Building Your First GRC Bot with Copilot Studio

In my last post, AI in GRC: A Transformative Force in Cybersecurity, I talked about the “Sorcerer’s Apprentice” problem, which is the very real risk that AI is unleashing forces we might struggle to control. We looked at the scary stuff: deepfakes, AI-generated malware and the fact that our traditional GRC frameworks are struggling to keep up.

But if AI is the storm, it can also be the shelter.

One of the most practical ways to handle this new reality is to stop fighting the current and start swimming with it. We can use the same technology to bring our policies to life. Instead of letting your Acceptable Use Policy gather digital dust in a static PDF, you can turn it into an interactive, 24/7 compliance assistant.

I originally planned to write a technical deep-dive comparing Microsoft Copilot Studio against custom LangChain solutions for this. But the more I spoke to clients, the more I realised that for many organisations, especially those heavily invested in Microsoft 365, the immediate need isn’t a complex architectural debate. It is simply: How do we get this working today?

So, let’s park the Python code for a moment. This article focuses on Microsoft Copilot Studio, a low-code platform that allows you to deploy a policy bot in hours, not weeks.

What are we actually building?

We are building a RAG bot (Retrieval-Augmented Generation), but you don’t need to worry about the acronym.

Think of it this way: When an employee asks, “Can I use ChatGPT for client work?”, a standard AI model might guess the answer based on general internet knowledge. A RAG bot is grounded in your truth. It will:

Look at your specific Acceptable Use Policy.

Read the relevant section.

Tell the employee: “No, our policy prohibits inputting client data into public LLMs.”

It bridges the gap between your documentation and your people.

Why Copilot Studio?

As a Cyber Security Professional, I often look for the path of least resistance. Copilot Studio is usually it because it offers native integration. If you are already managing your policies in SharePoint, you can point an AI agent at those folders and say, “Answer questions using these documents.”

You don’t need to manage vector databases or worry about authentication headers. It leans on your existing Microsoft Entra ID (formerly Azure AD), meaning if a user doesn’t have permission to see the policy in SharePoint, the bot won’t show it to them. That security trimming is hard to build from scratch.

Beyond just access, it also hooks into your wider compliance tooling. If you use Microsoft Purview for Data Loss Prevention (DLP), those same rules apply here. For instance, if a policy is in place to block financial data, the bot will be prevented from displaying sensitive content from a document. It’s another layer of automated governance that you get out-of-the-box, without writing a single line of code.

How to Build It: A Walkthrough

Creating a basic bot is deceptively simple. Here is the workflow I use.

1. “Clean Your Room” (Data Preparation)

The AI is only as good as the documents you feed it. Before you even open Copilot Studio, go to your SharePoint. Create a dedicated library or folder for “Published Policies.” Remove the drafts, remove the duplicates and ensure the file names make sense. This folder is the brain of your bot.

2. Create the Agent

Log into Microsoft Copilot Studio. Select New Agent. Give it a clear name (e.g., “GRC Policy Assistant”) and a description. You can use the “Describe” tab to tell it what you want in plain English, but I usually jump to the configuration settings for more control.

Fig: Agent Creation Screen

 Fig. Provide a description / prompt

 Fig. Select a model. In our case we have selected GTP-5 Auto

3. Connect Your Knowledge

This is the magic step. Navigate to the Knowledge section. Select Add Knowledge and choose SharePoint. Paste the URL of your policy library.

Note: As of mid-2025, Copilot Studio also supports SharePoint Lists, which is great if you keep your risk registers or exception logs in a list format rather than documents.

 Fig. Add SharePoint folder as Knowledge as a grounding mechanism. 

4. Test and Publish

Use the test pane on the right side of the screen. Ask it specific questions like, “What is our policy on remote work?” Verify that it cites the correct document. Once you are happy, publish it to Microsoft Teams so your staff can access it where they work.

The Fine Print: It’s Not Magic

While Copilot Studio is excellent for quick deployment, it isn’t perfect. Here is the reality check I give my clients:

It’s a Black Box: You have very limited control over how the bot retrieves information. If it consistently misses a specific paragraph in a policy, you can’t easily tweak the code to fix it. You usually have to edit the document itself to make it clearer.

Single-Document Bias: These agents are great at finding an answer in one document. They sometimes struggle to synthesize an answer that requires combining information from the IT Security Policy, the HR Handbook and the Remote Work Standard all at once.

Licensing: While often included in the broader ecosystem, keep an eye on your capacity limits. Heavy usage can sometimes trigger the need for additional standalone licenses.

Limited Model Selection: You’re using the AI models that Microsoft provides, which are generally from the Azure OpenAI family or Anthropic’s Claude. You can’t swap in a different large language model if you want to test alternatives like Google’s Gemini. You get what’s in the box; it’s powerful, but it’s not a choice you control.

Conclusion

Microsoft Copilot Studio offers a pragmatic way to bring your GRC policies to life. It moves compliance from a static “read and acknowledge” exercise to a dynamic dialogue.

Is it the most flexible tool on the market? No. But to get 80% of your policy answers into the hands of your team securely and quickly, it is hard to beat. Its primary strength is answering questions, not performing actions.

When your needs evolve beyond simple Q&A and you want an AI that can orchestrate multi-step business processes-like escalating a compliance query, creating a support ticket and scheduling a follow-up-you will start to look at enterprise-grade orchestration platforms like AIRIA.AI, which are built for creating these kinds of autonomous, agentic workflows. And for ultimate control, a custom LangChain solution remains the final frontier for bespoke development.

By starting with a tool like Copilot Studio, you can solve an immediate business need, learn what works and build a solid foundation for your journey into more advanced AI-driven automation. In a future post, I’ll share the results of my experiments using both AIRIA.AI and LangChain to build out these more complex, agentic solutions.

Author: Subodh Chettri

Further Reading:

https://skillfield.com.au/blog/ai-in-grc-a-transformative-force-in-cyber-security/

https://skillfield.com.au/blog/agentic-ai-rewriting-the-rules-of-enterprise-risk-management/