Summary
This guide presents a comprehensive checklist for ensuring data readiness for AI applications, including agentic AI and generative AI. It outlines essential practices such as enforcing strict access controls and continuously auditing sharing permissions to protect sensitive data. The document emphasizes the importance of applying security labels and policies in real time to automatically safeguard content. It details processes for quarantining exposed files and blocking unauthorized sharing to prevent data breaches. Additionally, the guide recommends regular reviews and updates of security policies to adapt to evolving threats. It highlights the need to prevent sensitive data from being used in AI training by implementing classification-based controls and employing techniques such as redaction, encryption, and anonymization. The document also discusses automating compliance processes and enhancing data governance to ensure data is manageable and accessible. Furthermore, it advises on customizing entity recognition and document classification to align with business needs and integrating data management platforms with AI technologies.
