Tool Capabilities: Capabilities of data masking tools help us protect sensitive information while maintaining the functionality of our systems. It involves replacing sensitive data with fictional or altered values, ensuring that the masked data closely resembles real data while safeguarding privacy and compliance. Data masking is particularly important when working with sensitive data like personally identifiable information (PII), protected health information (PHI), and other confidential data. The Data Masking (DM) tool must be an on-premises software solution. Under no circumstances should data be transmitted outside the organization for masking or any other activity. Regulatory Compliance: By masking sensitive data, we align with industry regulations such as HIPAA, GDPR, and more, reducing the potential for compliance violations. After data masking, the masked data should not be reversible or reproducible in any way back to its original state. Usable Test Data: Masked data retains the format and relationships of real data, making it suitable for testing, development, and analytics without compromising usability. Data Security: Data masking ensures that sensitive information remains confidential, reducing the risk of data breaches and unauthorized access. Privacy Protection: Data masking safeguards individuals' privacy by preventing exposure of personal details during testing, development, and analysis. Risk Mitigation: Data masking reduces the ri sk of data exposure during activities like software development, quality assurance, and third-party collaborations. Limits or Conditions on Tool Use: Provide a detailed description of any limitations or conditions under which the tool operates. This includes specifying if the tool works only under certain circumstances or if there are specific restrictions on its functionality or any limits or conditions on its use. Key Items: Data Types: Identify the types of data that need masking, such as PII, PHI, financial information, and more. Masking Techniques: Choose appropriate masking techniques like substitution, shuffling, encryption, and tokenization based on data type and sensitivity. Usability: Ensure that masked data remains useful for intended purposes without affecting system functionality or testing accuracy. Data Mapping: Create a comprehensive map of sensitive data to identify where masking is required across databases, flat files, and other data sources.
