We are configuring a Reporting Stream to export contact details to an external data lake hosted within the EU region. The requirement is to comply with GDPR Article 17 regarding the Right to be Forgotten while maintaining analytical integrity. Current configuration utilizes the GET /api/v2/analytics/contacts endpoint via Webhooks, but PII fields such as callerId and recordingTranscript appear unmasked in the payload before reaching our S3 bucket. This creates a potential violation of data minimization principles under Article 5. We need to implement masking within Genesys Cloud prior to export without losing correlation IDs for audit trails. Has anyone successfully applied Data Actions to filter or anonymize these specific fields during the stream delivery process?
This scenario requires careful handling of sensitive data types before they leave the platform. The standard approach involves creating a custom Data Action that intercepts the contact record payload. You must define a transformation rule within the Data Action settings to replace the callerId value with a hashed identifier while retaining the contactId.
Example configuration for the Data Action payload:
{
“name”: “mask_pii_data”,
“type”: “MASKING”,
“fields”: [
{
“target”: “callerId”,
“action”: “HASH_SHA256”
},
{
“target”: “recordingTranscript”,
“action”: “REDACT_WORDS”
}
]
}
Regarding the migration from legacy on-prem reporting tools, ensure that the new stream maintains the same correlation keys used in your knowledge base articles. If you are using Genesys DX integrations for chatbot handoff notes, those fields also require similar masking logic to remain compliant with data privacy regulations during the export phase.
Just a heads up on performance implications here. Polling that endpoint repeatedly can hit rate limits quickly at scale. We ran load tests using JMeter and saw significant degradation when calling /api/v2/analytics/contacts more than 50 times per minute per org.
Consider switching to Push Reporting Streams instead of REST API polling for this use case. It reduces the overhead on the API gateway significantly. If you do stick with polling, ensure the throttle settings in your load balancer allow for bursts up to 100 requests per second during peak hours. Otherwise the backoff logic will cause delays that might violate SLA reporting requirements.