Federated AI Observability in Multi-Cloud Microservices: A Secure and Scalable Federated Learning Perspective (Published)
As artificial intelligence (AI) becomes integral to microservices deployed across multi-cloud environments, ensuring secure and scalable observability is critical. Traditional centralized observability methods often fail to address the privacy, compliance, and performance challenges inherent to distributed AI systems. This paper presents a federated learning–based framework for AI observability that preserves data privacy and scalability across heterogeneous cloud platforms. The proposed framework decentralizes telemetry collection and analysis by integrating local observability agents with secure federated aggregation, while maintaining interoperability with modern DevOps pipelines. We evaluate the architecture through case studies in retail, healthcare, and finance sectors, demonstrating improvements in anomaly detection, regulatory compliance, and operational efficiency. Additionally, the paper examines ethical considerations such as data privacy, fairness, and transparency, and outlines future directions including edge observability, privacy-enhanced computation, and automated governance. This research provides a foundational strategy for building trustworthy and efficient observability systems tailored to AI-powered microservices within complex multi-cloud ecosystems. Traditional observability methods struggle with privacy and performance in AI-powered multi-cloud microservices. We propose a federated learning–based framework that enables decentralized telemetry monitoring while ensuring compliance and scalability. Our evaluation across healthcare, finance, and retail shows improvements in anomaly detection latency (25%), fraud detection accuracy (18%), and GDPR/HIPAA alignment. This work lays the groundwork for trustworthy and efficient AI observability in complex cloud-native ecosystems.
Keywords: AI observability, DevOps, Privacy-preserving monitoring, differential privacy, federated learning, model drift detection., multi-cloud microservices, secure aggregation
AI-Enhanced Data Governance for Modernizing the US Court System (Published)
The US court system is currently burdened by inefficiencies, data silos, and security vulnerabilities that urgently require modernization to restore public trust. Outdated legacy systems, fragmented data practices, and limited interoperability hinder case management and transparency. A robust data governance framework powered by cutting-edge technologies like Artificial Intelligence (AI), blockchain, and federated learning is essential to address these pressing challenges. This paper explores how AI-enhanced data governance can swiftly transform the judicial system by ensuring data integrity, security, and accessibility. It presents solutions that modernize the court system and offer scalable applications for other sectors, such as healthcare, finance, and education. Adopting centralized data platforms, AI-driven data management, and advanced encryption methods can enhance operational efficiency, reduce biases, and improve decision-making processes. By leveraging this technology-driven framework, the judiciary can deliver justice more effectively, regain public trust, and set a precedent for modernization across industries.
Keywords: AI-enhanced data governance, Operational Efficiency, Predictive Analytics, US court modernization, blockchain security, centralized data platforms, data integrity, federated learning, judicial transparency, privacy compliance