Ineffective anomaly detection means the system fails to recognize abnormal patterns in data or model behavior. The GREATEST risk is undetected data poisoning (B), which jeopardizes data integrity and leads to corrupted, biased, or unsafe AI decisions. AAIA emphasizes that anomaly detection is critical for identifying tampering, data drift, or malicious manipulation.

Configuration inconsistencies (A) are operational issues but far less damaging. Slower drift response (C) can cause performance degradation but is not as severe as undetected poisoning. Reporting standard failures (D) are compliance risks—not as critical as compromised decision integrity.

Thus, undetected poisoning poses the highest risk due to its direct impact on model trustworthiness and safety.