How AI Hallucinations Are Creating Real Security Risks

This article examines how AI hallucinations — confidently presented but factually incorrect outputs from language models — pose security risks to cybersecurity operations by causing missed detections, false positives, and dangerous remediation advice; it outlines root causes (flawed training data, bias, lack of validation, prompt ambiguity) and recommends mitigations including human review, data governance, least-privilege access for AI systems, prompt engineering training, and identity-centered governance.