REVERSE ENGINEERING AND GENERATIVE AI: REBUILDING THE CODE TO UNDERSTAND THE ENEMY

Abstract

The rise of generative artificial intelligence has transformed how computer systems are designed, tested, and understood. In the field of cybersecurity, this revolution reconfigures the role of reverse engineering, which ceases to be merely a technical decoding process and becomes a cognitive practice of intelligent reconstruction. This article analyzes the theoretical and investigative use of reverse engineering associated with generative AI as an instrument for behavioral analysis and adversarial learning—not only to dismantle malicious code but also to understand the logics that produce it. From an interdisciplinary approach, it discusses how generative models (such as deep neural networks and autonomous agents) can reproduce and simulate threats, allowing researchers and analysts to explore the inner workings of software and algorithms from an epistemological perspective. The study also proposes a conceptual model that integrates engineering, cognition, and digital ethics, aligned with the demands of the information age and contemporary cyber defense guidelines. The results indicate that the integration between reverse engineering and generative artificial intelligence represents a new research paradigm, capable of uniting technical reconstruction and ethical reflection in understanding and addressing digital threats.