Is there a clean way to configure data masking rules in Genesys Cloud to ensure that Personally Identifiable Information (PII) is redacted in conversation transcripts without inadvertently suppressing critical data points required for accurate performance metric calculations in the EU-West BYOC environment? Our organization is currently undergoing a mandatory compliance audit regarding GDPR adherence, necessitating the immediate implementation of strict data masking policies across all inbound and outbound voice channels. The current implementation involves the creation of custom regular expression patterns within the Security Settings to identify and mask credit card numbers, national identification numbers, and health-related information. While the masking functionality appears to operate correctly within the individual conversation detail views, there is a significant concern regarding the potential impact on downstream analytics. Specifically, the Performance Dashboard metrics, including Average Handle Time (AHT) and First Contact Resolution (FCR), rely on unobstructed access to conversation metadata and transcript content for accurate calculation. There is a documented risk that aggressive masking rules may interfere with the natural language processing algorithms used to derive sentiment analysis and interaction categorization, leading to skewed performance data for agents and supervisors. The environment is running on the latest stable release of the Genesys Cloud platform, with Architect flows configured to route calls based on complex IVR logic that includes dynamic data collection. The issue is not related to API access or SDK integration, as the focus is strictly on the native platform capabilities for data governance and performance reporting. It is imperative to establish a configuration standard that balances regulatory compliance with the integrity of operational metrics. The current uncertainty regarding the interaction between data masking rules and performance metric engines creates a bottleneck in the deployment process. Clarification is required on whether specific masking actions trigger a recalculation of performance data or if historical data remains unaffected by changes in masking policies. Additionally, guidance on best practices for testing the impact of new masking rules on dashboard accuracy before full-scale deployment would be beneficial. The goal is to achieve full compliance without compromising the visibility required for effective workforce management and quality assurance.