Merge pull request #108506 from erhopf/tls-patches

American-Dipper · web-flow · commit 6eacbe046375 · 2020-03-20T14:54:42.000-07:00
[CogSvcs] Documentation updates for security + TLS
diff --git a/articles/cognitive-services/Anomaly-Detector/overview.md b/articles/cognitive-services/Anomaly-Detector/overview.md
@@ -14,6 +14,8 @@ ms.author: aahi
 
 # What is the Anomaly Detector API?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data with machine learning. The Anomaly Detector API adapts by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.
 
 ![Detect pattern changes in service requests](./media/anomaly_detection2.png)
@@ -22,7 +24,7 @@ Using the Anomaly Detector doesn't require any prior experience in machine learn
 
 ## Features
 
-With the Anomaly Detector, you can automatically detect anomalies throughout your time series data, or as they occur in real-time. 
+With the Anomaly Detector, you can automatically detect anomalies throughout your time series data, or as they occur in real-time.
 
 |Feature  |Description  |
 |---------|---------|
@@ -47,7 +49,7 @@ To run the Notebook, complete the following steps:
 1. Un-check the "public" option in the dialog box before completing the clone operation, otherwise your notebook, including any subscription keys, will be public.
 1. Click **Run on free compute**
 1. Select one of the notebooks.
-1. Add your valid Anomaly Detector API subscription key to the `subscription_key` variable. 
+1. Add your valid Anomaly Detector API subscription key to the `subscription_key` variable.
 1. Change the `endpoint` variable to your endpoint. For example: `https://westus2.api.cognitive.microsoft.com/anomalydetector/v1.0/timeseries/last/detect`
 1. On the top menu bar, click **Cell**, then **Run All**.
 
diff --git a/articles/cognitive-services/Computer-vision/Home.md b/articles/cognitive-services/Computer-vision/Home.md
@@ -17,6 +17,8 @@ ms.custom: seodec18
 
 # What is Computer Vision?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information, depending on the visual features you're interested in. For example, Computer Vision can determine if an image contains adult content, or it can find all of the human faces in an image.
 
 You can use Computer Vision in your application through a native SDK or by invoking the REST API directly. This page broadly covers what you can do with Computer Vision.
diff --git a/articles/cognitive-services/Content-Moderator/overview.md b/articles/cognitive-services/Content-Moderator/overview.md
@@ -17,6 +17,8 @@ ms.author: pafarley
 
 # What is Azure Content Moderator?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Azure Content Moderator is a cognitive service that checks text, image, and video content for material that is potentially offensive, risky, or otherwise undesirable. When this material is found, the service applies appropriate labels (flags) to the content. Your app can then handle flagged content in order to comply with regulations or maintain the intended environment for users. See the [Moderation APIs](#moderation-apis) section to learn more about what the different content flags indicate.
 
 ## Where it's used
@@ -73,4 +75,4 @@ As with all of the Cognitive Services, developers using the Content Moderator se
 
 ## Next steps
 
-Get started using the Content Moderator service by following the instructions in [Try Content Moderator on the web](quick-start.md).
+Get started using the Content Moderator service by following the instructions in [Try Content Moderator on the web](quick-start.md).
diff --git a/articles/cognitive-services/Custom-Vision-Service/home.md b/articles/cognitive-services/Custom-Vision-Service/home.md
@@ -16,6 +16,8 @@ ms.author: pafarley
 
 # What is Custom Vision?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent _classes_) to images, according to their visual characteristics. Unlike the [Computer Vision](https://docs.microsoft.com/azure/cognitive-services/computer-vision/home) service, Custom Vision allows you to determine the labels to apply.
 
 ## What it does
diff --git a/articles/cognitive-services/Face/Overview.md b/articles/cognitive-services/Face/Overview.md
@@ -15,6 +15,8 @@ ms.author: pafarley
 
 # What is the Azure Face service?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 The Azure Cognitive Services Face service provides algorithms that are used to detect, recognize, and analyze human faces in images. The ability to process human face information is important in many different software scenarios. Example scenarios are security, natural user interface, image content analysis and management, mobile apps, and robotics.
 
 The Face service provides several different functions. Each function is outlined in the following sections. Read on to learn more about them.
diff --git a/articles/cognitive-services/Face/QuickStarts/CSharp.md b/articles/cognitive-services/Face/QuickStarts/CSharp.md
@@ -74,11 +74,6 @@ Add the following code to the **Main** method of the **Program** class. This cod
 ```csharp
         static void Main(string[] args)
         {
-
-            // Explicitly set TLS 1.2.
-            ServicePointManager.SecurityProtocol = ServicePointManager.SecurityProtocol |
-                SecurityProtocolType.Tls12;
-
             // Get the path and filename to process from the user.
             Console.WriteLine("Detect faces:");
             Console.Write(
diff --git a/articles/cognitive-services/LUIS/what-is-luis.md b/articles/cognitive-services/LUIS/what-is-luis.md
@@ -7,6 +7,8 @@ ms.date: 02/23/2020
 
 # What is Language Understanding (LUIS)?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
 
 A client application for LUIS is any conversational application that communicates with a user in natural language to complete a task. Examples of client applications include social media apps, chat bots, and speech-enabled desktop applications.
@@ -124,4 +126,4 @@ Samples using LUIS:
 [flow]: https://docs.microsoft.com/connectors/luis/
 [authoring-apis]: https://go.microsoft.com/fwlink/?linkid=2092087
 [endpoint-apis]: https://go.microsoft.com/fwlink/?linkid=2092356
-[qnamaker]: https://qnamaker.ai/
+[qnamaker]: https://qnamaker.ai/
diff --git a/articles/cognitive-services/QnAMaker/Overview/overview.md b/articles/cognitive-services/QnAMaker/Overview/overview.md
@@ -15,6 +15,8 @@ ms.author: diberry
 
 # What is the QnA Maker service?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../../includes/cognitive-services-tls-announcement.md)]
+
 QnA Maker is a cloud-based Natural Language Processing (NLP) service that easily creates a natural conversational layer over your data. It can be used to find the most appropriate answer for any given natural language input, from your custom knowledge base (KB) of information.
 
 A client application for QnA Maker is any conversational application that communicates with a user in natural language to answer a question. Examples of client applications include social media apps, chat bots, and speech-enabled desktop applications.
diff --git a/articles/cognitive-services/Speech-Service/overview.md b/articles/cognitive-services/Speech-Service/overview.md
@@ -14,6 +14,8 @@ ms.author: dapine
 
 # What is the Speech service?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 The Speech service is the unification of speech-to-text, text-to-speech, and speech-translation into a single Azure subscription. It's easy to speech enable your applications, tools, and devices with the [Speech SDK](speech-sdk-reference.md), [Speech Devices SDK](https://aka.ms/sdsdk-quickstart), or [REST APIs](rest-apis.md).
 
 > [!IMPORTANT]
diff --git a/articles/cognitive-services/Speech-Service/speech-to-text.md b/articles/cognitive-services/Speech-Service/speech-to-text.md
@@ -14,6 +14,8 @@ ms.author: dapine
 
 # What is speech-to-text?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Speech-to-text from the Speech service, also known as speech recognition, enables real-time transcription of audio streams into text. Your applications, tools, or devices can consume, display, and take action on this text as command input. This service is powered by the same recognition technology that Microsoft uses for Cortana and Office products. It seamlessly works with the <a href="./speech-translation.md" target="_blank">translation <span class="docon docon-navigate-external x-hidden-focus"></span></a> and <a href="./text-to-speech.md" target="_blank">text-to-speech <span class="docon docon-navigate-external x-hidden-focus"></span></a> service offerings. For a full list of available speech-to-text languages, see [supported languages](language-support.md#speech-to-text).
 
 The speech-to-text service defaults to using the Universal language model. This model was trained using Microsoft-owned data and is deployed in the cloud. It's optimal for conversational and dictation scenarios. When using speech-to-text for recognition and transcription in a unique environment, you can create and train custom acoustic, language, and pronunciation models. Customization is helpful for addressing ambient noise or industry-specific vocabulary.
diff --git a/articles/cognitive-services/Speech-Service/speech-translation.md b/articles/cognitive-services/Speech-Service/speech-translation.md
@@ -14,6 +14,8 @@ ms.author: erhopf
 
 # What is speech translation?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Speech translation from the Speech service enables real-time, multi-language speech-to-speech and speech-to-text translation of audio streams. With the Speech SDK, your applications, tools, and devices have access to source transcriptions and translation outputs for provided audio. Interim transcription and translation results are returned as speech is detected, and finals results can be converted into synthesized speech.
 
 Microsoft's translation engine is powered by two different approaches: statistical machine translation (SMT) and neural machine translation (NMT). SMT uses advanced statistical analysis to estimate the best possible translations given the context of a few words. With NMT, neural networks are used to provide more accurate, natural-sounding translations by using the full context of sentences to translate words.
diff --git a/articles/cognitive-services/Speech-Service/text-to-speech.md b/articles/cognitive-services/Speech-Service/text-to-speech.md
@@ -14,13 +14,15 @@ ms.author: erhopf
 
 # What is text-to-speech?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Text-to-speech from the Speech service enables your applications, tools, or devices to convert text into human-like synthesized speech. Choose from standard and neural voices, or create a custom voice unique to your product or brand. 75+ standard voices are available in more than 45 languages and locales, and 5 neural voices are available in a select number of languages and locales. For a full list of supported voices, languages, and locales, see [supported languages](language-support.md#text-to-speech).
 
 > [!NOTE]
 > Bing Speech was decommissioned on October 15, 2019. If your applications, tools, or products are using the Bing Speech APIs or Custom Speech, we've created guides to help you migrate to the Speech service.
 > - [Migrate from Bing Speech to the Speech service](how-to-migrate-from-bing-speech.md)
 
-## Core features 
+## Core features
 
 * Speech synthesis - Use the [Speech SDK](quickstarts/text-to-speech-audio-file.md) or [REST API](rest-text-to-speech.md) to convert text-to-speech using standard, neural, or custom voices.
 
@@ -49,7 +51,7 @@ Sample code for text-to-speech is available on GitHub. These samples cover text-
 - [Text-to-speech samples (SDK)](https://github.com/Azure-Samples/cognitive-services-speech-sdk)
 - [Text-to-speech samples (REST)](https://github.com/Azure-Samples/Cognitive-Speech-TTS)
 
-## Customization 
+## Customization
 
 In addition to standard and neural voices, you can create and fine-tune custom voices unique to your product or brand. All it takes to get started are a handful of audio files and the associated transcriptions. For more information, see [Get started with Custom Voice](how-to-custom-voice.md)
 
diff --git a/articles/cognitive-services/cognitive-services-security.md b/articles/cognitive-services/cognitive-services-security.md
@@ -17,10 +17,10 @@ Security should be considered a top priority when developing any and all applica
 
 ## Transport Layer Security (TLS)
 
-All of the Cognitive Services endpoints exposed over HTTP enforce TLS 1.2. With an enforced security protocol, consumers attempting to call a Cognitive Services endpoint should adhere to guidelines:
+All of the Cognitive Services endpoints exposed over HTTP enforce TLS 1.2. With an enforced security protocol, consumers attempting to call a Cognitive Services endpoint should adhere to these guidelines:
 
-* The client Operating System (OS) would need to support TLS 1.2
-* The language (and platform) used to make the HTTP call would need to specify TLS 1.2 as part of the request
+* The client Operating System (OS) needs to support TLS 1.2
+* The language (and platform) used to make the HTTP call need to specify TLS 1.2 as part of the request
   * Depending on the language and platform, specifying TLS is done either implicitly or explicitly
 
 For .NET users, consider the <a href="https://docs.microsoft.com/dotnet/framework/network-programming/tls" target="_blank">Transport Layer Security best practices <span class="docon docon-navigate-external x-hidden-focus"></span></a>.
diff --git a/articles/cognitive-services/form-recognizer/overview.md b/articles/cognitive-services/form-recognizer/overview.md
@@ -15,6 +15,8 @@ ms.author: pafarley
 
 # What is Form Recognizer?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Azure Form Recognizer is a cognitive service that uses machine learning technology to identify and extract text, key/value pairs and table data from form documents. It ingests text from forms and outputs structured data that includes the relationships in the original file. You quickly get accurate results that are tailored to your specific content without heavy manual intervention or extensive data science expertise. Form Recognizer is comprised of custom models, the prebuilt receipt model, and the layout API. You can call Form Recognizer models by using a REST API to reduce complexity and integrate it into your workflow or application.
 
 Form Recognizer is made up of the following services:
@@ -46,7 +48,7 @@ Form Recognizer also includes a model for reading English sales receipts from th
 
 ## Layout API
 
-Form Recognizer can also extract text and table structure (the row and column numbers associated with the text) using high-definition optical character recognition (OCR). 
+Form Recognizer can also extract text and table structure (the row and column numbers associated with the text) using high-definition optical character recognition (OCR).
 
 ## Get started
 
@@ -56,9 +58,9 @@ Follow a quickstart to get started extracting data from your forms. We recommend
   * Train without labels
     * [Quickstart: Train a Form Recognizer model and extract form data by using the REST API with cURL](quickstarts/curl-train-extract.md)
     * [Quickstart: Train a Form Recognizer model and extract form data by using the REST API with Python](quickstarts/python-train-extract.md)
-  * Train with labels 
+  * Train with labels
     * [Train a Form Recognizer model with labels using the sample labeling tool](quickstarts/label-tool.md)
-    * [Train a Form Recognizer model with labels using REST API and Python](quickstarts/python-labeled-data.md) 
+    * [Train a Form Recognizer model with labels using REST API and Python](quickstarts/python-labeled-data.md)
 * Prebuilt receipts - extract data from USA sales receipts
   * [Quickstart: Extract receipt data using cURL](quickstarts/curl-receipts.md)
   * [Quickstart: Extract receipt data using Python](quickstarts/python-receipts.md)
@@ -89,7 +91,7 @@ The input requirements for the receipt model are slightly different.
 
 * Format must be JPEG, PNG, BMP, PDF (text or scanned) or TIFF.
 * File size must be less than 20 MB.
-* Image dimensions must be between 50 x 50 pixels and 10000 x 10000 pixels. 
+* Image dimensions must be between 50 x 50 pixels and 10000 x 10000 pixels.
 * PDF dimensions must be at most 17 x 17 inches, corresponding to Legal or A3 paper sizes and smaller.
 * For PDF and TIFF, only the first 200 pages are processed (with a free tier subscription, only the first two pages are processed).
 
diff --git a/articles/cognitive-services/immersive-reader/overview.md b/articles/cognitive-services/immersive-reader/overview.md
@@ -16,6 +16,8 @@ ms.author: metan
 
 # What is Immersive Reader?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 The [Immersive Reader](https://www.onenote.com/learningtools) is an inclusively designed tool that implements proven techniques to improve reading comprehension for emerging readers, language learners, and people with learning differences such as dyslexia.
 
 You can use Immersive Reader in your web application by using the Immersive Reader SDK.
@@ -58,4 +60,4 @@ Get started with Immersive Reader:
 
 * Jump into the [quickstart](./quickstart.md)
 * Explore the [Immersive Reader SDK on GitHub](https://github.com/microsoft/immersive-reader-sdk)
-* Read the [Immersive Reader SDK Reference](./reference.md)
+* Read the [Immersive Reader SDK Reference](./reference.md)
diff --git a/articles/cognitive-services/personalizer/what-is-personalizer.md b/articles/cognitive-services/personalizer/what-is-personalizer.md
@@ -15,6 +15,8 @@ ms.author: diberry
 
 # What is Personalizer?
 
+[!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
+
 Azure Personalizer is a cloud-based API service that helps your client application choose the best, single _content_ item to show each user. The service selects the best item, from content items, based on collective real-time information you provide about content and context.
 
 After you present the content item to your user, your system monitors user behavior and reports a reward score back to Personalizer to improve its ability to select the best content based on the context information it receives.
diff --git a/includes/cognitive-services-tls-announcement.md b/includes/cognitive-services-tls-announcement.md
@@ -0,0 +1,10 @@
+---
+author: erhopf
+ms.author: erhopf
+ms.service: cognitive-services
+ms.topic: include
+ms.date: 03/20/2020
+---
+
+> [!IMPORTANT]
+> TLS 1.2 is now enforced for all HTTP requests to this service. For more information, see [Azure Cognitive Services security](../articles/cognitive-services/cognitive-services-security.md).
