Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most organizations treating AI security as a model problem are defending the wrong layer. Security teams filter prompts, patch jailbreaks, and tune model behavior, which is all necessary work, while the actual attack surface sits largely unexamined underneath. That surface is the API layer: the endpoints AI systems use to retrieve data, call tools, and take action on behalf of users. This isn't a theoretical gap.

Every secure API draws a line between code and data. HTTP separates headers from bodies. SQL has prepared statements. Even email distinguishes the envelope from the message. The Model Context Protocol (MCP), the fast-growing standard for connecting AI agents to external services, inherits that gap from the models it sits on top of.

Your legal team just handed you a 400-page document and said "figure out compliance." The EU AI Act is live, your organization falls under its scope, which is broader than many expect. Even non‑EU companies must comply if their AI systems are used, deployed, or produce effects within the European Union. In practice, that means that global organizations building or integrating AI models cannot treat the Act as a regional regulation.

AI risk doesn’t live in the model. It lives in the APIs behind it. Every AI interaction triggers a chain of API calls across your environment. Many of those APIs aren’t documented or tracked. That’s your real exposure. Shadow API discovery gives you visibility into those hidden endpoints, so you can find them before attackers do. If you don’t know which APIs your AI relies on, you can’t secure the system.