Why Large Language Models Are Essentially Cats

Why Large Language Models Are Essentially Cats

If you have attended any of my webinars since 2014, you have likely met my longest-tenured colleague: a domestic short-haired rescue cat named Pancake. While he doesn’t contribute much to the actual workload, he accepts a very low salary and serves as excellent entertainment during Microsoft Teams meetings. Over the years, Pancake has earned a variety of titles. His veterinarian calls him “majestic.” My daughters call him “beautiful.” My sons consider him “disdainful,” and I generally just call him “entitled.” During a recent customer dinner, I was discussing the inherent risks of artificial intelligence and outlining why Keepit is actively investing in our AI Truth Cloud solution. I highlighted a fundamental flaw in modern generative AI: you can issue instructions, but compliance is never guaranteed. Behavioral guardrails simply do not work reliably enough. The only foolproof method to prevent a Large Language Model (LLM) from executing an undesirable action is to physically revoke its ability to perform that specific action.

The Illusion of Control

You can train a feline. You can implement reward systems to encourage good behavior. Yet, despite your best efforts, there remains a persistent, non-zero probability that the cat will simply ignore your commands and do exactly as it pleases. Pancake proved this a few years after his adoption. For reasons known only to him, he decided my bathroom bathtub was a superior alternative to his litter box. No amount of positive reinforcement or deterrents could persuade him to stop utilizing the tub (thankfully, I am a shower person). The only effective solution was structural: keeping the bathroom door securely closed so he could not physically enter the space.

Why Access Control Outperforms Guardrails

The correlation to AI management is direct. The tech industry invests heavily in model alignment, attempting to teach AI to behave safely. However, the only truly reliable defense against unauthorized data modification or deletion is removing the model’s access to that data entirely. While “closing the door” might not solve every behavioral output issue—like preventing an LLM from generating illicit instructions—it is the only way to guarantee that a hallucinating model or a malicious prompt does not result in catastrophic data loss. This feline unpredictability in AI tools is the primary driving force behind the AI Truth Cloud. Integrating AI into business workflows offers massive advantages in speed, quality, and efficiency—provided the AI behaves. When it inevitably acts up, the AI Truth Cloud ensures you can rapidly restore your production environments to a pristine, correct state to keep operations running smoothly.

Looking Ahead

We will share much more about the AI Truth Cloud as its development and refinement continue. In the meantime, browse our webinar archive. If I am presenting, there is a very high probability you will get to see Pancake making a majestic appearance.