Quick question about the integration between Data Retention Policies and Architect flow execution for compliance audits.
We are operating under strict GDPR requirements for our Paris queue, where customer PII must be purged from system logs after 30 days. While the standard Data Retention Policy correctly clears historical conversation data, we have identified a discrepancy in how dynamic data blocks within active Architect flows handle this requirement.
Specifically, when a flow stores sensitive information in a context variable for the duration of a complex routing sequence, that data persists in the conversation detail view even after the retention period has elapsed for the initial interaction. The Performance dashboard does not flag this as a compliance breach, but our internal audit team requires assurance that no PII remains accessible in the flow execution logs.
Is there a specific configuration within the Architect settings to force the immediate nullification of context variables upon flow completion, independent of the global retention schedule? Alternatively, does the system automatically scrub these variables once the conversation state transitions to ‘completed’?
You need to decouple the PII storage from the primary flow execution context.
Cause: Architect data blocks are ephemeral by nature but persist in the Conversation API logs for the duration of the interaction. Standard Data Retention Policies apply to the conversation resource type after the fact, which may not meet the strict 30-day purge window for in-flight data blocks containing PII. The logs retain the serialized state of the flow variables, effectively bypassing the immediate deletion trigger.
Solution: Implement a webhook-based scrubbing mechanism. Configure a Data Action to strip PII fields from the flow data block immediately after processing. Use the Genesys Cloud REST API to update the conversation transcript, replacing the sensitive data with a placeholder or null value. This ensures the logs never contain the raw PII in the first place.
{
"name": "StripPII",
"method": "POST",
"url": "https://api.us.genesys.cloud/v2/conversations/{{conversation.id}}/transcripts",
"body": {
"text": "[REDACTED]"
}
}
Cross-reference the GDPR compliance guide for digital channels to ensure the redaction pattern matches your audit requirements.
{
“name”: “gdpr_purge_flow”,
“steps”: [
{
“id”: “1”,
“name”: “Start”,
“type”: “start”,
“transitions”: [{ “nextStepId”: “2” }]
},
{
“id”: “2”,
“name”: “Set Retention Tag”,
“type”: “set-variable”,
“data”: {
“gdpr_purge_flag”: “true”
},
“transitions”: [{ “nextStepId”: “3” }]
}
]
}
The suggestion above about decoupling storage is theoretically sound, but from a load testing perspective, relying solely on post-interaction retention policies is risky. We observed that during peak concurrent call volumes in our Singapore cluster, the background jobs responsible for purging conversation logs often lag behind real-time ingestion. This creates a window where PII persists in the `v2/conversations/details` endpoint longer than the 30-day requirement, especially when the platform is under heavy API throughput stress.
Instead of just setting a flag, try implementing an explicit API call within the flow to sanitize the data immediately upon collection. Use the `DELETE` method on the specific conversation transcription or recording resource right after the PII block is processed. This forces an immediate sync rather than waiting for the batch retention job.
However, be careful with the rate limits. If you inject a `DELETE` request for every single PII capture in a high-volume queue, you will hit the `429 Too Many Requests` limit on the conversation management API quickly. We saw this crash our JMeter scripts when simulating 500 concurrent users. The fix is to batch these deletion requests or use a low-priority web task that processes deletions asynchronously, ensuring the main flow execution isn't blocked by API latency. This approach ensures compliance without choking the WebSocket connection limits during peak loads.