Implementing Recording Pause and Resume Automation Triggered by PCI Payment Card Detection

How-To-Articles
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Implementing Recording Pause and Resume Automation Triggered by PCI Payment Card Detection

What This Guide Covers

This guide details the architectural implementation of dynamic call recording suspension in Genesys Cloud CX when a customer reads a credit card number. You will configure a Speech Analytics model to detect PAN (Primary Account Number) patterns, expose that detection state to Genesys Architect, and build a flow logic that issues a pause command during the read and a resume command immediately after. The resulting architecture ensures that no sensitive card data is stored in your recording repository, satisfying PCI-DSS Requirement 3.4 while maintaining full audit trails for the rest of the interaction.

Prerequisites, Roles & Licensing

  • Licensing: Genesys Cloud CX 1.0 (or higher) with Speech Analytics add-on enabled.
  • Permissions:
    • Speech Analytics > Models > Edit
    • Architect > Flows > Edit
    • Architect > Settings > Edit (to enable custom attributes if required)
    • Recording > Settings > Edit
  • External Dependencies:
    • A configured and active Speech Analytics Model with LLM or Keyword capabilities.
    • An Architect Flow handling the voice interaction.
    • OAuth Scopes: If using the API directly for testing, speechanalytics:write and recording:edit.

The Implementation Deep-Dive

1. Designing the Speech Analytics Detection Model

The foundation of this automation is not in the Architect flow, but in the Speech Analytics model. You cannot pause a recording based on a guess; you need a deterministic signal. While Regex can catch numbers, it creates false positives on phone numbers or zip codes. We will use a combination of Keyword triggers and Pattern matching to minimize false positives.

Step 1.1: Create the PCI Detection Model

Navigate to Admin > Speech Analytics > Models. Create a new model named PCI-PAN-Detection.

  1. Select Model Type: Choose Keyword or Custom. For PAN detection, a Custom model utilizing Regex is most robust, but Keyword is easier to maintain for simple “credit card” phrases. We will use a Hybrid approach.
  2. Define the Trigger:
    • Create a Keyword Group named Card_Payment_Context. Add terms: credit card, visa, mastercard, debit card, card number, account number.
    • Create a Regex Pattern named PAN_Pattern. Use the standard Luhn algorithm compatible regex. A simplified version for detection purposes:
      \b(?:\d{4}[-\s]?){3}\d{4}\b
      Note: This matches 16-digit sequences with optional separators. Do not use this for validation, only for detection.

Step 1.2: Configure Real-Time Streaming

For pause/resume to work, the model must stream results to Architect.

  1. In the Model Settings, enable Real-time Streaming.
  2. Set the Threshold to 0.6 (or higher). A lower threshold increases false positives, causing unnecessary recording pauses which creates gaps in compliance audits. A higher threshold risks missing the card number.
  3. Enable “Notify Architect”: This is the critical switch. Without this, the model runs in the background, and Architect never receives the speechAnalyticsEvent.

The Trap: The Latency Window
Speech Analytics processing is not instantaneous. There is a latency of approximately 1.5 to 3 seconds between the customer speaking the digits and the JSON event arriving in Architect via WebSocket. If you pause the recording after the event arrives, you have already recorded 2-3 seconds of the card number.

  • The Fix: You must configure the Keyword Group to trigger on the context (e.g., “Please read your card number”) rather than the number itself. Or, accept the small latency gap and mask the audio in post-processing if your compliance policy allows a few seconds of bleed. For strict PCI-DSS, context-triggered pausing is safer.

2. Exposing Detection State to Architect

Genesys Architect does not natively “know” what Speech Analytics hears unless you explicitly wire it. We will use the Speech Analytics Event block to capture the detection and set a flow variable.

Step 2.1: Define Flow Variables

In your Architect Flow, add two Flow Variables:

  1. pci_detection_active (Type: Boolean, Default: false)
  2. pci_pause_timestamp (Type: DateTime, Default: null)

Step 2.2: Insert the Speech Analytics Event Block

Place a Speech Analytics Event block in your main flow loop (typically after the agent is connected).

  1. Model: Select PCI-PAN-Detection.
  2. Event Type: Select Keyword Matched or Pattern Matched.
  3. Output: Map the event to a variable pci_event_data.

Architect Expression Logic:
You need to determine if the matched keyword indicates the start of the card reading.
Use a Set Variable block immediately after the Speech Analytics Event:

// Check if the matched text contains context keywords
pci_detection_active = contains(pci_event_data.text, "card number") || contains(pci_event_data.text, "credit card")

The Trap: Race Conditions in Parallel Paths
If your flow has parallel paths (e.g., an IVR playing hold music while Speech Analytics processes), the Speech Analytics Event block might block the flow execution if not configured as a “Notification Only” or if placed incorrectly in a loop.

  • The Fix: Use the Speech Analytics Event block in a Loop structure with a Wait block, or better yet, use the Webhook approach if the flow logic is too complex. However, for standard pause/resume, placing the Event block in the main conversation loop is acceptable provided you do not put heavy processing blocks immediately after it that could delay the pause command.

3. Implementing the Pause/Resume Logic in Architect

Now that we have a boolean flag pci_detection_active, we must issue the recording control commands.

Step 3.1: The Pause Trigger

When pci_detection_active becomes true, we must pause the recording.

  1. Add a Condition block checking pci_detection_active == true.
  2. If true, proceed to a Set Variable block:
    • pci_pause_timestamp = now()
  3. Add a Recording Control block.
    • Action: Pause
    • Scope: Entire Conversation (or Agent Side / Customer Side depending on PCI scope. Usually, both sides are paused to prevent any audio bleed).

Step 3.2: The Resume Trigger

We need to know when the card reading is over. Speech Analytics will stop sending “Card Number” keywords once the customer stops reading digits. We can use a Timer or a Negative Detection.

Approach A: The Timer (Recommended for Simplicity)
Assume a card number read takes 5-10 seconds.

  1. After pausing, add a Wait block for 6 seconds.
  2. Add a Set Variable block: pci_detection_active = false.
  3. Add a Recording Control block.
    • Action: Resume

Approach B: The Negative Detection (More Robust)

  1. Create a second Keyword Group in the Speech Analytics model: End_of_Payment_Context with keywords done, finished, okay, that is it.
  2. In Architect, listen for this second event.
  3. When End_of_Payment_Context is matched, set pci_detection_active = false and Resume recording.

The Trap: The “Forever Paused” Recording
If the Speech Analytics model fails to send the “end” signal, or if the timer is too long, the recording remains paused. When the call ends, Genesys Cloud will upload a recording that has a massive gap or is entirely silent for the last portion. This breaks compliance audits because the agent’s post-payment summary is missing.

  • The Fix: Always implement a Fallback Resume. In your Call Ended block, add a condition:
    if (pci_detection_active == true) {
    // Force resume before hangup to capture final disposition
    Recording Control -> Resume
    Wait 2 seconds
}
    This ensures that even if the detection logic fails, the final 2 seconds are captured, and the recording file is not corrupted by an abrupt end.

4. Handling Multi-Channel and Transfer Scenarios

PCI data can be shared during transfers or in chat sessions. The recording pause logic must persist across routing events.

Step 4.1: Transfer Handling

If an agent transfers the call while pci_detection_active is true, the recording pause must carry over to the receiving agent.

  1. In the Transfer block, ensure the Recording Control state is not reset.
  2. Genesys Cloud maintains the recording state per conversation ID. However, if the transfer creates a new conversation leg (e.g., a blind transfer to an external number), you must re-evaluate the Speech Analytics stream.
  3. Best Practice: Do not pause recording during blind transfers to external numbers unless you have a secure SIP trunk with encryption. For internal transfers, the pause state persists automatically if the Conversation ID remains the same.

Step 4.2: Chat and Webchat

Speech Analytics for Chat works differently. It processes text streams.

  1. Enable Real-time Text Analytics in the Chat Model.
  2. In the Chat Architect Flow, use the Text Event block.
  3. If the regex matches, use the Set Chat Recording State block (if available in your tenant) or rely on Data Masking in the Transcript rather than pausing, as pausing a chat transcript creates a literal gap in the log which is often less useful than a masked **** string.
  • Note: PCI-DSS generally requires masking, not necessarily deletion/pausing, for chat. Pausing is more critical for Voice.

Validation, Edge Cases & Troubleshooting

Edge Case 1: False Positive on Zip Codes or Phone Numbers

The Failure Condition: The customer states their zip code (“555-1234”) or phone number (“123-456-7890”), and the recording pauses.
The Root Cause: The Regex \b(?:\d{4}[-\s]?){3}\d{4}\b is too broad. Phone numbers in some formats match the 16-digit pattern if spaces are ignored, or zip codes match shorter patterns if not constrained.
The Solution:

  1. Refine the Regex to require exactly 13-19 digits.
  2. Add a Negative Keyword in the Speech Analytics model: Exclude matches if the preceding 5 seconds contain keywords zip code, postal code, area code, phone number.
  3. In Architect, add a Condition before pausing:
    // Check confidence score from Speech Analytics
if (pci_event_data.confidence > 0.85) {
    pci_detection_active = true
}

Edge Case 2: The “Silent” Pause (Agent Talking Over the Pause)

The Failure Condition: The agent asks for the card number. The customer reads it. The system pauses. The agent says “Thank you” and the recording resumes. However, the agent’s “Thank you” is sometimes cut off or merged with the pause gap, making the transcript disjointed.
The Root Cause: The latency of the resume command. If you use a fixed timer (6 seconds), and the agent speaks at 7 seconds, the recording might still be paused.
The Solution:

  1. Use the Speech Analytics “Silence” Detection. Configure the model to detect when the customer stops speaking for 2 seconds.
  2. Trigger the Resume only when Speech Analytics Activity drops to Idle for > 2 seconds AND pci_detection_active is true.
  3. This ensures the recording resumes as soon as the sensitive data stream ends, capturing the agent’s immediate follow-up.

Edge Case 3: Compliance Audit Failure (Missing Recording Segments)

The Failure Condition: The compliance team reports that recordings from Q3 have large black holes. They cannot verify that the agent read the Terms and Conditions.
The Root Cause: The pci_detection_active flag was set to true but never reset to false due to a flow error (e.g., the customer hung up during the card read). The recording remained paused until the next call leg or was never resumed.
The Solution:

  1. Implement the Fallback Resume in the Call Ended block as described in Section 3.2.
  2. Add a Log block in Architect when Pause/Resume occurs:
    Log "PCI Recording Paused at " + pci_pause_timestamp
  3. Use Genesys Cloud Analytics to query the Call table for calls where Recording Duration < Call Duration by a significant margin. This allows you to proactively identify calls where the pause logic may have stuck.

Official References