This whitepaper provides a practical framework for securing generative AI systems by focusing on asking the right questions throughout the lifecycle of development, deployment, and operations. It highlights the complexity and evolving nature of GenAI, where risks include prompt injection, data poisoning, model misuse, and supply chain vulnerabilities. The paper emphasizes understanding data sources and flows, protecting models as critical assets, and applying principles like least privilege and zero trust. It also stresses the importance of governance, continuous monitoring, and secure-by-design practices. By aligning stakeholders and embedding security into every stage, organizations can build resilient, trustworthy, and compliant AI systems while managing emerging risks effectively.

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