> ## Documentation Index > Fetch the complete documentation index at: https://docs.getcollate.io/llms.txt > Use this file to discover all available pages before exploring further. # Custom Recognizers for Auto-Classification > Configure custom detection rules to automatically identify and tag sensitive data using pattern matching, exact terms, and pre-built recognizers. # Custom recognizers Custom recognizers let you configure detection rules that automatically identify and tag sensitive data during profiling and ingestion. Unlike the default auto-classification, recognizers give you full control over what patterns to detect and how to tag them. ## What are recognizers? Recognizers are configurable detection rules attached to classification tags. When profiling runs, recognizers analyze your data and automatically apply tags when they detect matching patterns. Each auto-applied tag includes metadata showing which recognizer detected it and the confidence score. **Key benefits:** * **Customizable detection**: Define your own patterns for organization-specific data (employee IDs, internal codes, custom formats) * **Multiple detection methods**: Use regex patterns, exact terms, or 45+ pre-built detectors * **Learning from feedback**: Users can report false positives, which automatically refine recognizer behavior * **Confidence-based tagging**: Set minimum confidence thresholds to control precision ## Recognizer types ### Pattern recognizers Use regular expressions to match structured data formats. **Best for:** * Emails, phone numbers, IP addresses * Custom organizational patterns (employee IDs: `EMP-\d{5}`) * Any data following predictable patterns **Example:** ``` Pattern: \b\d{3}[-.]?\d{3}[-.]?\d{4}\b Detects: US phone numbers (123-456-7890, 123.456.7890) ``` ### Exact Terms recognizers Match specific values from a predefined list. **Best for:** * Known sensitive values (internal project codes, department names) * Fixed vocabularies (country codes, status values) * Cases requiring exact matches (no pattern variation) **Example:** ``` Terms: ["PROJECT_ALPHA", "PROJECT_BETA", "PROJECT_GAMMA"] Detects: Exact matches of confidential project codes ``` ### Predefined recognizers Built-in detectors from Microsoft Presidio (45+ recognizers). **Best for:** * Standard PII (credit cards, SSNs, passports) * International identifiers (IBANs, UK NHS numbers, ES NIF) * When you don't want to write custom regex **Categories:** * **Financial**: CreditCardRecognizer, IbanRecognizer, UsBankRecognizer * **Personal ID**: UsSsnRecognizer, UsPassportRecognizer, InPanRecognizer, InAadhaarRecognizer * **Healthcare**: NhsRecognizer, MedicalLicenseRecognizer * **Contact**: EmailRecognizer, PhoneRecognizer, UrlRecognizer ## Creating a Recognizer 1. Navigate to **Govern > Classification** 2. Select a classification (e.g., "PII") 3. Click on a tag within that classification 4. Go to the **recognizers** tab 5. Click **Add Recognizer** recognizers Page 6. Configure the recognizer: * **Name**: Unique identifier (e.g., `email_pattern`) * **Display Name**: Human-readable name (e.g., "Email Pattern Detector") * **Description**: What this recognizer detects * **Target**: Choose where to analyze: * **Content**: Analyze actual data values * **Column Name**: Analyze only column/field names * **Confidence Threshold**: Minimum score (0.0-1.0) to apply tag (default: 0.6) New Recognizer Form 7. Configure type-specific settings (see below) 8. Click **Submit** ### Pattern Recognizer Settings * **Patterns**: Add one or more regex patterns, each with: * **Name**: Descriptive label * **Regex**: Regular expression * **Score**: Confidence for this pattern (0.0-1.0) * **Context Words** (optional): Words that boost confidence when found near matches (e.g., \["email", "contact"] for email detection) * **Regex Flags**: Configure case sensitivity, multi-line, etc. **Example: Email Detection** ``` Name: email_pattern Target: Content Patterns: - Name: "Standard Email" Regex: [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} Score: 0.9 Context: ["email", "e-mail", "contact"] Confidence Threshold: 0.7 ``` ### Exact Terms Settings * **Exact Terms**: List of exact strings to match * **Ignore Case**: Whether to match case-insensitively **Example: Internal Codes** ``` Name: internal_dept_codes Target: Content Exact Terms: - DEPT_001_CONFIDENTIAL - DEPT_002_RESTRICTED Ignore Case: true Confidence Threshold: 0.9 ``` ### Predefined Recognizer Settings * **Predefined Recognizer**: Select from dropdown (e.g., "UsSsnRecognizer") * **Context Words** (optional): Boost confidence with context (e.g., \["SSN", "social security"]) * **Supported Language**: Select language if recognizer supports multiple **Example: SSN Detection** ``` Name: us_ssn_detector Target: Content Predefined Recognizer: UsSsnRecognizer Context: ["SSN", "social", "security"] Confidence Threshold: 0.8 ``` ## Managing recognizers ### View all recognizers The recognizers tab displays all recognizers for a tag with columns: * **Enabled**: Toggle to activate/deactivate * **Name** & **Description** * **Type**: Pattern, Exact Terms, Predefined, or Context * **Target**: Content or Column Name * **Exceptions**: Number of entities excluded from this recognizer * **Confidence**: Confidence threshold ### Filter & Search Use filters to narrow the list: * **Type**: Pattern, Exact Terms, Predefined, Context * **Target**: Content, Column Name * **Enabled**: Enabled, Disabled Use the search box to find recognizers by name or description. ### Edit a Recognizer 1. Click **Edit** (pencil icon) in the Actions column 2. Modify fields in the form 3. Click **Submit** **Note**: Changes only apply to future classification runs, not retroactively. ### Delete a Recognizer 1. Click **Delete** (trash icon) in the Actions column 2. Confirm deletion **Warning**: Deleting a recognizer does not remove tags it previously applied. You must manually remove those tags if needed. ### Enable/Disable recognizers Use the toggle switch in the Enabled column to temporarily stop a recognizer without deleting it. Changes take effect on the next classification run. ### Managing Exceptions Click the **Exceptions** count to view entities where this recognizer should not run. Exceptions are automatically added when [feedback is approved](/how-to-guides/data-governance/classification/auto-classification/feedback). You can manually delete exceptions: 1. Click exceptions count 2. View list in the exceptions panel 3. Click delete on specific exceptions 4. Confirm removal ## Best Practices ### Creating effective recognizers 1. **Start with high confidence**: Begin with threshold 0.7-0.8, adjust if needed 2. **Test patterns first**: Validate regex patterns with sample data before creating the recognizer 3. **Use context words**: Add relevant context to reduce false positives 4. **Multiple patterns**: Create separate patterns for different formats (e.g., phone: `(123)456-7890` vs `123-456-7890`) 5. **Descriptive names**: Use clear, searchable names and descriptions ### Managing False Positives 1. **Review feedback regularly**: Check pending feedback from users 2. **Adjust thresholds**: If too many false positives, increase confidence threshold 3. **Refine patterns**: Edit patterns to be more specific 4. **Add context words**: Boost confidence for true positives with relevant context ### Performance Tips 1. **Target appropriately**: Use "Column Name" target when possible (faster than content analysis) 2. **Disable unused recognizers**: Deactivate recognizers you no longer need 3. **Combine patterns**: Use one recognizer with multiple patterns instead of many single-pattern recognizers 4. **Limit context words**: Keep context word lists concise (under 20 words) ## Troubleshooting ### Recognizer Not Detecting Data **Check:** * Recognizer is Enabled * Confidence threshold not too high * Pattern syntax is correct (test with a regex tool) * Target matches your use case (Content vs Column Name) * Entity is not in the exception list * Profiler and auto-classification are enabled in ingestion config ### Too Many False Positives **Solutions:** * Increase confidence threshold * Add context words for true positives * Make patterns more specific * Consider using exact terms recognizer instead * Let users submit feedback to build exception lists ### Pattern Not Matching **Common issues:** * Missing escape characters in regex (use `\\d` not `\d`) * Incorrect regex flags (check case sensitivity, multi-line) * Pattern too specific or too broad * Test your pattern at [regex101.com](https://regex101.com) first ## Next Steps Learn how to report false positives and improve recognizer accuracy through user feedback Understand the default PII tagging logic