What happened
This MIT Sloan Management Review feature (published 3 November 2026) argues that emerging AI governance frameworks harbor a hidden problem: by making "humans in the loop" reactive to AI-generated alerts instead of proactive in identifying potential threats, they may subtly erode important human behaviors and reduce overall reliability. Drawing on high-reliability organizing (HRO) lessons from nuclear power plants, aircraft carriers and air traffic control — including the Three Mile Island alarm-cascade failure and the Columbia foam-strike dissent suppression — the author contends that AI-enabled threat-detection systems (fraud, intrusion, compliance, patient deterioration) are prone to bias, drift and hallucination, and that governance frameworks patterned on reactive human sign-off can degrade, rather than strengthen, front-line judgment. It recommends designing operating principles that restore decision authority to the humans closest to the problem and treat emerging anomalies as signals rather than noise.
Why it matters
For a CISO or operations leader designing AI oversight, escalation and sign-off processes, this challenges the common assumption that adding human review automatically improves safety — it can reduce human vigilance and create new blind spots, a material consideration in regulated or safety-critical AI deployments.
Action needed
Have your AI risk and security leads test whether AI-alert human-in-the-loop checkpoints are eroding front-line vigilance, and consider applying high-reliability operating principles (front-line judgment authority, anomaly-as-signal) to AI threat-detection governance.