Change Management using Generative Modeling on Digital Twins
Nilanjana Das Anantaa Kotal Daniel Roseberry Anupam Joshi
A key challenge faced by small and medium-sized business entities is securely
managing software updates and changes. Specifically, with rapidly evolving
cybersecurity threats, changes/updates/patches to software systems are
necessary to stay ahead of emerging threats and are often mandated by
regulators or statutory authorities to counter these. However, security
patches/updates require stress testing before they can be released in the
production system. Stress testing in production environments is risky and poses
security threats. Large businesses usually have a non-production environment
where such changes can be made and tested before being released into
production. Smaller businesses do not have such facilities. In this work, we
show how "digital twins", especially for a mix of IT and IoT environments, can
be created on the cloud. These digital twins act as a non-production
environment where changes can be applied, and the system can be securely tested
before patch release. Additionally, the non-production digital twin can be used
to collect system data and run stress tests on the environment, both manually
and automatically. In this paper, we show how using a small sample of real
data/interactions, Generative Artificial Intelligence (AI) models can be used
to generate testing scenarios to check for points of failure.