Abstract
Conventional software validation methods can be static and periodic, which may present challenges for continuously updated platforms (e.g., cloud platforms) in regulated industries and can impede the validation of non-deterministic systems like artificial intelligence models. A system for continuous validation may use a dual-pathway architecture guided by a machine-readable compliance model. One pathway can perform adversarial testing within an isolated digital twin of a production application to discover potential compliance weaknesses. Concurrently, a second pathway can provide real-time observational monitoring of the live production system for policy deviations and anomalies. Findings from both pathways may be consolidated into a persistent, verifiable evidence record, which can provide an ongoing assurance function to help maintain a system's validated state and mitigate compliance risks in dynamic environments.
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Neelakandan, Ramakrishnan; Singh, Teginder; and Patel, Bakul, "Model-Driven Continuous Validation Using Adversarial Testing in a Digital Twin", Technical Disclosure Commons, (December 15, 2025)
https://www.tdcommons.org/dpubs_series/9042