Enterprise artificial intelligence (AI) systems face multi-stage attack campaigns that combine reconnaissance, exploitation, and persistence techniques borrowed from traditional cybersecurity but adapted for machine learning (ML) contexts. Adversaries increasingly chain multiple attack vectors—extracting model intelligence, poisoning training data, embedding trojans, and exploiting privacy leakage—to achieve objectives that no single technique could accomplish alone.