The article describes how genAI and, above all, Microsoft Copilot AI worked. The aim is to take possible options into account when designing the solution architecture and approach in order to achieve the desired result later on. This is because Copilot uses some functions in M365 to generate its answers—and that brings some special challenges with it.
How does Copilot work in Microsoft 365? Data flow of a prompt
Microsoft 365 Copilot is not only a powerful tool for increased productivity, but also a secure and compliant solution. With its advanced data protection and governance features, Copilot ensures that data remains within the boundaries of the Microsoft 365 service and is protected in accordance with existing security, compliance, and privacy policies. The same applies to the semantic index.
The semantic index for Copilot is a feature that helps AI understand context and deliver more accurate results. It builds on the keyword matching, personalization, and social matching features in Microsoft 365 by creating vectorized indexes to enable conceptual understanding. This means that, unlike traditional methods for queries based on exact matches or predefined criteria, the semantic index for Copilot finds the most similar or relevant data based on semantic or contextual meaning, rather than just keywords.
Source and further details: Semantic indexing for Microsoft 365 Copilot and YouTube video from Microsoft Mechanics: How Microsoft 365 Copilot works | Timestamp 139 seconds. Also, check out Michael Bargury's blog post titled: Copilot Vulnerable to RCE. To explain how the RCE hack works, he explains how Copilot works under the hood.
How exactly does the data flow work?
- Starting point: Entering the prompt
- The user enters a prompt in a Microsoft 365 app (e.g., Teams, Word, Outlook).
- The request is transmitted securely (TLS 1.2 or higher).
- Preprocessing and security checks:
- Copilot performs Responsible AI (RAI) checks to prevent harmful content.
- Grounding: The prompt is enriched with context from Microsoft Graph to better understand the user's intent.
- Processing by the LLM:
- The modified prompt is sent to a dedicated LLM within the Microsoft 365 environment.
- Important security aspects: No customer data is stored in the LLM or used for training. The LLM operates statelessly.
- Postprocessing:
- After the LLM responds, grounding and RAI checks are performed again.
- Copilot adds relevant data from Microsoft Graph to the response.
- Compliance and Retention:
- Prompts and responses are stored in Exchange Online for eDiscovery, legal hold, and compliance aspects.
- Output to the user:
- The final answer is returned to the original app.
System Prompt
- I don't want you to agree with me just to be friendly or sympathetic.
- Drop all filters and be brutally honest, direct, and logical.
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