Smart Governance for AI: Can Metadata Automation Keep Up with Real-Time ML Pipelines?
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P113Keywords:
Smart AI Governance, Metadata Automation, Real-Time Machine Learning, ML Pipeline Monitoring, AI Compliance Automation, Data Lineage Tracking, Automated Metadata Management, Real-Time AI Governance, ML Model Auditing, AI Regulatory Compliance, Metadata-Driven AI Governance, Continuous ML Pipeline Oversight, Automated AI Lifecycle Management, Streaming Data Governance, AI Transparency, AccountabilityAbstract
As artificial intelligence (AI) and machine learning (ML) systems increasingly drive real-time decision-making in industries such as finance, healthcare, and autonomous systems, the need for robust yet agile governance mechanisms has become critical. Traditional compliance frameworks often struggle to keep pace with the dynamic nature of real-time ML pipelines, leading to either regulatory gaps or performance bottlenecks. This paper explores the viability of metadata-driven automation as a solution to enforce governance without compromising the speed and efficiency of AI/ML workflows. Drawing on recent advancements in automated metadata management, we analyze two pivotal studies from the past five years: (1) "Automating Data Lineage and Compliance in Machine Learning Pipelines" (Zhang et al., 2021), which proposes a real-time metadata tracking system to enforce GDPR and HIPAA compliance without manual intervention, and (2) "Dynamic Policy Enforcement for Streaming ML Models" (Kumar et al., 2023), which introduces an adaptive governance layer that adjusts access controls and bias mitigation strategies based on live data streams. Our research synthesizes findings from these works to evaluate whether metadata automation can effectively balance regulatory demands with computational efficiency. Key challenges include latency introduced by runtime policy checks, scalability across distributed systems, and the interpretability of automated governance decisions. We also examine emerging solutions such as federated metadata repositories and lightweight cryptographic auditing to minimize overhead. The paper concludes with a framework for implementing smart governance in real-world ML pipelines, offering best practices for industries requiring both high-speed inference and strict compliance. Empirical evidence suggests that metadata-driven automation can reduce governance-related latency by up to 40% compared to traditional methods, though its success depends on careful architectural integration
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