Keeping Copilot Under Control: Why fine-tuning AI is critical for protecting sensitive data

Keeping Copilot Under Control: Why fine-tuning AI is critical for protecting sensitive data

Artificial intelligence tools, such as Microsoft Copilot, have the potential to transform workplaces by automating tasks and streamlining processes. 

But as businesses rush to adopt the technology, many overlook a critical step: configuring Copilot to respect data boundaries. Without proper controls, Copilot’s ability to access and process vast amounts of organisational data can become a liability rather than an asset.

In this article, you’ll learn why fine-tuning Copilot is essential, how to establish boundaries and the steps your organisation should take before using it.
 

What can go wrong when Copilot has unrestricted data access?

Microsoft Copilot works by generating responses by accessing your organisation’s data, including SharePoint documents, Teams chats, files, emails and calendars. 

By default, it can access all the data users have permission to view within your Microsoft 365 environment. While the potential is great, it introduces significant risks if you mismanage permissions, including the following:

• Exposure of sensitive data. Copilot can inadvertently surface confidential information, such as financial records, intellectual property, or customer data, to unauthorised employees. For example, support staff might receive AI-generated summaries of employee contracts that contain salaries.
• Third-party data leaks. Files shared externally via Teams or SharePoint could be processed by Copilot, exposing sensitive data to partners or contractors.
• Regulatory non-compliance. GDPR, CCPA and industry-specific regulations can require strict access controls.

The fallout of these data breaches can be severe, ranging from disgruntled employees to regulatory fines.

How to set up Copilot for optimal security

Fine-tuning Microsoft Copilot is critical for safeguarding sensitive organisational data and tailoring AI responses to your specific business needs. It provides a way to customise Copilot’s behaviour, restrict data access and ensure compliance while unlocking its productivity potential.

Below are detailed, actionable steps and best practices you can follow to fine-tune Copilot effectively:

1. Outline your AI vision and goals

Before diving into technical configurations, it is important to establish a clear vision for how you intend to use AI within your company. Doing so will help to align Copilot's deployment with strategic business objectives.

• Define specific use cases. Identify the primary tasks and processes where you expect Copilot to add value, such as generating content, summarising documents and automating workflows.
• Determine desired outcomes. Clearly articulate what success looks like for each use case, whether it's increased productivity, improved decision-making or enhanced customer service.
• Align with business policies. Ensure that your AI goals are consistent with existing data governance policies, privacy regulations and ethical guidelines.

By outlining your AI goals first, you create a roadmap that guides subsequent decisions about data access and security controls, ensuring Copilot serves your organisational needs effectively and responsibly.

2. Conduct a pre-deployment audit

The next step is to conduct a thorough audit of your data landscape and user permissions. Start by identifying which data repositories contain sensitive information, such as HR records, financial files or intellectual property, and exclude these from Copilot’s initial access.

Next, review user access levels and apply the principle of least privilege, ensuring that only authorised personnel can access specific datasets. Employment contracts, for example, should be limited to the HR team and C-suite.

Finally, clean up outdated or overly broad permissions on SharePoint sites, Teams channels and document libraries to prevent unnecessary exposure. This audit helps align Copilot’s data access with your organisation's existing governance policies, setting a strong foundation for secure AI use.

3. Implement data classification and labelling

You can apply a structured classification system to your data to guide Copilot’s behaviour. For example:

• Assign sensitivity labels (e.g., Confidential, Highly Confidential) to emails and files throughout your Microsoft 365 environment.
• Use automated tools like Azure Purview to maintain a real-time catalogue of sensitive assets and apply these labels consistently at scale.
• Leverage labels to enforce security policies within Copilot. For example, preventing “Confidential” documents from being copied or shared outside authorised channels.

By embedding classification into your data management, you enable Copilot to respect data boundaries automatically, reducing the risk of accidental leaks.

4. Restrict access with zero-trust controls

Zero trust principles rethink how access and trust are managed. We take a “never trust, always verify” approach before granting access to any resource, regardless of the user's location or network origin.

Implement zero-trust principles to tightly govern who can use Copilot and which data it can access:

• Segment sensitive data by placing it in isolated SharePoint sites or Teams channels with restricted access tailored to specific teams or projects.
• Enable Conditional Access policies requiring multi-factor authentication for users interacting with sensitive information via Copilot.
• Remove legacy sharing permissions such as “Everyone” or “All Employees” and replace them with role-based security groups to minimise broad exposure.

5. Monitor and refine access

Even after deployment, maintaining control over Copilot’s data interactions requires ongoing vigilance. Conduct regular reviews, ideally quarterly or every six months, of both data access permissions and Copilot usage rights, promptly removing access for users who have changed roles or completed projects.

You can also use Microsoft Purview’s governance tools for comprehensive insights and compliance reporting related to AI-driven data access. This proactive monitoring ensures your Copilot deployment remains secure and aligned with evolving organisational needs.

Are you ready for AI?

Microsoft Copilot’s value is undeniable, but its data-hungry nature demands proactive governance. Organisations that don’t take time to fine-tune AI before deployment risk exposing sensitive information, creating internal turmoil and damaging their reputation.

Want to find out if you’re ready to deploy AI? Then get started with an AI readiness assessment courtesy of Method. We’ll audit your data landscape, review access controls and offer guidance on how you can improve. 

Don’t let unchecked AI access become a data breach. Book a meeting today to learn more.