Source: SiliconANGLE
Traditional security monitoring was built to catch known attack signatures and anomalous behavior patterns, but AI systems operate across dimensions—latency, token sequences, embedding spaces—that conventional tools can't instrument or interpret. Attackers are already exploiting this gap while enterprises spend resources on detection frameworks that don't map to how modern models actually fail or get compromised. Security vendors need to rebuild their detection layer around neural network internals rather than bolt AI onto legacy monitoring. Until that happens, attackers who understand model behavior have the advantage.