Vendor needs to provide data masking solution. Provide and implement a best-in-class masking solution (the “Solution”). The solution must deliver robust data masking capabilities and meets the high-level functional and nonfunctional requirements. The Solution must automatically discover and identify sensitive data elements across the supported data sources, including personal, health, financial, and claim-related data, using a combination of metadata inspection, pattern recognition, and configurable detection logic. The Solution must allow the configure, manage, and maintain data classification and masking rules that align with the data classification standards and privacy obligations. This includes the ability to refine and tune detection rules to reduce false positives and false negatives. The Solution must support multiple masking techniques, such as substitution, tokenization, shuffling, nulling, and encryption-based masking, and must allow to apply different techniques based on data type, sensitivity level, and business usage requirements. The Solution must preserve referential integrity, data relationships, and key dependencies across tables, schemas, and databases to ensure that applications, integrations, reports, and analytics continue to function correctly after masking is applied. The Solution must support consistent and repeatable masking results across recurring data refreshes and across multiple non-production environments, including development, testing, training, and business intelligence environments. The Solution must provide comprehensive monitoring, logging, error handling, and audit capabilities for all discovery, classification, and masking activities, including the ability to demonstrate what data was masked, when, how, and under which rules. The Solution must support role-based access controls and segregation of duties, ensuring that only authorized users can configure rules, execute masking jobs, view sensitive metadata, or approve masking exception. The contract period will be for two years. All the questions must be submitted no later than February 12, 2026.