What is rogue AI, and why is it important to define it?

Since my last blog, which reviewed a series of notable incidents seen over several months, the conversation around “rogue AI” has again moved on. 

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We are now hearing growing calls for an ‘AI slowdown’. Put simply, many people at the forefront of AI development believe the technology is advancing too quickly for oversight and security to keep pace.

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Some of the warnings have been headline-grabbing. Jacob Coxon described the next year or two as a ‘crunch time for humanity,’ while Anthropic CEO Dario Amodei urged the industry to ‘pace the frontier’; a position later backed by Sam Altman of OpenAI, Elon Musk of xAI, and Demis Hassabis of Google DeepMind.

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The widening gap between AI development and oversight is something I have written and spoken about repeatedly and at length. Many of us have long argued that AI oversight must be significantly strengthened to keep pace with a technology advancing at an extraordinary rate. But security does not have to stand in the way of progress. As I have also argued, when AI can be trusted, innovation can accelerate.

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The problem is that the AI oversight debate which focuses on the biggest AI companies and frontier models can lead to the misconception that these are the only circumstances where rogue AI is a threat. That can distract from practical, urgent conversations about how to deploy AI responsibly in everyday use. Instead, we need to ensure that everyone using AI is educated on what rogue AI looks like, how it is detected and how responsible deployment can be achieved.  

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In reality, every AI system – from the LLMs many of us use every day to the agents companies rely on to automate tasks – has the potential to go rogue. ‘Going rogue’ means deviating from the baseline; what is normal and expected for that AI. Anomalies may indicate security risks, operational failures, data misuse, or unintended behavior. This could be sudden breaks, like a jailbroken chatbot, such as when delivery company DPD was forced to disable its AI customer-service chatbot after it began swearing and criticising the company. Or it could be slow drift; a 2025 study of AI models used across US Veterans Affairs hospitals found that disparities in predictions between demographic groups changed over a ten-year period. These are all examples of rogue AI, and they can happen overtime or in seconds, and cause significant damage through lost customers, lawsuits, financial losses, and reputational harm.

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The debate over how we regulate AI must continue, and it is encouraging to see potential solutions being discussed. But we should not allow that debate to distort the meaning of rogue AI or lead organizations to believe that the risks don’t apply to them.

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Rogue AI is not a future hypothetical. It is happening now, every day. It is a concern not only for market leaders, but for every organization building, selling, or deploying AI.

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That is why RAIDS is so important. We provide continuous behavioral monitoring of AI systems in production, showing what they are actually doing – not just what they were designed or expected to do – and detecting emerging risks that static testing can miss. A ‘point in time’ audit tells you what your model was doing during that period of observation, but it doesn’t show what it is doing now. 

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Industry-wide discussions about how to manage AI’s advancement are welcome, but the language we use is equally important. If ‘rogue AI’ isn’t understood in the context of how AI models are being used every day, organizations may overlook harmful deviations in the systems they already use. Defining the term clearly is not simply a matter of semantics; it is essential to identifying, monitoring, and managing the risks already in front of us.