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Inference protection for LLMs: Keeping sensitive data out of AI workflows

Inference protection is a preventive approach to LLM privacy that stops sensitive data from ever reaching AI models. Learn how de-identification enables secure, compliant AI workflows with unstructured text.

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Blog posts

Inference protection for LLMs: Keeping sensitive data out of AI workflows

Generative AI
Data privacy
Tonic Textual

How to de-identify financial documents with Tonic Textual

Data privacy
Generative AI
Financial services
Tonic Textual

Tonic Structural vs Informatica: Which is better for Test Data Management?

Test data management
Test data management
Data de-identification
Tonic Structural
Tonic Fabricate

Informatica Test Data Management pros and cons: a complete guide

Test data management
Data de-identification
Tonic Structural
Tonic Fabricate

How to maximize HEDIS scores with synthetic data

Data de-identification
Data privacy
Healthcare
Tonic Structural
Tonic Textual
Tonic Fabricate

How to mitigate the risk of a data breach in non-production environments

Data privacy
Data de-identification
Tonic Fabricate
Tonic Structural
Tonic Textual

Introducing the Unstructured Data Catalog: From unknown text to usable data

Product updates
Tonic Textual

Data masking: DIY internal scripts or time to buy?

Data de-identification
Tonic Structural

How data masking & synthesis support Zero Trust

Data privacy
Data de-identification
Data synthesis
Tonic Structural
Tonic Fabricate
Tonic Textual

How synthetic data can help solve AI’s data crisis

Data synthesis
Data privacy
Generative AI
Tonic Structural
Tonic Fabricate
Tonic Textual

Healthcare’s blind spot: What happens after our data is shared?

Data privacy
Healthcare
Tonic.ai editorial
Tonic Textual

Tonic.ai product updates: January 2026

Product updates
Product updates
Tonic.ai editorial
Tonic Fabricate
Tonic Structural
Tonic Textual

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