An organization is deploying an automated AI cybersecurity system.

Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage—directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. “Ensure responsible use” is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.