Organizations can prepare their data for AI adoption by following four key steps: 1) Know your data by identifying and classifying sensitive information; 2) Clean up data and permissions to ensure compliance and reduce risks; 3) Protect sensitive data using labeling and encryption; and 4) Prevent data loss by implementing data loss prevention policies. This proactive approach helps organizations manage their data effectively and mitigate potential security risks.

Without proper data governance, organizations face several risks, including data oversharing, where unauthorized users gain access to sensitive information, and data leakage, where confidential data is inadvertently shared with unsanctioned AI applications. Additionally, non-compliant usage of AI can lead to regulatory violations and hefty fines. A lack of visibility into data can further complicate these issues, making it essential for organizations to establish strong governance frameworks.

Microsoft Copilot provides built-in security controls that help prevent data oversharing and ensure compliance with data protection regulations. It integrates seamlessly with existing Microsoft 365 solutions, allowing organizations to leverage AI-driven capabilities while maintaining control over their data. Copilot also inherits sensitivity labels from existing files, ensuring that data protection measures are consistently applied, which is crucial for organizations concerned about data security during AI adoption.