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articles/ai-services/content-safety/how-to/containers/container-overview.md

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keywords: on-premises, Docker, container
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# Content Safety containers overview
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# Content Safety containers overview (preview)
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Containers let you use a subset of the Azure AI Content Safety features in your own environment. With content safety containers, you can build a content safety application architecture optimized for both robust cloud capabilities and edge locality. Containers help you meet specific security and data governance requirements.
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articles/ai-services/content-safety/how-to/containers/image-container.md

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keywords: on-premises, Docker, container
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# Analyze image content with docker containers
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# Analyze image content with docker containers (preview)
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The analyze image container scans images for sexual content, violence, hate, and self-harm with multi-severity levels. This guide shows how to download, install, and run a content safety image container.
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articles/ai-services/content-safety/how-to/containers/install-run-container.md

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keywords: on-premises, Docker, container
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# Install and run content safety containers with Docker
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# Install and run content safety containers with Docker (preview)
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By using containers, you can use a subset of the content safety service features in your own environment. In this article, you learn how to download, install, and run a content safety container.
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articles/ai-services/content-safety/how-to/containers/text-container.md

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keywords: on-premises, Docker, container
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# Analyze text content with Docker container
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# Analyze text content with Docker container (preview)
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The analyze text container scans text generated by foundation models or people for sexual content, violence, hate, and self-harm with multi-severity levels. This guide shows you how to download, install, and run a content safety analyze text container.
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articles/ai-services/content-safety/how-to/embedded-content-safety.md

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ms.author: zhanxia
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# Embedded content safety
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# Embedded content safety (preview)
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Embedded content safety is designed for on-device scenarios where cloud connectivity is intermittent or prefer on-device for privacy reason. For example, you can use embedded content safety in a PC to detect harmful content generated by foundation model, or a car that might travel out of range. You can also develop hybrid cloud and offline solutions. For scenarios where your devices must be in a secure environment like a bank or government entity, you should first consider [disconnected containers](../../containers/disconnected-containers.md).
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articles/ai-services/content-safety/quickstart-multimodal.md

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# zone_pivot_groups: programming-languages-content-safety
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# Quickstart: Analyze multimodal content (Preview)
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# Quickstart: Analyze multimodal content (preview)
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The Multimodal API analyzes materials containing both image content and text content to help make applications and services safer from harmful user-generated or AI-generated content. Analyzing an image and its associated text content together can preserve context and provide a more comprehensive understanding of the content.
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articles/ai-services/content-safety/quickstart-protected-material-code.md

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# Quickstart: Protected material detection for code
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# Quickstart: Protected material detection for code (preview)
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The Protected Material for Code feature provides a comprehensive solution for identifying and managing the risks associated with AI-generated code. By detecting and preventing the display of protected code from existing GitHub repositories, organizations can ensure compliance with intellectual property regulations, uphold code originality, and safeguard their reputations. Protected material refers to code that closely matches content from known GitHub repositories, including software libraries, source code, algorithms, and other proprietary programming content.
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articles/ai-services/content-safety/quickstart-protected-material.md

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ms.author: pafarley
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# Quickstart: Protected material detection for text
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# Quickstart: Protected material detection for text (preview)
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The Protected Material for Text feature provides a comprehensive solution for managing risks associated with AI-generated content (English content only). By detecting and preventing the display of protected material, organizations can ensure compliance with intellectual property laws, maintain content originality, and protect their reputations. Protected material refers to content that matches known text from copyrighted sources, such as song lyrics, articles, recipes, or other selected web content.
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articles/ai-services/content-safety/toc.yml

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- name: Prompt Shields
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- name: Groundedness detection
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- name: Groundedness detection (preview)
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- name: Protected material detection
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- name: Protected material detection (preview)
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- name: Custom categories (preview)
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- name: Groundedness detection (preview)
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- name: Protected material detection for text
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- name: Protected material detection for text (preview)
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- name: Protected material detection for code
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- name: Protected material detection for code (preview)
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- name: Custom categories (preview)
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- name: Mitigate false results
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- name: Containers
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- name: Containers (preview)
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- name: Content Safety containers overview
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- name: Image analysis container
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- name: Embedded Content Safety
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- name: Embedded Content Safety (preview)
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href: how-to/embedded-content-safety.md
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- name: Encryption of data at rest
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href: how-to/encrypt-data-at-rest.md

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