Artificial intelligence (AI) is reshaping two domains that were long treated separately: creative advertising and network security. Where human intuition and creativity once dominated marketing campaign design, AI now enables data-driven personalization, automated creative generation, and real-time optimization. Simultaneously, AI-based models provide more adaptive, predictive, and automated defenses against evolving cyber threats, while introducing new attack surfaces (e.g., adversarial examples). This paper examines how AI bridges creative intuition and computational precision to transform advertising campaign design and network security strategies. We review relevant literature across marketing, machine learning, and cyber security; describe a mixed-methods empirical study comparing traditional and AI-augmented workflows in advertising and intrusion detection; present quantifiable results (click-through rates, conversion rates, detection rates, false positive rates), and discuss practical, ethical, and technical implications. Results show statistically significant improvements in campaign performance (CTR and conversions) and in detection performance, though trade-offs remain with privacy, model explainability, and adversarial vulnerability. We conclude with recommendations and future research directions to align creative, ethical, and security goals when deploying AI across these domains.