Change Management using Generative Modeling on Digital Twins

TL;DR

This paper proposes using cloud-based digital twins combined with generative AI models to enable small and medium-sized businesses to securely test software updates and patches in a simulated environment before deployment.

Contribution

It introduces a novel approach of creating digital twins for IT and IoT systems on the cloud, utilizing generative AI to simulate testing scenarios for patch validation.

Findings

Digital twins can serve as effective non-production testing environments.

Generative AI models can simulate realistic testing scenarios from limited data.

The approach enhances security and reliability of software updates for smaller businesses.

Abstract

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.