In the past decade, a growing number of cyberattacks have been reported, enabling unprecedented levels of personalization, automation, and deception. For instance, recent industry surveys have reported sharp increases in unique social engineering attacks within a single month of 2023, coinciding with the public release of ChatGPT-3.5. This trend highlights how Artificial Intelligence (AI)-powered phishing campaigns have become a significant threat to digital ecosystems. The present study provides an integrative analysis of how generative and deepfake technologies have reshaped the landscape of a Social Engineering (SE) attack, categorizing the main attack strategies and examining their psychological, technological, and ethical implications. In addition, to reviewing enabling technologies, our study conducts a comparative analysis of frameworks and analytical models across technical, empirical, and quantitative perspectives that model AI-driven SE operations and their defensive countermeasures. The convergence of these frameworks reveals three core capabilities—realism, personalization, and automation—that systematically amplify attack efficiency. Building on these insights, the study proposes the Unified Model for AI-Driven Social Engineering (UM-AISE), a conceptual framework that integrates these dimensions across the attack lifecycle and employs a theoretical Markov Decision Process (MDP) analysis. This formalization demonstrates how these capabilities can shift the attacker’s optimal strategy, offering a formal economic perspective distinct from empirical validation. Finally, the study discusses emerging ethical and regulatory challenges associated with AI-mediated deception, highlighting risks related to opacity, accountability, and large-scale manipulation. Taken together, these elements inform evolving approaches for detection, defense, and governance relevant to researchers, policymakers, and practitioners.

Transparency in business practices is essential for sustainability, ensuring that resources are used responsibly and that environmental and social impacts are properly measured and monitored, allowing the end consumer to make informed purchasing decisions without feeling cheated. The Digital Product Passport (DPP) promotes transparency by providing detailed information about a product’s origin, composition, and life-cycle activities, enabling more sustainable and responsible choices. The implementation of the DPP for textile and clothing items faces many challenges due to the large number and diversity of companies involved in the value chain of these products, combined with the large amount and variability of information that needs to be collected. Therefore, the integration and standardization of data from these companies is one of the largest present challenges. In this article, we study the use of Machine Learning (ML) algorithms for validating, in a homogeneous way, the quality of the data submitted by each company for the implementation of the DPP. We have studied four solutions that, using datasets organized in different ways and using different ML algorithms, enable selecting the solution that best suits each particular situation.

Transparency in business practices is essential for sustainability, ensuring that resources are used responsibly and that environmental and social impacts are properly measured and monitored, allowing the end consumer to make informed purchasing decisions without feeling cheated. The Digital Product Passport (DPP) promotes transparency by providing detailed information about a product’s origin, composition and life cycle activities, enabling more sustainable and responsible choices. The implementation of the DPP for textile and clothing items faces many challenges due to the huge number and diversity of companies involved in the value chain of these products combined with the large amount and variability of information that needs to be collected. Therefore, the integration and standardization of data from these companies is one of the biggest challenges. In this article we propose the use of Machine Learning (ML) algorithms for validating, in an homogeneous way, the quality of the data submitted by each company for the implementation of the DPP. We present four solutions that, using datasets organized in different ways and using different ML algorithms, enable selecting the solution that best suits each particular situation.