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articles/ai-services/language-service/personally-identifiable-information/overview.md

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# What is Personally Identifiable Information (PII) detection in Azure AI Language?
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As of June 2024, we now provide General Availability support for the Conversational PII service (English-language only).
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Customers can now redact transcripts, chats, and other text written in a conversational style (i.e. text with “um”s, “ah”s, multiple speakers, and the spelling out of words for more clarity) with better confidence in AI quality, Azure SLA support and production environment support, and enterprise-grade security in mind.
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PII detection is one of the features offered by [Azure AI Language](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The PII detection feature can **identify, categorize, and redact** sensitive information in unstructured text. For example: phone numbers, email addresses, and forms of identification. Azure AI Language supports general text PII redaction, as well as [Conversational PII](how-to-call-for-conversations.md), a specialized model for handling speech transcriptions and the more informal, conversational tone of meeting and call transcripts. The service also supports [Native Document PII redaction](#native-document-support), where the input and output are structured document files.
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## What's new
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The Text PII and Conversational PII detection preview API (version `2024-11-15-preview`) now supports the option to mask detected sensitive entities with a label beyond just redaction characters. Customers have the option to specify if personally identifiable information content such as names and phone numbers, i.e. `“John Doe received a call from 424-878-9192”`, are masked with a redaction character, i.e. `“******** received a call from ************”`, or masked with an entity label, i.e. `“[PERSON_1] received a call from [PHONENUMBER_1]”`. More on how to specify the redaction policy style for your outputs can be found in our [how-to guides](how-to-call.md).
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The Conversational PII detection models (both version `2024-11-01-preview` and `GA`) have been updated to provide enhanced AI quality and accuracy. The numeric identifier entity type now also includes Drivers License and Medicare Beneficiary Identifier.
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As of June 2024, we now provide General Availability support for the Conversational PII service (English-language only). Customers can now redact transcripts, chats, and other text written in a conversational style (i.e. text with “um”s, “ah”s, multiple speakers, and the spelling out of words for more clarity) with better confidence in AI quality, Azure SLA support and production environment support, and enterprise-grade security in mind.
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> [!TIP]
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> Try out PII detection [in AI Foundry portal](https://ai.azure.com/explore/language), where you can [utilize a currently existing Language Studio resource or create a new AI Foundry resource](../../../ai-studio/ai-services/connect-ai-services.md)
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articles/ai-services/language-service/summarization/region-support.md

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|------------------|----------------------------------|-----------------------------------------------|
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|US Gov Virginia |✅ |✅ |
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|US Gov Arizona |✅ |✅ |
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|North Europe |✅ |✅ |
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|Australia East |✅ |✅ |
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|Canada Central |✅ |✅ |
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|Central US |✅ |✅ |
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|China North 3 |✅ |✅ |
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|East US |✅ |✅ |
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|East US 2 |✅ |✅ |
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|Central US |✅ |✅ |
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|France Central |✅ |✅ |
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|Italy North |✅ |✅ |
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|Japan East |✅ |✅ |
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|North Central US |✅ |✅ |
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|North Europe |✅ |✅ |
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|South Central US |✅ |✅ |
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|West US |✅ |✅ |
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|West US 2 |✅ |✅ |
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|South UK |✅ |✅ |
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|Southeast Asia |✅ |✅ |
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|Switzerland North |✅ |✅ |
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|USNat East |✅ |✅ |
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|USNat West |✅ |✅ |
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|USSec East |✅ |✅ |
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|USSec West |✅ |✅ |
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|South UK |✅ |✅ |
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|Southeast Asia |✅ |✅ |
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|Australia East |✅ |✅ |
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|France Central |✅ |✅ |
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|Japan East |✅ |✅ |
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|North Central US |✅ |✅ |
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|Central Sweden |✅ |✅ |
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|Switzerland North |✅ |✅ |
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|West Europe |✅ |✅ |
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|Italy North |✅ |✅ |
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|China North 3 |✅ |✅ |
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|West US |✅ |✅ |
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|West US 2 |✅ |✅ |
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## Next steps
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articles/ai-services/openai/how-to/business-continuity-disaster-recovery.md

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The additional benefit of this architecture is that it allows you to stack Standard deployments with Provisioned Deployments so that you can dial in your preferred level of performance and resiliency. This allows you to use PTU for your baseline demand across workloads and leverage pay-as-you-go for spikes in traffic.
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:::image type="content" source="../how-to/media/disaster-recovery/recovery.jpg" alt-text="Failover architectural diagram." lightbox="../how-to/media/disaster-recovery/recovery.jpg":::
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:::image type="content" source="../how-to/media/disaster-recovery/scaling.jpg" alt-text="Provisioned scaling diagram." lightbox="../how-to/media/disaster-recovery/scaling.jpg":::
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## Supporting Infrastructure
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Organizations consuming the service through the Microsoft public backbone should consider the following design elements:
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1. The Generative AI Gateway should be deployed in manner that ensures it's available in the event of an Azure regional outage. If using APIM (Azure API Management), this can be done by deploying separate APIM instances in multiple regions or using the [multi-region gateway feature of APIM](/azure/api-management/api-management-howto-deploy-multi-region).
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1. A public global server load balancer should be used to load balance across the multiple Generative AI Gateway instances in either an active/active or active/passive manner. [Azure FrontDoor](/azure/traffic-manager/traffic-manager-routing-methods) can be used to fulfill this role depending on the organization’s requirements.
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:::image type="content" source="../how-to/media/disaster-recovery/scaling.jpg" alt-text="Provisioned scaling diagram." lightbox="../how-to/media/disaster-recovery/scaling.jpg":::
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:::image type="content" source="../how-to/media/disaster-recovery/recovery.png" alt-text="Failover architectural diagram." lightbox="../how-to/media/disaster-recovery/recovery.png":::
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### Designing for consumption through the private networking
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