BlueXP classification automatically identifies specific words, strings, and patterns (Regex) inside the data. For example, Personal Identification Information (PII), credit card numbers, social security numbers, bank account numbers, passwords, and more. See the full list. BlueXP classification identifies this type of information in individual files, in files within directories (shares and folders), and in database tables.

Additionally, if you have added a database server to be scanned, the Data Fusion feature allows you to scan your files to identify whether unique identifiers from your databases are found in those files or other databases. See Adding personal data identifiers using Data Fusion for details.

For some types of personal data, BlueXP classification uses proximity validation to validate its findings. The validation occurs by looking for one or more predefined keywords in proximity to the personal data that was found. For example, BlueXP classification identifies a U.S. social security number (SSN) as a SSN if it sees a proximity word next to it—​for example, SSN or social security. The table of personal data shows when BlueXP classification uses proximity validation.

BlueXP classification automatically identifies special types of sensitive personal information, as defined by privacy regulations such as articles 9 and 10 of the GDPR. For example, information regarding a person's health, ethnic origin, or sexual orientation. See the full list. BlueXP classification identifies this type of information in individual files, in files within directories (shares and folders), and in database tables.

BlueXP classification uses artificial intelligence (AI), natural language processing (NLP), machine learning (ML), and cognitive computing (CC) to understand the meaning of the content that it scans in order to extract entities and categorize it accordingly.

For example, one sensitive GDPR data category is ethnic origin. Because of its NLP abilities, BlueXP classification can distinguish the difference between a sentence that reads "George is Mexican" (indicating sensitive data as specified in article 9 of the GDPR), versus "George is eating Mexican food."

BlueXP classification takes the data that it scanned and divides it into different types of categories. Categories are topics based on AI analysis of the content and metadata of each file. See the list of categories.

Categories can help you understand what's happening with your data by showing you the types of information that you have. For example, a category like resumes or employee contracts can include sensitive data. When you investigate the results, you might find that employee contracts are stored in a nonsecure location. You can then correct that issue.

BlueXP classification takes the data that it scanned and breaks it down by file type. Reviewing your file types can help you control your sensitive data because you might find that certain file types are not stored correctly. See the list of file types.

For example, you might be storing CAD files that include very sensitive information about your organization. If they are unsecured, you can take control of the sensitive data by restricting permissions or moving the files to another location.

You can filter the contents of the BlueXP classification dashboard to see compliance data for all working environments and databases, or for just specific working environments.

When you filter the dashboard, BlueXP classification scopes the compliance data and reports to just those working environments that you selected.