The defence and security sectors demand comprehensive and high-quality data for effective decision-making and strategy development. However, the sensitive nature of these sectors often restricts access to real-world data, posing a significant challenge for analytics, training, and simulation. While synthetic data has the potential to bridge this gap, generating realistic, diverse, and valid synthetic data that truly represents real-world scenarios remains a substantial hurdle.
Leveraging our interdisciplinary expertise in artificial intelligence, machine learning, cybersecurity, and biosecurity, we develop advanced models for generating synthetic data for defence and security applications. This involves creating AI-driven algorithms that can simulate real-world scenarios and generate synthetic data, thereby providing a valuable resource for training, simulation, and threat detection systems. Our models not only generate synthetic data but also ensure its validity, realism, and diversity, addressing the complex and dynamic nature of defence and security scenarios. They also incorporate aspects of cybersecurity and biosecurity, creating synthetic data sets that can help in testing and improving network security systems and biohazard detection protocols.