A Cloud-Native AI Framework for Secure Financial Networks: DevSecOps-Enabled Threat Detection and Multivariate Risk Analytics

Abstract

The financial sector faces an evolving threat landscape driven by increasing digitalization, sophisticated attackers, and the broad adoption of cloud-native architectures. This paper proposes a comprehensive Cloud‑Native AI Framework designed specifically for secure financial networks that combines DevSecOps practices, continual threat detection using machine learning (ML) and deep learning (DL), and multivariate risk analytics to provide proactive, adaptive defense and compliance capabilities. The framework emphasizes microservices-based architecture, containerization, immutable infrastructure, and policy-as-code to deliver secure, resilient, and auditable deployment pipelines. At its core, an ensemble of real-time anomaly detectors—ranging from lightweight statistical models for latency-sensitive streams to deep graph neural networks for relationship and fraud detection—operates alongside a feature-store-backed risk analytics engine that ingests telemetry, transaction flows, identity signals, and third-party threat intelligence.

The DevSecOps pipeline integrates automated security testing, vulnerability scanning, secrets management, and policy enforcement into CI/CD stages, ensuring that security is shifted left and continuously verified. Risk scoring is produced by multivariate models that combine behavioral, network, financial, and contextual features to produce interpretable risk vectors for downstream decision systems (e.g., block/allow decisions, transaction throttling, investigator alerts). The framework includes feedback loops: analyst-labeled incidents and outcomes feed back into model retraining and policy refinement, enabling continuous improvement and adaptation to adversary evolution.

We evaluate the framework via simulated datasets and a case study deployment in a mid-size banking environment, demonstrating improvements in detection F1-scores, mean time-to-detection (MTTD), and reduction in false positives compared to baseline signature-based and single-model approaches. Additionally, implementation of DevSecOps principles reduced deployment-related misconfigurations and accelerated secure releases without sacrificing compliance. The paper concludes with design recommendations, limitations, and future directions, such as integrating federated learning for cross-institution privacy-preserving collaboration and enhanced model governance for regulatory compliance.