AI Model Security Strategies for CISOs and Security Leaders

AI model security is an emerging discipline focused on safeguarding machine learning systems from attacks that exploit their probabilistic nature, such as poisoned training data, adversarial inputs, and model extraction, which traditional application security tools often fail to address. As AI adoption grows, with models sourced from third-party APIs, open-source repositories, and AI coding assistants, the attack surface has expanded, prompting the need for robust AI security programs. This practice involves protecting key assets like training data, model weights, and inference endpoints while addressing business risks, regulatory pressures, and the diverse AI attack vectors that current security frameworks and standards, such as NIST AI RMF and OWASP ML Security Top 10, aim to manage. Implementing AI model security requires organizations to establish AI asset inventories, integrate security into development workflows, deploy continuous monitoring, and develop incident response capabilities tailored to AI threats. This also includes securing third-party AI models, managing AI-generated code risks, and fostering collaboration between security, data science, and engineering teams to ensure comprehensive governance and policy enforcement.

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