Graham Gillen
This blog discusses how Microsoft Copilot incorporates private enterprise data, even as IT leaders struggle to embrace third-party AI models without compromising data security.
Microsoft Copilot: Harnessing the Power of AI Without Compromising Data Security
In today’s data-driven world, organizations are increasingly turning to artificial intelligence (AI) to enhance productivity and gain a competitive edge. However, concerns about data security often hinder the adoption of AI solutions, particularly those that involve third-party models. Microsoft Copilot, a new AI-powered tool, aims to address these concerns by providing a secure and seamless way for organizations to integrate AI into their workflows.
The Rise of AI Assistants and the Challenge of Data Privacy
Microsoft Copilot, which became generally available in September 2023, is part of a broader trend of AI assistants becoming ubiquitous in the workplace. These AI-powered tools – like ChatGPT – are designed to assist users in various tasks, from completing code snippets to writing emails. While AI assistants offer significant benefits, they also raise concerns about data privacy. When organizations use third-party AI models, they risk exposing their sensitive data to those providers.
Microsoft Copilot’s Solution: Retrieval Augmented Generation (RAG)
Microsoft Copilot addresses this challenge by employing a novel technique called Retrieval Augmented Generation (RAG), which we documented in detail in another blog. RAG enables Microsoft Copilot to leverage private enterprise data without compromising its security. Here’s how it works:
- Information Retrieval: When a user submits a query, Microsoft Copilot utilizes an information retrieval (IR) system to identify relevant information from the user’s private enterprise data.
- Prompt Engineering: The retrieved information is then combined with a prompt, which is a set of instructions that guides the large language model (LLM) in generating the desired output.
- LLM Generation: The LLM, trained on a massive dataset of text and code, generates text based on the prompt and the retrieved information.
- Output Delivery: The generated text is then presented to the user, providing them with insights and assistance based on their private enterprise data.
Data Security Measures: Ensuring Data Privacy
Microsoft prioritizes data security and has implemented robust measures to protect private enterprise data:
- Data Access Controls: Microsoft’s Data Access Controls govern who can access what data within Microsoft, ensuring that only authorized individuals have access to sensitive information.
- Data Isolation: When using RAG, all data remains within the Microsoft 365 service boundary, preventing it from being shared with third parties.
- Model Training Exclusion: Prompts, responses, and grounding data generated with RAG are not used to train the foundational models, eliminating the risk of data leakage.
- Private Azure OpenAI Instance: Microsoft maintains a private instance of Azure OpenAI, ensuring that OpenAI does not have access to customer data or models.
Watch Architecture Summary of Microsoft Copilot for Microsoft 365
The Microsoft Ignite video about Copilot is 47 minutes long, Ramesh’s 7 minutes are well worth watching, starting at 31:56.
Conclusion: Embracing AI with Confidence
Microsoft Copilot demonstrates that organizations can harness the power of AI without compromising data security, thanks to architectures like RAG. While this example is specific to Copilot and the Azure OpenAI models, we use the same architecture to build custom AI-powered search applications for our customers using different AI models in different search use cases.
We’d love to help you with your next AI-powered search project, so please CONTACT US if you have any further questions, thank you for reading!
– The Pureinsights Team.