Abstract
Proposed herein is an adaptive development, security, and operations (DevSecOps) and machine learning operations (MLOps) pipeline system that can transform how organizations deliver Artificial Intelligence (AI)-generated software by embedding intelligence, security, and governance directly into the development lifecycle. Instead of treating AI-generated code and models as static artifacts, the system continuously analyzes their behavior, provenance, risk, and compliance posture. By combining ML–driven risk assessment, continuous security enforcement, and automated policy governance, the platform dynamically adapts testing, deployment, and approval workflows in real time. This ensures that only trustworthy, compliant, and high-quality AI artifacts reach production. The result is a unified pipeline that enables enterprises to scale AI development safely, accelerate innovation, and maintain operational trust—without sacrificing speed or security.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Rasal, Kirti Sachin, "ADAPTIVE DEVELOPMENT, SECURITY, AND OPERATIONS AND MACHINE LEARNING OPERATIONS PIPELINE SYSTEM FOR INTELLIGENT PROCESSING OF AI-GENERATED ARTIFACTS", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10110