Poisoned truth: The quiet security threat inside enterprise AI

As enterprises rush to deploy internal LLMs, AI copilots, and autonomous agents, most security conversations focus on familiar threats : prompt injection, jailbreaks, model abuse, and data exfiltration. But some security leaders argue a quieter risk deserves far more attention: what happens when the model’s understanding of reality itself becomes corrupted. This problem is broadly described as AI data poisoning , though experts use different language depending on where the manipulation occurs.