType parameters.

  2. Layer 2 - Transcript Stream Interception: When Genesys Cloud streams transcription events via the Notifications API WebSocket, your Lambda can intercept and sanitize the stream before downstream systems consume it.

  3. Layer 3 - Stored Transcript Retroactive Scanning: Even with Layers 1 and 2 active, edge cases (unusual accent recognition, split-word patterns) may leak data. A nightly batch job scans all stored transcripts and retrospectively redacts any detected PII.

2. Layer 1 - AWS Transcribe Native Redaction Configuration

When configuring the AWS Transcribe job settings for your Genesys BYOC audio stream or recording pipeline, enable native entity redaction.

import boto3

TRANSCRIBE = boto3.client('transcribe', region_name='us-east-1')

def start_transcription_with_redaction(audio_s3_uri: str, job_name: str) -> str:
    """Starts an AWS Transcribe job with PII entity redaction enabled."""
    
    response = TRANSCRIBE.start_transcription_job(
        TranscriptionJobName=job_name,
        Media={'MediaFileUri': audio_s3_uri},
        MediaFormat='wav',
        LanguageCode='en-US',
        
        # PII Redaction Configuration
        ContentRedaction={
            'RedactionType': 'PII',
            'RedactionOutput': 'redacted',  # 'redacted' or 'redacted_and_unredacted'
            'PiiEntityTypes': [
                'CREDIT_DEBIT_NUMBER',
                'CREDIT_DEBIT_CVV',
                'CREDIT_DEBIT_EXPIRY',
                'SSN',
                'BANK_ACCOUNT_NUMBER',
                'BANK_ROUTING',
                'PHONE',
                'ADDRESS',
                'NAME',  # Toggle off if agent names should appear in transcript
            ]
        },
        
        # Output to dedicated redacted transcripts bucket
        OutputBucketName='your-redacted-transcripts-bucket',
        OutputKey=f'redacted/{job_name}.json'
    )
    
    return response['TranscriptionJob']['TranscriptionJobName']

When RedactionOutput is redacted, the output transcript replaces PAN with [PII]. The unredacted transcript is never written to S3 in this mode.

3. Layer 2 - Real-Time WebSocket Stream Sanitization

Genesys Cloud’s real-time transcription streams transcript increments (partial and final results) via the Notifications API WebSocket. If downstream systems (like a custom screen-pop or an analytics dashboard) subscribe to this WebSocket, they could receive unredacted transcript chunks.

Intercepting the Stream with a Lambda Proxy:

import re
import json
import asyncio
import websockets

# Redaction patterns ordered by priority
REDACTION_PATTERNS = [
    (re.compile(r'\b(?:\d[ -]*?){15,16}\b'), '[CARD REDACTED]'),   # PAN
    (re.compile(r'\b\d{3}[- ]?\d{2}[- ]?\d{4}\b'), '[SSN REDACTED]'),  # SSN
    (re.compile(r'\b\d{3}\b', re.IGNORECASE), '[CVV REDACTED]'),         # CVV (context-aware)
]

def redact_text(text: str) -> tuple[str, int]:
    """Applies all redaction patterns. Returns (redacted_text, violation_count)."""
    violations = 0
    for pattern, replacement in REDACTION_PATTERNS:
        matches = pattern.findall(text)
        if matches:
            violations += len(matches)
            text = pattern.sub(replacement, text)
    return text, violations

async def transcript_proxy(genesys_ws_url: str, downstream_ws_url: str, auth_token: str):
    """
    WebSocket proxy that sanitizes Genesys transcript events before 
    forwarding to downstream consumers (dashboards, analytics).
    """
    async with websockets.connect(
        genesys_ws_url,
        extra_headers={"Authorization": f"Bearer {auth_token}"}
    ) as genesys_ws, websockets.connect(downstream_ws_url) as downstream_ws:
        
        async for raw_message in genesys_ws:
            event = json.loads(raw_message)
            
            # Only process transcript events
            if event.get('topicName', '').endswith('transcription'):
                transcript_data = event.get('eventBody', {})
                
                # Redact the transcript text field
                original_text = transcript_data.get('transcript', '')
                redacted_text, violation_count = redact_text(original_text)
                
                if violation_count > 0:
                    transcript_data['transcript'] = redacted_text
                    transcript_data['pii_redacted'] = True
                    transcript_data['pii_violations'] = violation_count
                    
                    # Log the violation event (not the content) to SIEM
                    log_redaction_event(
                        conversation_id=event.get('eventBody', {}).get('conversationId'),
                        violation_count=violation_count
                    )
                
                # Forward the sanitized event
                await downstream_ws.send(json.dumps(event))

4. Layer 3 - Retroactive Stored Transcript Scanning

A nightly batch job provides the safety net for any PII that slipped through Layers 1 and 2.

import boto3
import re

S3 = boto3.client('s3')
BUCKET = 'your-transcript-archive-bucket'

def scan_and_redact_stored_transcripts():
    """Batch job: scans all transcripts from the last 24 hours for PII patterns."""
    
    paginator = S3.get_paginator('list_objects_v2')
    pages = paginator.paginate(Bucket=BUCKET, Prefix='transcripts/')
    
    for page in pages:
        for obj in page.get('Contents', []):
            key = obj['Key']
            
            # Get the transcript file
            response = S3.get_object(Bucket=BUCKET, Key=key)
            content = response['Body'].read().decode('utf-8')
            
            # Scan for PII
            redacted_content, violation_count = redact_text(content)
            
            if violation_count > 0:
                # Overwrite the object with redacted content
                S3.put_object(
                    Bucket=BUCKET,
                    Key=key,
                    Body=redacted_content.encode('utf-8'),
                    Metadata={'pii_redacted': 'true', 'violations': str(violation_count)}
                )
                
                # Log to SIEM for compliance audit trail
                log_retroactive_redaction_event(key, violation_count)

Validation, Edge Cases & Troubleshooting

Edge Case 1: Speech Recognition Splitting Digits Across Words

AWS Transcribe and Genesys native transcription may split a 16-digit card number across multiple partial transcript events: “four one one one” (first event), “two two two two three three” (second event). Neither partial event matches the 16-digit regex individually.
Solution: Implement a sliding window buffer that concatenates the last N words from consecutive transcript events before applying redaction. If the concatenated buffer matches a PAN pattern, retroactively redact the matching words across the stored partial events.

Edge Case 2: CVV False Positives

A 3-digit CVV regex (\b\d{3}\b) will match any 3-digit number in the transcript-zip codes, product codes, order numbers-producing excessive false positives.
Solution: Only activate the CVV pattern when the agent has asked for a card number in the previous 5 transcript tokens (using a context window analysis). If “CVV,” “security code,” or “3 digits” appears in the recent transcript, activate the 3-digit redaction for the next 30 seconds; otherwise, disable it.

Edge Case 3: Redaction Introducing Metric Distortions

If your NLP analytics pipeline uses raw transcripts to detect “Payment Frustrated” sentiment and the redacted transcript changes “my card number 4111 ending in 1234” to “my [CARD REDACTED] ending in [CARD REDACTED]”, the sentiment model might fail to classify the intent correctly.
Solution: Maintain two parallel transcript stores: the redacted version for all downstream consumers (agents, dashboards, long-term analytics), and a tokenized version where the PAN is replaced with [PAN_TOKEN_xxxx] rather than a meaningless placeholder. The tokenized version preserves sentence structure for NLP models while keeping the actual data protected.

Official References