Anonymization Strategies

Overview

The Anonymization Strategy feature helps protect sensitive information by transforming Personally Identifiable Information (PII) before it is stored and used by AI-powered applications.

When documents are uploaded to the AI Content screen, the selected anonymization strategy is applied automatically. The protected content is then processed, indexed, and stored for retrieval.

This ensures that AI responses are generated using anonymized data rather than exposing original sensitive information.

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Supported Anonymization Strategies

The platform supports the following anonymization methods:

Strategy	Description
Replacement (Default)	Replaces sensitive information with redacted placeholders.
Masking	Hides sensitive data using masking characters such as asterisks (*).
Encryption	Encrypts sensitive data before storage.
Format-Preserving Encryption (FPE)	Encrypts data while maintaining the original format and structure.
Hashing	Converts sensitive information into irreversible hash values.
Custom Scrambler	Rearranges characters or digits to obscure the original value.
Pseudonymization	Replaces sensitive values with realistic substitute values.

Note: If no strategy is selected, the system automatically applies the Replacement strategy.

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Where to Configure Anonymization Strategies

Anonymization strategies can be configured within:

• Basic Mode

• AI Content

• Publish Configuration

The selected strategy is applied during document processing.

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Applying an Anonymization Strategy

Steps

1. Navigate to the AI-Content screen.

Select the desired Anonymization Strategy out of 7 available strategies,

Click Save.

Upload the document.

Publish the content and test the bot.

Query the bot using information from the uploaded document.

The bot will return responses based on the anonymized version of the content.

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Example: Masking Strategy

Original Content

Plain Text1Name: John Smith2Phone Number: 9876543210Show more lines

Anonymized Content

Plain Text1Name: J*** S****2Phone Number: ******3210

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Benefits

• Protects existing conversation history.

• Supports privacy and regulatory requirements.

• Eliminates manual redaction efforts.

• Provides automated scheduling options.

• Ensures historical data remains protected.

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Conclusion

The Anonymization Strategies feature provides a flexible and effective approach to protecting sensitive information throughout the AI knowledge ingestion and retrieval process. By offering multiple anonymization methods such as Replacement, Masking, Encryption, FPE, Hashing, Custom Scrambler, and Pseudonymization, organizations can choose the strategy that best aligns with their security, privacy, and compliance requirements.

When documents are uploaded, the selected strategy is automatically applied before content is processed, indexed, and stored. This ensures that sensitive information remains protected while still enabling users to leverage AI-powered search and conversational experiences.

By safeguarding data at the source, Anonymization Strategies help organizations enhance data privacy, reduce the risk of exposing confidential information, support regulatory compliance, and maintain trust in AI-driven interactions.