Data masking is a cybersecurity technique that replaces sensitive information within databases, files, or applications with fictitious but realistic data. The primary objective is to protect confidential data from unauthorized access while maintaining the usability of datasets for development, testing, analytics, or training purposes. By obscuring original values, data masking helps organizations reduce the risk of data breaches and comply with privacy regulations. Core capabilities of data masking solutions include static and dynamic masking, format-preserving transformations, and policy-driven masking rules. These tools often support integration with various data sources and can automate the identification and masking of sensitive fields such as personally identifiable information (PII), payment card data, or protected health information (PHI). The technical scope may also encompass audit logging and role-based access controls to ensure proper governance. Data masking is typically used by organizations in regulated industries, such as finance, healthcare, and retail, as well as by IT teams responsible for software development and quality assurance. Unlike encryption or tokenization, which protect data in production environments and require decryption for use, data masking irreversibly alters data for non-production contexts, minimizing exposure during testing or analytics. Cyberin provides a platform for professionals to explore and compare data masking solutions based on technical features and organizational requirements.