Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they’ve already deployed – sponsored by our friends at Wallarm.
Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.
We all know that you can’t secure what you can’t see, which is why AI discovery is a first principle for AI security, but what’s really required for AI discovery? It’s more than just LLMs and agents. Today’s episode is entitled AI Discovery isn’t just AI, and joining us is Tim Ebbers, Field CTO at Wallarm. Tim and I discuss the real requirements for AI discovery, and why the connections between assets and infrastructure are part of the puzzle.
Questions
- Security teams often say, “You can’t secure what you can’t see.” In the context of AI, what exactly do they need to see?
- What supporting infrastructure matters most when mapping AI risk, such as APIs, cloud services, Kubernetes workloads, data stores, identities, and external integrations?
- Where does shadow AI typically appear first inside an enterprise environment? How can it be prevented?
- How do relationships between assets change the risk picture? For example, why does it matter which API an agent can call or which data source a workflow can reach?
- What makes AI discovery harder than traditional application or cloud asset discovery? What are the similarities and differences?
- How should organizations prioritize what they find? Is every AI asset equally risky?
- What does “continuous discovery” mean in a world where AI services can be deployed, connected, or changed in minutes?
- Once an organization has visibility into its AI footprint, what’s next? What are the biggest gaps in today’s AI security programs?
Links
Full Abstract
Most security teams know that you can’t secure what you can’t see. In the context of AI, that rule turns out to be a lot harder to satisfy than it sounds.
AI discovery isn’t just a matter of cataloging your LLMs and agents. The real picture includes the APIs those agents call, the data sources they reach, the infrastructure they run on, and all the AI that got deployed without anyone telling security. Building that picture requires understanding relationships, not just inventories, because risk doesn’t live in assets in isolation. It lives in what those assets can do together.
In this episode, Tim Ebbers, Field CTO at Wallarm, examines what a complete AI control loop actually requires at the discovery stage: what needs to be visible, why the connections between assets change the risk calculation, where shadow AI tends to appear first and how it becomes unmanaged risk, and what makes AI discovery structurally different from traditional cloud or application discovery. It also looks at what organizations should do once discovery is in place, and where the biggest gaps remain in AI security programs today.
If your team is building toward continuous AI governance, this is where that work starts.
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