AI-Driven Continuous Compliance in DevOps Pipelines for Secure Platform Engineering Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P125Keywords:
Artificial Intelligence, Continuous Compliance, Devops, Devsecops, Platform Engineering, Compliance-As-Code, Secure Software Development, Cloud Security, Machine Learning, Governance AutomationAbstract
Modern software delivery processes have been drastically changed by the quick ramp-up of cloud-native architectures, microservices, Infrastructure-as-Code (IaC), and DevSecOps practices. Organizations are more likely to be deploying applications using highly automated DevOps pipelines that support continuous integration, continuous delivery, and continuous deployment. These practices are highly effective in enhancing agility and operational efficiency, but carry complex security, governance, and regulatory issues. Most compliance evaluation methods rely on manual audits and periodic evaluations and cannot compete with the pace and scale of today's software development environments. As a result, there is a greater risk of policy violations, security misconfigurations, noncompliance with industry regulations, and late software releases. In response to these challenges, Artificial Intelligence (AI)-based Continuous Compliance is emerging as a viable paradigm for embedding compliance monitoring, risk assessment, and policy enforcement into DevOps pipelines. In this paper, a complete framework for continuous compliance in secure platform engineering systems using artificial intelligence is described. The intended approach is to use a combination of machine learning algorithms, intelligent policy engines, automated evidence-gathering mechanisms, compliance-as-code concepts, and predictive risk analytics to continuously audit and assess system configurations and deployment activities on the fly against the set of regulatory requirements. It utilizes AI methodologies to detect anomalies, make policy recommendations, evaluate the security posture, and predict compliance throughout the software development lifecycle. When compliance validation is built into every stage of the pipeline, companies can spot compliance issues early, minimize remediation costs, and ensure regulatory compliance while improving deployment speed. The study also delves into the convergence of AI-powered compliance tools and platform engineering, focusing on automated governance in cloud-native spaces. Compared with traditional compliance management strategies, a comparative evaluation clearly shows higher levels of compliance accuracy, audit readiness, risk detection, and deployment efficiency. The results of the experiments suggest the potential of AI-powered compliance surveillance to cut compliance violation detection time by more than 80 percent, boost compliance coverage by 35 percent, and slash manual audit work by around 60 percent. It also enables adaptive policy learning, allowing continuous improvement in governance effectiveness as organizational contexts change. The suggested model advances the development of smart DevSecOps ecosystems by providing an automated, proactive, and scalable compliance control structure. The results underscore the importance of AI in supporting secure platform engineering practices and illustrate how ongoing compliance can become a key enabler for today's digital transformation efforts. Possible future research directions are federated compliance learning, explainable AI for governance decisions, and autonomous compliance orchestration in multi-cloud environments.
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