Implementing Automated PII and PCI Redaction Pipelines for Digital Interaction Transcripts

How-To-Articles
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Implementing Automated PII and PCI Redaction Pipelines for Digital Interaction Transcripts

What This Guide Covers

  • Going beyond basic “Secure Pause” to implement active, continuous redaction of Personally Identifiable Information (PII) and Payment Card Industry (PCI) data across all digital channels (Chat, SMS, WhatsApp).
  • Utilizing Genesys Cloud’s built-in PII masking, combined with AWS Comprehend (via EventBridge) for advanced, custom regex-based redaction of unstructured text.
  • The end result is a highly secure digital transcript vault where credit card numbers, social security numbers, and health records are scrubbed from the database before they are ever saved to disk, ensuring strict compliance.

Prerequisites, Roles & Licensing

  • Licensing: Genesys Cloud CX 2 or 3 (Digital).
  • Permissions: Routing > Message > Edit, Integrations > Integration > Edit.
  • Infrastructure: AWS EventBridge and AWS Lambda (if implementing custom backend redaction).

The Implementation Deep-Dive

1. The Vulnerability of the Written Word

In a voice call, you can use “Secure Pause” to stop the recording when a customer reads their credit card number aloud.

The Trap:
In a digital channel, the customer types the number. Even if the agent doesn’t ask for it, customers will spontaneously type: “Hi, my card 4111-2222-3333-4444 isn’t working.” Once they hit enter, that raw 16-digit PAN (Primary Account Number) is sent over the network, displayed on the agent’s screen, and permanently etched into the Genesys Cloud transcript database. This is an immediate, catastrophic violation of PCI-DSS. You cannot retroactively delete a single message from a transcript natively; you must prevent it from being saved in the first place.

2. Native Genesys Cloud PII/PCI Masking

Genesys Cloud offers a native masking feature that acts as a first line of defense.

Implementation Steps:

  1. Navigate to Admin > Account Settings > Organization Settings.
  2. Go to the Security & Compliance tab.
  3. Enable Data Masking.
  4. You will see default Regex patterns for Credit Cards (PCI) and Social Security Numbers (SSN).
  5. Ensure these are set to Mask.
  6. The Result: When the customer types a 16-digit number, the Genesys Cloud edge servers intercept the payload. The agent sees ****-****-****-4444, and the exact same masked string is written to the historical transcript. The raw data never touches the database.

3. The Limitation of Native Masking

Native masking is rigid. It works well for standard US SSNs and Visa cards, but it fails on proprietary formats (e.g., a specific 12-digit patient MRN in a hospital).

Architectural Reasoning:
To scrub custom formats or complex PII (like a combination of Name + Diagnosis), you must intercept the messages using an AWS Lambda middleware before they reach your long-term storage or analytics engine.

Implementation Steps (AWS EventBridge + Comprehend):

  1. Configure an Amazon EventBridge integration in Genesys Cloud.
  2. Subscribe to the v2.detail.events.conversation.{id}.transcripts topic.
  3. Route this topic to an AWS Lambda function.
  4. The Python Script: Inside the Lambda, pass the transcript text to Amazon Comprehend’s Medical or PII detection API.
import boto3
import re

comprehend = boto3.client('comprehend')

def lambda_handler(event, context):
    transcript_text = event['detail']['messages'][0]['text']
    
    # 1. Custom Regex for Proprietary Patient IDs (Format: PAT-123456)
    scrubbed_text = re.sub(r'PAT-\d{6}', '[REDACTED_PATIENT_ID]', transcript_text)
    
    # 2. AWS Comprehend for unstructured PII (Names, Addresses)
    response = comprehend.detect_pii_entities(Text=scrubbed_text, LanguageCode='en')
    
    for entity in response['Entities']:
        if entity['Type'] in ['NAME', 'ADDRESS', 'EMAIL']:
            # Replace the PII with asterisks based on the offset
            start, end = entity['BeginOffset'], entity['EndOffset']
            scrubbed_text = scrubbed_text[:start] + ('*' * (end - start)) + scrubbed_text[end:]
            
    # Save scrubbed_text to your S3 Vault
    # ...

4. Handling Agent-Side Leaks

It’s not just customers who leak data; agents often copy-paste sensitive info into the chat.

Implementation Steps:

  1. Native Genesys Data Masking applies to both inbound and outbound messages. If an agent tries to type a credit card number to confirm it, it will be masked on the customer’s screen and in the transcript.
  2. Coaching the Behavior: Create an Analytics query or a Speech/Text Analytics topic looking for the string ****.
  3. If an agent is constantly triggering the masking algorithm, it means they are repeatedly attempting to type or request PCI data in an unauthorized channel. Flag these agents for immediate compliance retraining.

Validation, Edge Cases & Troubleshooting

Edge Case 1: The False Positive Nightmare

  • The Failure Condition: A customer is trying to provide a tracking number for a lost package: 4111222233334444. The native Regex aggressively masks it as a credit card. The agent cannot see the tracking number and the interaction fails.
  • The Root Cause: Loose Regex logic. A 16-digit string without spaces or dashes is identical to a raw PAN.
  • The Solution: The native masking regex in Genesys Cloud is editable. You must tune it. If your tracking numbers are always 16 digits, but credit cards in your region always start with specific BINs (e.g., 4 for Visa, 5 for Mastercard), modify the Regex to only trigger on those specific leading integers. Alternatively, train agents on a bypass protocol (e.g., “Please type your tracking number with a space between every 4 digits”).

Edge Case 2: Attachments and Images

  • The Failure Condition: The customer realizes typing the credit card is unsafe, so instead, they take a physical photograph of their Visa card and upload it as a .jpg attachment in the Web Chat. Your text-based redaction scripts ignore it. You now have raw PCI data sitting in your media servers.
  • The Root Cause: Regex and text-based PII scanners cannot read images.
  • The Solution: If your business does not explicitly require image uploads (e.g., for damage claims), you must disable attachments in your Web Messaging deployment settings. If attachments are required, you must route the attachmentId through an AWS Lambda function that utilizes Amazon Rekognition (OCR) to scan the image for 16-digit numbers. If detected, use the Genesys API to immediately delete the attachment from the server.

Official References