What’s new in Azure Language in Foundry Tools

· Source: Microsoft Foundry Blog articles · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Intermediate, short

Summary

Microsoft announced significant updates to Azure Language in Foundry Tools at Microsoft Build, enhancing capabilities for identifying and managing sensitive information across text, conversations, and healthcare scenarios. Key releases include a generally available (GA) Text PII API (2026-05-01) with advanced redaction options like "syntheticReplacement" for anonymization, optional type validation controls, and configurable confidence thresholds. New preview releases for Text PII and Conversational PII expand entity detection to include items like Password, PIN code, Zip code, Airport code, GitHub account identifiers, and credit card expiration dates. Additionally, Microsoft introduced Foundry playgrounds for Text PII, Conversational PII, and Text Analytics for Health, allowing teams to explore and evaluate API functionalities before integration. These updates aim to automate privacy protection and streamline data processing for various application workflows.

Key takeaway

For AI Engineers or Data Privacy Officers building applications that handle sensitive information, these Azure Language in Foundry Tools updates offer critical capabilities. You can now automate PII detection and redaction more precisely using the GA Text PII API's new customization features, including "syntheticReplacement" for anonymization. Utilize the new Microsoft Foundry playgrounds to thoroughly evaluate API performance and integration suitability before deploying privacy-aware solutions, ensuring compliance and data integrity.

Key insights

Azure Language in Foundry Tools now offers enhanced PII detection and redaction with new APIs and interactive playgrounds.

Principles

Method

The GA Text PII API allows configuring anonymization via "syntheticReplacement", disabling entity type validation, and setting confidence score thresholds for output control.

In practice

Topics

Best for: CTO, AI Architect, Machine Learning Engineer, AI Engineer, Software Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Microsoft Foundry Blog articles.