Custom Data Protection Filter

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Overview

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What the Guardrail Does

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Purpose

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Scope

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Regex-Based Pattern Detection

• Input – Applies the selected behaviour to what users send to the model.
• Output – Applies the selected behaviour to what the model returns as a response.
• Both – Full bidirectional coverage

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Operational Modes

• Monitor – Lets you review input or output content without taking any action—used for observation and diagnostics.
• Block – Automatically stops content from being processed if it violates the selected guardrail rules.
• Mask – Replaces detected sensitive information with anonymised placeholders while allowing content to proceed.

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Detection Approach

• Deterministic Matching: Exact pattern matching based on defined regex rules
• Custom Patterns: User-defined regex patterns for specific data formats
• Predictable Results: Consistent detection based on pattern matching rules
• High Precision: Low false positive rates when patterns are well-defined

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Key Features

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Why Use This Guardrail?

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Benefits

• Precise Control: Define exactly what patterns should be detected using regex
• Predictable Results: Deterministic matching ensures consistent behaviour
• Custom Compliance: Support for organisation-specific or industry-specific data formats
• Performance: Fast processing with minimal computational overhead
• Complementary Protection: Works alongside ML-based filters for comprehensive coverage

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When to Use Custom Data Protection vs. Standard Data Protection

• You have specific data formats that require exact pattern matching
• You need predictable, deterministic detection results
• You have unique identifiers or codes specific to your organisation
• You want to complement ML-based detection with rule-based patterns
• You need to meet specific compliance requirements with custom patterns

• You need intelligent detection of varied PII formats
• You want context-aware detection that understands data patterns
• You need to detect PII across multiple languages and formats
• You want ML-powered detection that adapts to new patterns

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Use Case: Financial Services with Custom Account Numbers

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Scenario

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Challenge

• Custom account number formats are detected and protected
• Internal reference codes are not exposed in AI responses
• Specific compliance patterns are matched exactly
• Detection is predictable and consistent across all interactions

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Solution: Implementing Custom Data Protection Filter

1. Custom Pattern Definition
   • Created regex pattern: ACC-\d{4}-\d{4}-\d{4} for account numbers
   • Added pattern for internal reference codes: REF-[A-Z]{2}\d{6}
   • Applied to both Input and Output for comprehensive protection
2. Deterministic Enforcement
   • Set to Mask behaviour to anonymise sensitive data while maintaining workflow continuity
   • Replaces detected patterns with appropriate placeholders (e.g., , )
3. Complementary Setup
   • Used alongside standard Data Protection Filter for comprehensive coverage
   • Custom patterns handle organisation-specific formats
   • ML-based filter handles standard PII types

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How to Use the Guardrail

Note: The steps below guide you through configuring the Custom Data Protection Filter using the Guardrail Setup.

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Step 1: Navigate to the Guardrail Setup

1. From the Home Page, open the AI System Dashboard by selecting View to open your AI system from the AI System Table.
2. In the guardrails section of the AI System Overview, click Edit Guardrails to launch the guardrail configuration workflow.

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Step 2: Select and Enable the Custom Data Protection Filter

1. In the Configure Guardrails page, a list of available guardrails will be displayed.
2. Click on Custom Data Protection to open its configuration options on the right-hand side of the screen.
3. Toggle the Enable Policy switch to ON to begin configuration.

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Step 3: Create Custom Regex Patterns

1. In the Custom Patterns section, click Add Pattern to create a new regex pattern.
2. Enter a Pattern Name (e.g., “Internal Account Numbers”, “Employee IDs”).
3. Enter the Regex Pattern using standard regex syntax (e.g., ACC-\d{4}-\d{4}-\d{4}).
4. Add an optional Description to explain what this pattern detects.
5. Click Save Pattern to add it to your configuration.

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Step 4: Set Application Scope

1. Under the Apply Guardrail To section, select where you want the guardrail enforced:
   • Input – Applies the selected behaviour to what users send to the model.
   • Output – Applies the selected behaviour to what the model returns as a response.
   • Both – Full bidirectional coverage

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Step 5: Configure Enforcement Behaviour

1. Under Select Guardrail Behaviour, choose how the system should respond to detected patterns:
   • Monitor – Lets you review input or output content without taking any action—used for observation and diagnostics.
   • Block – Automatically stops content from being processed if it violates the selected guardrail rules.
   • Mask – Replaces detected sensitive information with anonymised placeholders while allowing content to proceed.

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Step 6: Save, Test, and Apply the Guardrail

1. Click Save & Continue to store your custom patterns and configuration.
2. Go to the Test Guardrails step to evaluate how the guardrail behaves with your custom patterns.
3. After saving, you can proceed to the Summary section to review your configuration, save all changes, and view your AI System overview.

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Regex Pattern Examples

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Common Pattern Types

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Account Numbers

Pattern: ACC-\d{4}-\d{4}-\d{4}
Matches: ACC-1234-5678-9012
Description: Internal account number format

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Employee IDs

Pattern: EMP-[A-Z]{2}\d{6}
Matches: EMP-AB123456
Description: Employee identification format

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Internal Reference Codes

Pattern: REF-\d{3}[A-Z]{2}\d{4}
Matches: REF-123AB5678
Description: Internal reference code format

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Custom Serial Numbers

Pattern: SN-\d{2}[A-Z]\d{3}-\d{4}
Matches: SN-12A345-6789
Description: Product serial number format

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Best Practices for Regex Patterns

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Pattern Design Guidelines

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1. Be Specific and Precise

• Use exact patterns that match only your intended data formats
• Avoid overly broad patterns that might create false positives
• Test patterns thoroughly before deployment

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2. Consider Edge Cases

• Account for variations in formatting (spaces, dashes, case sensitivity)
• Test with real data samples to ensure accuracy
• Consider international formats if applicable

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3. Performance Optimisation

• Use efficient regex patterns to minimise processing time
• Avoid overly complex patterns that might impact performance
• Consider using anchors (^ and $) for exact matches when appropriate

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4. Documentation and Maintenance

• Provide clear descriptions for each pattern
• Document the expected format and use cases
• Regularly review and update patterns as requirements change

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Example Pattern Configurations

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Financial Services

Pattern Name: Customer Account Numbers
Regex: CUST-\d{8}
Description: 8-digit customer account numbers with CUST prefix
Test Cases: CUST-12345678, CUST-87654321

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Healthcare

Pattern Name: Patient Record IDs
Regex: PAT-\d{3}[A-Z]{2}\d{4}
Description: Patient record identifier format
Test Cases: PAT-123AB5678, PAT-456CD9012

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Government

Pattern Name: Case Reference Numbers
Regex: CASE-\d{4}-\d{4}-\d{4}
Description: Government case reference format
Test Cases: CASE-1234-5678-9012, CASE-9876-5432-1098

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Performance Considerations

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Regex vs. ML-Based Detection

• Speed: Very fast pattern matching
• Predictability: Deterministic results
• Resource Usage: Low computational overhead
• Accuracy: High precision for well-defined patterns
• Flexibility: Limited to predefined patterns

• Speed: Moderate processing time
• Predictability: Context-dependent results
• Resource Usage: Higher computational requirements
• Accuracy: High recall across varied formats
• Flexibility: Adapts to new patterns and contexts

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Optimisation Tips

1. Use Anchors: Start patterns with ^ and end with $ for exact matches
2. Avoid Greedy Quantifiers: Use *? instead of * when appropriate
3. Group Efficiently: Use non-capturing groups (?:...) when you don’t need to capture
4. Test Performance: Validate pattern performance with large datasets
5. Monitor Usage: Track pattern match rates and adjust as needed