Executive Summary

Several merging forces are reshaping how enterprises need to think about AI security:

• AI adoption is moving quickly from pilots to production use cases embedded inside business applications, employee workflows, customer experiences, and automated decision paths.
• The attack surface is broadening beyond infrastructure misconfiguration into data poisoning, prompt misuse, retrieval abuse, model supply chain risk, excessive agent permissions, insecure API exposure, and weak runtime controls.
• Traditional Application Security and Cloud Security disciplines remain relevant, but they are not sufficient on their own because AI systems are probabilistic, data-dependent, and increasingly autonomous in how they invoke tools and act on context.
• Governance pressure is increasing at the same time that technical complexity is rising. Organizations now need to align security with AI governance, privacy, compliance, model risk, and operational resilience.
• Visibility alone is not enough. The organizations that create lasting value are the ones that turn AI security into an operating capability with clear ownership, repeatable workflows, and controls that prevent the same risks from recurring.

The real-world implication is straightforward: AI application security should be treated as the next maturity layer after CNAPP, not as a separate side project. Successful programs will extend proven cloud security disciplines into AI-specific controls for data, identities, models, agents, and runtime behavior, but controls and best practices alone are not the same as an operating model. The operating model is the organization-specific way those disciplines are assigned, governed, and executed day to day.

Download the full whitepaper to learn more.