> For the complete documentation index, see [llms.txt](https://docs.decube.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.decube.io/governance/auto-classify-data-assets/use-case-examples-rules.md).

# Use Case Examples - Rules

Data governance stewards often rely on column naming patterns to automatically tag or classify sensitive data.\
This helps ensure consistency in data catalogs, lineage, and compliance reporting.

Below are common examples of regex-based patterns used to detect and tag sensitive columns automatically.

**Notes**:

* Regular expressions in Elasticsearch must follow standard regex syntax (e.g. `.*UNID.*`, not `*UNID*`).
* Use the `"flags": "CASE_INSENSITIVE"` parameter to match column names regardless of case.
* Combine multiple terms using parentheses and `|`, e.g. `.*(UNID|SERNO).*`.

***

### 1. Personal Identifiers

Detect columns that contain unique identifiers for individuals.

| Example Column Names                         | Regex Pattern (Elasticsearch syntax)           |
| -------------------------------------------- | ---------------------------------------------- |
| `customer_unid`, `user_serno`, `employee_id` | `.*(UNID\|SERNO\|EMP(_)?ID\|CUSTOMER(_)?ID).*` |
| `uuid`, `guid`, `unique_ref`                 | `.*(UUID\|GUID\|UNIQUE).*`                     |

***

### 2. Contact Information

Identify fields that hold email addresses, phone numbers, or physical addresses.

| Example Column Names                              | Regex Pattern              |
| ------------------------------------------------- | -------------------------- |
| `email`, `user_email`, `contact_email_address`    | `.*EMAIL.*`                |
| `phone_number`, `mobile_no`, `contact_tel`        | `.*(PHONE\|MOBILE\|TEL).*` |
| `address_line1`, `home_address`, `postal_address` | `.*ADDRESS.*`              |

***

### 3. Financial Data

Detect columns that may contain sensitive financial details.

| Example Column Names                     | Regex Pattern                |
| ---------------------------------------- | ---------------------------- |
| `credit_card_number`, `ccn`, `card_no`   | `.*(CREDIT.?CARD\|CCN).*`    |
| `bank_account_number`, `iban`, `acct_no` | `.*(ACCOUNT\|ACCT\|IBAN).*`  |
| `salary_amount`, `income`, `wage`        | `.*(SALARY\|INCOME\|WAGE).*` |

***

### 4. National Identifiers

Detect government-issued identifiers.

| Example Column Names                             | Regex Pattern                               |
| ------------------------------------------------ | ------------------------------------------- |
| `ssn`, `social_security_number`, `nin`, `tax_id` | `.*(SSN\|SOCIAL.?SECURITY\|NIN\|TAX.?ID).*` |
| `passport_no`, `passport_number`                 | `.*PASSPORT.*`                              |

***

### 5. Authentication Data

Identify columns that may contain credentials or tokens.

| Example Column Names                    | Regex Pattern                         |
| --------------------------------------- | ------------------------------------- |
| `password`, `user_pwd`, `hash`, `token` | `.*(PASSWORD\|PWD\|HASH\|TOKEN).*`    |
| `api_key`, `access_key`, `secret_key`   | `.*(API.?KEY\|ACCESS.?KEY\|SECRET).*` |

***

### 6. Health and Demographic Data

Classify columns related to personal health or demographic details.

| Example Column Names                  | Regex Pattern                 |
| ------------------------------------- | ----------------------------- |
| `medical_record_number`, `patient_id` | `.*(MEDICAL\|PATIENT).*`      |
| `age`, `birth_date`, `dob`            | `.*(AGE\|BIRTH.?DATE\|DOB).*` |
| `gender`, `sex`                       | `.*(GENDER\|SEX).*`           |

***

### How It Works

When column name classification is enabled:

1. The system evaluates each column name against the configured regex patterns.
2. Columns matching any pattern are automatically tagged with the associated classification.

This ensures sensitive data is consistently identified and categorized across all datasets, even as new data sources are added.

***