Data privacy is important to protect sensitive information such as personally identifiable information, financial data, and medical records from theft, fraud, or misuse. It also helps to build trust between individuals and organizations.

Personal data is any information that can be used to find a person, such as a name, address, email address, phone number, social security number, or date of birth.

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Personally identifiable information (PII) is a subset of personal data that can be used to uniquely find an individual, such as their full name, Social Security number, driver's license number, or passport number.

Sensitive data is any information that, if shown, could cause harm to an individual or organization, such as financial information, medical records, or confidential business information.

ID-REDACT^® is a data redaction product that uses artificial intelligence and machine learning to remove personally identifiable information (PII) and other sensitive data from large datasets. It is provided by Data Safeguard (Datasafeguard.ai).

ID-REDACT^® works by analyzing the dataset and finding any sensitive or personally identifiable information, such as names, addresses, social security numbers, or credit card numbers. The product then applies advanced algorithms, artificial intelligence, machine learning, and contextual rules to remove the identified information while keeping the integrity and accuracy of the remaining data.

ID-REDACT^® can be applied to a wide range of data types and file formats, including databases, data warehouses, data lakes, Microsoft Word documents, PDF, Microsoft Excel, Microsoft PowerPoint, CSV, and online programs like Chat and Mail.

Using ID-REDACT^® can help organizations protect sensitive information and follow data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). It can also reduce the risk of data breaches and help organizations keep the trust of their customers and stakeholders.

Synthetic fraud is a type of identity theft in which a fraudster creates a new identity by combining real and fake information to open fraudulent accounts or obtain loans or credit cards.

A fraudster creates a Frankenstein identity by combining real and fake information, such as a real Social Security number and a fake name or address. The fraudster then uses the Frankenstein identity to apply for credit cards, loans, or other financial accounts. Over time, the fraudster builds up a credit history for the Frankenstein identity, making it easier to obtain more credit in the future.

Synthetic ID fraud can be difficult to detect because the fraudster is using a combination of real and fake information. The fraudster may also build up a credit history for the Frankenstein identity over time, making it look more legitimate.

Financial institutions can detect synthetic ID fraud by using advanced fraud detection techniques, such as behavioral analytics and machine learning. They can also verify the authenticity of Social Security numbers and other personal information and check for suspicious activity.

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