From Pipelines to Policy: Embedding AI-Ready Governance into Cloud DevOps at Scale
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P121Keywords:
Cloud DevOps, AI Governance, Policy-as-Code, Continuous Compliance, MLOps, AI Lifecycle ManagementAbstract
The rapid adoption of cloud-native architectures and DevOps practices has significantly accelerated software delivery, but it has also introduced complex challenges in governance, compliance, and AI integration. In the context of organizations looking to further incorporate the idea of artificial intelligence (AI) into their operational pipelines, the pandemic methods of traditional governance that tend to be reactive and fragmented are no longer applicable. The given paper introduces an AI-ready DevOps architecture that brings in policy-as-code, continuous compliance control, and AI lifecycle control to cloud CI/CD pipelines. The suggested multi-layer architecture allows the end-to-end governance of data, models, infrastructure, and applications as the policy enforcement mechanisms are integrated into development and deployment processes. It is based on automated validation, real-time monitoring, as well as adaptive feedback loops to guarantee compliance, transparency as well as operational resilience. The experimental assessment shows that there are substantial improvements, such as 93.5% rule violation detection, a 57% compliance deviation decrease, and a 59% high severity incidents reduction. Moreover, the accuracy of AI models increased by 10 percent and the problems associated with drifts decreased significantly, which is the evidence of the efficiency of integrated governance in increasing the reliability of the system and the performance of AI. The results underline the need to change the pipeline-centric automation to more proactive, scalable, and AI-conscious policy-driven governance models. The paper offers a practical basis to business organizations that want to strike a balance between innovation and compliance with regulations so that the deployment of AI can be responsible in complex and distributed cloud systems.
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