3 The relationship between governance, security, and responsible AI Encryption Regulatory compliance Securing data from interception and AI applications must adhere to regulations tampering using encryption helps ensure such as GDPR or CCPA, which govern data that generative AI tools can ground their protection and privacy. Non-compliance can responses in the correct context—such as lead to significant penalties and erode trust. work-related data, files, chats, and emails— It’s also crucial to understand where AI tools without risking data leakage. process data, as many free tools may handle data globally or outside of your company’s Data governance policies can mandate usual storage locations. Data governance robust encryption practices, protecting ensures that AI applications not only run data throughout its lifecycle. This approach within legal frameworks but also keep ensures that AI tools can deliver reliable data within the right service boundaries, responses while keeping your data secure aligning with your organization’s compliance and maintaining trust. standards. This approach supports ethical AI use and helps build trust in AI technology. Incident response Sensitive organizational data can be exposed through incidents involving generative AI tools that grant unauthorized access to files, emails, or other business data that systems use to generate responses. A proactive incident response plan is crucial in these scenarios. Without such a plan, the organization risks not only exposing sensitive data but also relying on compromised outputs from the AI. Data governance includes having detailed response protocols to quickly address breaches, minimizing their impact and preserving the reliability of AI systems. 12