Copilot for Microsoft 365 is a powerhouse for businesses in the GenAI era. The intelligent AI chatbot made enormous waves when it was unveiled, delivering promised productivity, efficiency, and time savings.
Microsoft Copilot is a game-changing technology but it also introduces a new set of challenges while amplifying existing ones. As it sifts through the entire Microsoft tenant for analysis and response, it may expose sensitive data if proper controls and policies aren’t implemented.
Traditional governance practices are not fit for governing and protecting data, especially unstructured data, for GenAI applications like copilots. As organizations rush to adopt these tools, it is high time they upgrade their governance strategy from a traditional to an adaptive approach.
Read on to learn more about the importance of copilot governance, the labeling or over-permission challenges it faces, and the high-impacting best practices for robust data governance.
Why Is Data Governance Important for Microsoft 365 Copilot?
Governance is a core component of data management that offers multidimensional benefits. It helps organizations streamline the integrity and confidentiality of their most important data while ensuring enhanced data quality and regulatory compliance.
Let’s quickly examine why data governance is indispensable to the safe adoption of Copilot for Microsoft 365.
Prevent Unauthorized Access or Sensitive Data Exposure
Copilot has access to a vast volume of data scattered across Microsoft 365 environments, including OneDrive, Excel, PowerPoint, Word, Sharepoint, and Outlook. Without proper data governance controls, the AI chatbot is likely to expose sensitive data to unauthorized users, which could result in severe consequences for an organization.
According to IBM, the global average cost of a data breach reached a whopping $4.4 million in 2024. Global data breach insights, such as IBM’s, signify the financial impact of security oversight. For instance, unintended access permissions in SharePoint may reveal a company’s M&A plans to its non-authorized personnel, like a marketing executive, via Copilot's response. This unintentional exposure could result in data breaches and legal repercussions.
Effective governance frameworks help organizations gain a deeper understanding of their sensitive data through measures like data discovery and classification, labeling, or access intelligence. Security teams leverage these insights to implement appropriate policies and controls, preventing security incidents before they spiral out of control.
Avoid Regulatory Risks & Reputational Damage
Since the introduction of generative AI, the regulatory landscape has expanded drastically. Legal boundaries once limited to data now extend to Artificial Intelligence (AI) and encompass everything in between.
However, every law, whether the GDPR (Article 24) or the EU AI Act (Article 10), demands that organizations adopt appropriate measures to ensure the strict governance and security of regulated data. Without proper guardrails, organizations may be exposed to risks, such as regulatory fines, operational disruptions, or reputational damage.
For instance, if an organization fails to adopt necessary measures to identify and mitigate bias in the data, it could be violating the EU AI Act’s Article 10 provisions. Under the EU AI Act, violators can be fined up to €35 million or 7% of the annual turnover. Similar or higher fines can be expected in violation of other laws, such as the GDPR.
With robust governance measures, organizations can safely ensure Microsoft Copilot compliance. For example, governance teams can enforce appropriate labeling policies to restrict Copilot from exposing sensitive data in its responses, thereby avoiding compliance violations.
Mitigate Risks to Ensure Responsible AI
The incident with Microsoft Tay reflects a memorable yet cautionary message about the significance of ethical or responsible AI. GenAI applications like the Copilot aren’t impervious to flaws like bias, inaccuracy, or misinformation.
Copilot has access to all the applications in the Microsoft 365 environment. As it analyzes and learns from data, it tends to generate responses based on the data it is trained on. Hence, if the training data in the Microsoft tenant is biased or inaccurate, Copilot will likely generate biased or inconsistent responses. Similarly, without effective governance policies around data mapping, lineage, and quality, it becomes challenging for organizations to track how the tool made that decision and resolve the issue accordingly.
Data governance is necessary for organizations as it helps adhere to responsible AI guidelines and practices. Consequently, organizations can not only resolve inconsistencies in their product but also build users’ confidence and trust.
Microsoft 365 Copilot Governance: Challenges with Permissions, Labeling & Data Quality
Gartner’s 2024 report reveals that only 6% of organizations are moving their copilots from pilot to deployment, while a whopping 60% are still in the piloting phase. Copilot has transformational potential, but many organizations have severe reservations. These concerns pose challenges for enterprises lacking effective data governance.
Take, for instance, file permission issues. Copilot can access data in Microsoft tenants for which users have permission. Often, these users are given broader permissions to files they don’t need. Though they have permissions to such files, they are oblivious to them since the files can be anywhere across the environment. However, as Copilot can analyze the context and content of files even if they are ‘view only’ anywhere in the tenant, it can expose the sensitive information within those files to users who were unaware of it.
Similarly, organizations are further overwhelmed with the challenges associated with managing redundant, obsolete, and trivial (ROT) data. ROT data in a Microsoft 365 environment, or any other environment, is not so uncommon. However, this data poses significant security and privacy risks to an organization. Apart from that, ROT data is also detrimental to the accuracy, quality, and freshness of the responses Copilot generates against relevant prompts. Hence, without deleting or quarantining such data, organizations face the risk of hallucination, biased or harmful content, and copyright infringement.
Organizations are also challenged with data labeling concerns that hinder safe Copilot adoption. Microsoft’s native offerings lack the capability of labeling files accurately. Moreover, the tools do not offer granular insights into files. Furthermore, due to the limited number of files that can be labeled per day, scaling labeling of petabyte-scale data becomes a mounting challenge.
These challenges hamper an organization’s ability to effectively govern data for Copilot, slowing down its transition from piloting to complete deployment.
Ensuring Effective Microsoft 365 Copilot Governance with Securiti
Copilot is a tool that can give organizations a competitive edge. Hence, it is imperative to ensure a robust governance framework that can help ensure its safe and responsible use. The following are some key considerations for streamlining data governance.
Data Discovery & Classification
Discovery and classification are core components of proper governance. Data teams must have complete knowledge of all their data across all their environments. Additionally, effective classification helps ensure that the data is processed in accordance with the organization’s business policies and regulatory requirements.
Securiti Data Command Center effectively identifies data across many data repositories, data lakes, cloud storage, and SaaS applications, including the Microsoft 365 environment. Teams can auto-classify sensitive data by leveraging hundreds of advanced OOB classifiers. Using advanced techniques for unstructured data, data teams can effectively classify data based on sensitivity, importance, or relevance.
Data Labeling
Copilot can pick up, learn from, or leak sensitive data in its responses if appropriate governance controls aren’t implemented. Similarly, as SharePoint access controls are limited to user roles and locations rather than content and context of the data, there’s a high likelihood that Copilot may suggest something to users who aren’t supposed to know it. Without effective sensitive data labeling, these risks continue to surface and hinder Copilot adoption.
Securiti helps organizations automatically label files and objects with high precision and at scale. The data labeling is based on factors like classification, ownership, sensitivity, regulations, and age. Organizations can ensure consistent labeling by leveraging an extensive, unified data policy engine. And protect sensitive data by excluding specific labels from Microsoft 365 Copilot’s responses.
Access Management
Access management includes a set of tools and practices that enable organizations to control only authorized users' access to the data. In other words, access management is imperative for allowing teams to maintain a stringent permissions policy. Organizations are further recommended to enforce a least privilege access model, preventing the overexposure of sensitive data. However, managing access can be daunting because there can be multiple permission combinations. For a robust permissions policy, organizations must have contextual intelligence around data access and automated controls for scalability.
Securiti helps security teams identify toxic combinations of files, folders, users, and permissions. The solution further provides granular insights into file-level information, including data sensitivity, entitlements, and regulatory requirements. Leveraging these insights and Securiti’s automation capabilities, security teams can efficiently notify SharePoint’s files and site owners about misconfigurations and security violations. These powerful capabilities allow organizations to reduce alert fatigue by prioritizing sensitive data access.
ROT Data Minimization
ROT data has been a significant concern for organizations even before the inception of Copilots. ROT data pose heightened security and compliance risks, and when it comes to Copilots, it can have a dire impact on the quality and accuracy of responses. The key to improving Copilot responses lies in minimizing duplication, quarantining doubtful data, and deleting trivial information.
Securiti streamlines ROT data minimization, allowing organizations to clean their data environment and ensure data freshness, quality, and accuracy. Data teams can effectively delete duplicate or near-duplicate files by leveraging techniques like AI-enabled clustering and graph-based policies. Detect obsolete files depending on the various parameters, such as ownership of the file, content, access, or age. Moreover, teams can further use sensitive data labeling to quarantine data.
The best practices mentioned above are crucial for Copilot data governance and help enhance the overall security, privacy, and compliance posture of the data environment.
Frequently Asked Questions (FAQs)
