AI systems face unique threats not commonly found in traditional IT environments, particularly data poisoning , where attackers manipulate training data to corrupt model behavior. Controls that specifically monitor and mitigate poisoning-such as input provenance checks, anomaly detection on training data, and integrity validation pipelines-are emphasized in AAIA's coverage of AI-specific vulnerabilities .
While privacy (A), data exfiltration (C), and data governance (D) controls are essential for all digital systems, monitoring for data poisoning is uniquely critical for AI because poisoned inputs can lead to faulty predictions, safety issues, or systemic bias. AAIA specifically highlights data poisoning as a distinct threat requiring specialized controls.
References:
ISACA, AAIA Exam Content Outline - Domain 2: Threats and Vulnerabilities Specific to AI.
ISACA AI security guidance discussing poisoning and integrity attacks.