Transparency Note for Microsoft Copilot (for individuals)

August 18, 2026

What is a Transparency Note?

An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Microsoft's Transparency Notes are intended to help you understand how the AI technology behind Copilot works, the choices we made that influence the system's performance and behavior, and the importance of thinking about the whole system, so that users of Copilot can take control of their own experiences and understand the steps we are taking to provide a safe and secure product.

Microsoft's Transparency Notes are part of a broader effort at Microsoft to put our AI Principles into practice. To find out more, see the Microsoft AI Principles.

The basics of Microsoft Copilot

Introduction

Microsoft Copilot helps you move from idea to finished work, all in one place. Copilot is integrated into Word, Excel, PowerPoint, Outlook, Teams, and more, bringing the files and information you use into one experience. Copilot can help you create polished documents, presentations, and spreadsheets, draft emails, organize your day, and get answers grounded in the work you’re doing. With Cowork, Copilot can also help with bigger, multi-step tasks and deliver completed to-dos. You can also choose between leading AI models for different kinds of work. Specific features vary by subscription.

At Microsoft, we take our commitment to responsible AI seriously. Copilot has been developed in line with Microsoft's AI Principles, Microsoft's Responsible AI Standard, and in partnership with responsible AI experts across the company, including Microsoft's Office of Responsible AI, our engineering teams, Microsoft Research, and Aether. You can learn more at Responsible AI at Microsoft.

In this document, we describe our approach to responsible AI for Copilot. Ahead of release, we leveraged Microsoft's state-of-the-art methods to map, measure, and manage potential risks and misuse of the system and to secure its benefits for users. As we have continued to evolve Copilot, we have also continued to learn and improve our responsible AI efforts. This document will be updated periodically to communicate our evolving processes and methods.

Key terms

Classifiers Machine learning models that help to sort data into labelled classes or categories of information. In Copilot, one way in which we use classifiers is to help detect potentially harmful content submitted by users or generated by the system to mitigate generation of that content and misuse or abuse of the system.

Grounding For certain conversations where users seek information, Copilot is grounded in web search results. This means that Copilot centers its response on high-ranking content from the web and provides hyperlinked citations following generated text responses or in conversation transcripts in voice mode.

Large language models (LLMs) Large language models (LLMs) in this context are AI models that are trained on large amounts of text data to predict words in sequences. LLMs can perform a variety of tasks, such as text generation, summarization, translation, classification, and more.

Mitigation A method or combination of methods designed to reduce potential risks that may arise from using the AI features within Copilot.

Multi-modal models (MMMs) Multi-modal models (MMMs) are AI models that are trained on different types of data, such as text, images, or audio. These models can perform a variety of tasks, such as writing text, describing images, recognizing speech, and finding information across different types of data.

Prompts Inputs in the form of text, images, and/or audio that a user sends to Copilot to interact with the AI features within Copilot.

Red teaming Techniques used by experts to assess the limitations and vulnerabilities of a system and to test the effectiveness of planned mitigations. Red team testing includes testers adopting both benign and adversarial personas to identify potential risks and are distinct from systematic measurement of risks.

Responses Text, images, or audio that Copilot outputs in response to a prompt or as part of the back and forth with the user. Synonyms for "response" include "completion," "generation, and "answer."

Small language models (SLMs) Small language models (SLMs) in this context are AI models that are trained on smaller, more focused amounts of data compared to large language models. Despite their smaller size, SLMs can perform a variety of tasks, such as text generation, summarization, translation, and classification. While they may not match the extensive capabilities of LLMs, SLMs are often more resource efficient and can be highly effective for specific, targeted applications.

System Message The system message (sometimes referred to as a "metaprompt") is a program that serves to guide the system's behavior. Parts of the system message help align system behavior with Microsoft’s AI Principles and user expectations. For example, a system message may include a line such as "do not provide information or create content that could cause physical, emotional, or financial harm."

Capabilities

System behavior

With Copilot, we've developed an innovative approach to bring a more personalized AI experience to users for an engaging experience that can help users with a variety of tasks. This innovative approach leverages a variety of advanced technologies, such as language and multi-modal models from Microsoft, OpenAI, and other model developers. We worked on the implementation of safety techniques for the models underlying Copilot prior to public release to develop a customized set of capabilities and behaviors that provide an enhanced Copilot experience. In Copilot, users can send prompts in natural language text or voice. Responses are presented to users in several different formats, such as chat responses in text form (with traditional links to web content as necessary) and images (if an image request was made as part of the prompt). If users send prompts in natural language voice within the Copilot Voice mode, they will receive audio responses.

When a user enters a prompt in Copilot, the prompt, conversation history, and the system message are sent through several input classifiers to help filter out harmful or inappropriate content. This is a crucial first step for helping to improve model performance and mitigate situations in which users might attempt to prompt the model in a way that could be unsafe. Once the prompt passes through the input classifiers, a determination is made if the request requires grounding data from the web and which language model should respond to the request. All models generate a response using the user's prompt and recent conversation history to contextualize the request, the system message to align responses with Microsoft’s AI Principles and user expectations, and if appropriate, align responses with search results to ground responses in existing, high-ranking content from the web.

Responses are presented to users in several different formats, such as chat responses in text form, traditional links to web content, images, and audio responses. When responses are provided in the form of text—and the responses are grounded in data from the web—the output contains hyperlinked citations listed below the text so users can access the website(s) that were used to ground the response and learn more about the topic from there. Copilot can also run code to complete complex calculations and generate graphs. Copilot can store specific facts users request it to remember, allowing it to generate more relevant responses and suggestions based on that context. Copilot can also delete saved facts when users ask to forget them explicitly.

Copilot also helps users to create new stories, poems, song lyrics, and images. When Copilot detects user intent to generate creative content (such as a user prompt that begins with "write me a …"), the system will, in most cases, generate content responsive to the user's prompt. Similarly, when Copilot detects user intent to generate an image (such as a user prompt that begins with "draw me a …"), Copilot will, in most cases, generate an image responsive to the user's prompt. When Copilot detects user intent to modify an uploaded image (such as a user prompt that begins with "add a …"), Copilot will, in most cases, modify an image responsive to the user's prompt. Copilot may not respond with creative content when the user prompt contains certain terms that could result in problematic content.

Intended safety behavior

Our goal for Copilot is to be helpful to users, and, we implement safeguards to help reduce the likelihood of generating harmful content and increase the likelihood of a safe and positive user experience. While we have taken steps to mitigate risks, generative AI models like those behind Copilot are probabilistic and can make mistakes, meaning mitigations may occasionally fail to block harmful user prompts or AI-generated responses. If you encounter harmful or unexpected content while using Copilot, let us know by providing feedback so we can continue to improve the experience.

Use cases

Intended uses

Copilot is designed to help users answer questions, generate content, and complete tasks through natural language using text, images, and voice. To provide helpful and relevant responses, Copilot may retrieve, analyze, and summarize information from available sources. Some advanced features may be available only to users with eligible subscriptions. Examples of common use cases include the following:

• Chat using text and get web-based summaries. Users can chat with Copilot via text and ask follow-up questions to find new information and receive support across a wide variety of topics. Copilot can perform web searches and will use the top web search results to generate a summary of the information to present to users. These summaries include citations to webpages to help users review the sources for search results that helped ground Copilot's summary. Users can click on these links to visit the website source to learn more or evaluate the reliability of the website.