AI-First Banking Architecting an Ethical and AI-Powered Cyber Decision Infrastructure for Scalable IoT Development Using Azure DevOps and GitHub Pipelines
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Abstract
The intersection of AI, IoT, and banking creates new opportunities for personalized services, real-time risk detection, and efficient operations — but it also introduces complex cyber, privacy, and governance challenges. This paper proposes an architectural and operational blueprint for an AI-First Cyber Decision Infrastructure (AICDI) tailored to banking institutions pursuing scalable IoT development. The AICDI integrates edge and cloud IoT telemetry with modular AI/ML components, a decision orchestration layer, and secure CI/CD pipelines implemented via Azure DevOps and GitHub Actions. Core design goals are: real-time, explainable decisioning; privacy preservation by design; threat-aware model lifecycle management; and auditable, policy-driven deployment for regulatory compliance. The architecture uses federated and hybrid learning at the edge to reduce sensitive data movement while enabling aggregated model improvement in the cloud. Security controls include hardware-rooted device identity, zero-trust network microsegmentation, encrypted telemetry, secrets lifecycle management via vaults, and automated security gates in pipelines. Ethical safeguards consist of algorithmic fairness checks, provenance tracking, and an AI governance control plane that ties model decisions to human review and regulatory reporting. We detail an implementation pattern using Azure DevOps for enterprise policy enforcement and GitHub Actions for developer-centric automation, linked by infrastructure as code (IaC) and policy as code (PaC). A simulated evaluation (prototype) demonstrates improved detection latency for anomalous transactions from IoT endpoints, reduced data exposure through edge aggregation, and faster, safer deployment cycles with integrated security tests. We conclude with a roadmap for operationalizing the AICDI in production banks, discuss tradeoffs (latency vs. privacy, automation vs. human oversight), and outline future research directions: standardized audit schemas, formal verification of decision pipelines, and cross-institutional federated governance.
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