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Data security as a foundation for secure AI adoption
Organizations moving to AI often face serious data security challenges — from unintentional data leakage to regulatory compliance issues. This whitepaper, "Data Security as a Foundation for Secure AI Adoption," offers step-by-step guidance on how to prepare your data environment before deploying AI. Learn how to classify and label sensitive data, implement compliance controls, and apply protection and loss prevention policies. Download the whitepaper now to get started, and reach out to CBA for expert consultation on securing your AI transformation.
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How can organizations prepare their data for AI adoption?
Organizations should follow four key steps to prepare their data for AI adoption: 1) Know your data by identifying and classifying sensitive information; 2) Clean up your data by managing permissions and removing obsolete data; 3) Protect your data using labeling and security measures to ensure sensitive information is safeguarded; and 4) Prevent data loss by implementing data loss prevention policies to control how data is shared and accessed.
What are the risks of using AI without proper data governance?
Without proper data governance, organizations face several risks including data oversharing, where unauthorized users access sensitive information; data leakage, where confidential data is inadvertently shared with unsanctioned AI applications; and noncompliant usage, which can lead to regulatory violations and significant fines. Approximately 83% of organizations experience multiple data breaches, highlighting the importance of robust data governance.
Why choose Copilot for Microsoft 365 for AI implementation?
Copilot for Microsoft 365 is designed with built-in security features that help prevent data oversharing and protect sensitive information. It integrates seamlessly with existing Microsoft security and compliance frameworks, ensuring that data remains under the organization's control. Additionally, it allows for personalized content creation while adhering to privacy and compliance commitments, making it a suitable choice for organizations aiming to leverage AI responsibly.
Data security as a foundation for secure AI adoption
published by CBA
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