Abstract Cyberattacks pose a significant risk, causing losses for organizations, and remain a major concern for stakeholders. The rapid advancement of artificial intelligence has driven organizations to adopt technologies such as Natural Language Processing (NLP) systems, often without fully understanding the associated security trade-offs. While NLP systems offer significant capabilities, they also introduce technological complexity, expand attack surfaces, and are prone to adversarial inputs. This study aims to conduct a pre- and post-NLP adoption analysis using firm-level data to examine whether NLP implementation leads to increased cyberattack risk. It further investigates how organizational and environmental factors moderate this relationship. By addressing these gaps, the study contributes to the cybersecurity and technology adoption literature and offers practical insights for balancing AI innovation with security resilience. Keywords NLP, AI, cyberattack, technological complexity, adversarial threats.