Traditional security approaches fall short for AI because they rely on static controls that assume vulnerabilities will appear consistently. Generative AI, being non-deterministic, produces varying outputs each time it runs, making it difficult for conventional security measures to detect and block attacks effectively. Organizations need to adopt asset-centric and data-centric security strategies, particularly a Zero Trust approach, to address these unique challenges.

Zero Trust enhances AI security by eliminating the assumption that a network perimeter is sufficient for protection. It encourages organizations to focus on securing assets both inside and outside traditional boundaries, which is crucial in a hybrid environment. By applying Zero Trust principles, organizations can better protect their data and AI applications from evolving threats while leveraging AI's capabilities to improve security operations.

Organizations can manage AI security risks by implementing several strategies: educating users about the risks of AI-driven scams, integrating security early in AI development to protect applications and data, adopting AI security capabilities to enhance security operations, and establishing written security standards that cover security, privacy, and ethical considerations. These steps can help organizations navigate the complexities of AI while safeguarding their assets.