Content detected by predefined EDD rules

The DAR component uses expressions to identify brand-specific patterns in credit card numbers, such as Visa, MasterCard, Discover, American Express, and Diner's Club. Each credit card has its own unique numbering pattern, which includes specific prefixes and a defined number of digits.

Credit card numbers are recognized if they are broken into space-separated segments (tokens), as long as there are at least three digits in each segment. Special characters, such as a hyphen (-), are also recognized. In addition, the Luhn algorithm Also known as the modulus 10 or mod 10 algorithm. A simple checksum formula used to validate identification numbers, such as credit card numbers. is used to verify that the character string is a valid credit card number and not a series of random numbers.

Each occurrence of a valid, unique credit card number detected in a file generates a Match Score of 1. For example, if the same credit card number is detected three times in a file, a Match Score of 1 is generated. If three unique credit card numbers are detected, a Match Score of 3 is generated.

The DAR component uses expressions to identify valid US Social Security Numbers (SSN) written in the following pattern: nnn-nn-nnnn.

The following patterns indicate invalid SSNs:

• Numbers with all zeros in any grouping (000-nn-nnnn, nnn-00-nnnn, or nnn-nn-0000).
• Numbers that start with 666 or 9nn.

For more information about the SSN numbering system, see USrecordsearch.com.

Each occurrence of a valid, unique SSN detected in a file generates a Match Score of 1. For example, if the same SSN is detected three times in a file, a Match Score of 1 is generated. If three unique SSNs are detected, a Match Score of 3 is generated.

The DAR component uses the following expression sets to identify US Personal Health Information (PHI) content:

• Health Terms
• Personal Health Identifiers (includes Social Security Numbers and Medicare Beneficiary Identifiers)

Health Terms is a lexicon that includes words and phrases, such as:

• fracture
• CAT scan
• convulsions
• aggressive fibromatosis
• ocular refraction

Personal Health Identifiers include expressions that define valid identifiers, such as:

• Social Security Number or SSN, followed by a valid Social Security Number
• Date of Birth , DOB, Birth Date, and so forth, followed by a date in a supported format
• Patient, followed by a name or number
• Account, Member, Record, and so forth, followed by a number

For the component to detect a match and report it in an EDD report, both lexicons must be matched. For example:

A file's PHI Match Score is calculated by tallying the total number of unique Personal Health Identifiers. Although a file must include at least one health term for a match to be detected, no Match Score is assigned to a matched health term. Therefore, the higher the Match Score, the more likely that a file contains Personal Health Information for many individuals rather than just one.

The DAR component uses expressions to identify personal data that is subject to the protection requirements set out in the General Data Protection Regulation (GDPR) The General Data Protection Regulation (GDPR) defines a set of data protection rules that apply to all organizations that process data related to individuals residing in the European Economic Area (EEA). for the countries of the European Economic Area (EEA) The EEA includes all European Union (EU) countries and also Iceland, Liechtenstein, and Norway. It defines the countries that participate in the European Single Market..

The following 40 personal identifiers are detected by the predefined GDPR Personal Data rule:

The rule include expressions that define a valid EEA personal identifier, such as:

• Codice Fiscal or CF, followed by a valid Italian Tax Code ID number
• Documento Nacional de Identidad or DNI, followed by a valid Spanish DNI number

A file's GDPR Personal Data Match Score is calculated by tallying the total number of unique EEA personal identifiers.

The DAR component uses the following expression sets to identify US Personal Financial Information (PFI) content:

• Financial Terms
• Personal Financial Identifiers

Financial Terms is a lexicon that includes words and phrases, such as:

• account balance
• annual rate
• direct deposit
• mortgage loan
• routing number

Personal Financial Identifiers include expressions that define valid identifiers, such as:

• Social Security Number or SSN, followed by a valid Social Security Number
• Account, Loan, Customer, Certificate, and so forth, followed by a name or number

For the component to detect a match and report it in an EDD report, both expression sets must be matched. For example:

A file's PFI Match Score is calculated by tallying the total number of unique Personal Financial Identifiers. Although a file must include at least one financial term for a match to be detected, no Match Score is assigned to a matched financial term. Therefore, the higher the Match Score, the more likely that a file contains Personal Financial Information for many individuals rather than just one.

The DAR component uses the Profanity lexicon to identify words or phrases that are considered to be profane in American English. Each unique term found in the file generates a Match Score of 1, regardless of how many times that particular term is used.

The DAR component identifies encrypted file A file that has been converted to an encrypted form to prevent unauthorized users from viewing the sensitive data contained within it. An encryption key is required to decrypt and view the file.s and password-protected files. Because the component is unable to open and analyze these files, it assigns the Encrypted content type to these occurrences. Each encrypted or password-protected file generates a Match Score of 1.

The DAR component uses a Bullying lexicon to identify file content that is inherently insulting, offensive, or threatening and therefore may indicate that a bullying incident has occurred. Internet slang (including commonly used acronyms) is also included in the lexicon if it's insulting, offensive, or threatening in nature. Each unique term found in the file generates a Match Score of 1, regardless of how many times the term is used.

The Bullying lexicon includes a preliminary list of terms; some instances of bullying may not be detected.

The DAR component identifies files that can't be scanned because they were in use at the time of the scan, or the files reside in an encrypted file directory. Each unscannable file generates a Match Score of 1.