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The AI Control Loop: When AI Goes Rogue - with Craig Thomas of Wallarm
Description
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.
In this episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, examines what rogue AI actually means in practice, where the risk materializes, and what it takes to move from detection to control.
Questions
- When we say "rogue AI," what do we actually mean? Is it only malicious AI, or can legitimate systems become risky too?
- What are the most common ways AI systems drift outside intended boundaries? Once an organization understands what rogue AI looks like, where does that loss of control typically begin, and who is responsible for preventing it?
- How do shadow LLMs, unsanctioned agents, and unmanaged AI workflows create risk even when no attacker is involved? If AI drift often starts with normal business activity, where do shadow AI systems fit into that picture?
- Why can an AI action look legitimate in isolation but still create serious business, security, or compliance risk when viewed as part of a larger sequence of actions? As these shadow systems become more embedded in everyday workflows, why is it so difficult to recognize risk in real time?
- How do APIs, integrations, and connected systems amplify the impact of those seemingly legitimate actions? What changes once those actions begin flowing across APIs, business applications, and interconnected systems?
- What kinds of unexpected outcomes worry CIOs and CISOs most today when AI systems are operating across those interconnected environments? As that connectivity expands, what are security and business leaders most concerned about?
- And given those concerns, what does meaningful oversight actually look like when AI systems can act at machine speed?
- How should organizations distinguish between the experimentation they want to encourage and the unmanaged AI behavior they need to control? One challenge is balancing governance with innovation. How do organizations avoid slowing down AI adoption while still maintaining control?
- We know that many organizations can detect risky AI behavior after the fact. But if they can't stop it in real time, what critical gap still remains? Even with governance programs in place, many organizations are still operating reactively. In closing, what's the key difference between detecting AI risk and actually controlling it?
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Full Abstract
In this episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, examines what rogue AI actually means in practice, where the risk materializes, and what it takes to move from detection to control.
Not every AI threat starts with an attacker. Some of the most consequential AI risks organizations face today come from systems that are working exactly as designed, just not quite as intended. An agent that calls an API it was never supposed to reach. A workflow that exposes PII because nobody mapped the data path before deployment. A shadow LLM standing up in an AWS account because a developer ne