The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

The number of cyberattacks has greatly increased in in the last years, as well as their sophistication and impact. For this reason, new emerging software development models are demanded, which help in developing secure by default software. To achieve this, the analysis and comparison in depth of the current models of secure software development is especially important. In this paper, a review of the most popular secure software models is presented, and a new secure software methodology is proposed, adapted to all current environments. A practical experiment in a software development company is tested, as a case study, considering data from real software projects. The results are presented and compared in two development scenarios: a classic one with a reactive security approach, and another one, emerging and preventive, that applies security by default in all phases of the software life cycle. In the case study, the total amount of vulnerabilities is reduced by 68,42%, decreasing their criticality and the temporal impact of their resolutions. In this way, software security and quality are methodologically improved with the proposed model, proving that the new emerging approach provides a more secure software.