A Federated Cloud Security Framework for Financial Networks Multi-Source Threat Correlation, Adaptive Caching, DevSecOps Automation, and ERP Strategy Planning

Abstract

Financial networks (banks, payment processors, exchanges) face increasingly sophisticated, distributed cyberthreats that exploit data silos, regulatory constraints, and heterogeneous infrastructure. This paper presents a holistic architecture that combines federated machine learning (FL), multi-source threat correlation, adaptive caching, and DevSecOps pipeline enforcement to provide real-time, privacy-preserving threat detection and resilient cloud defense for financial infrastructures. The proposed architecture is organized into four collaborating layers: (1) federated learning orchestration, enabling participating institutions to collaboratively train detection and classification models without exposing raw transaction or logs; (2) multi-source threat correlation, which ingests and normalizes CTI (Structured Threat Information eXpression — STIX/MAEC) and telemetry from network, host, application, and partner feeds to produce enriched, normalized events for model consumption; (3) adaptive caching and edge data plane, which uses cost-aware, workload-adaptive caching to accelerate lookups, reduce latency, and limit data exposure when model scoring and enrichment are required; and (4) DevSecOps enforcement, which automates secure model deployment, policy-driven configuration, and continuous compliance checks in CI/CD pipelines to reduce drift and enforce security posture across clouds and hybrid environments.
 

Our design leverages the FL paradigm to keep sensitive financial records onsite while enabling cross-institution learning (reducing centralization risks and supporting regulatory constraints). We integrate established privacy primitives — secure aggregation, per-client differential privacy and encrypted transport — to mitigate gradient and model inversion attacks and to protect participant contributions during aggregation. To unify heterogeneous CTI, we adopt standardized schemas (e.g., STIX/MAEC) and a correlation engine that supports temporal, behavioral and entity-centric linking. Adaptive caching at the edge employs workload-aware replacement policies and ML-assisted replacement selection so that the system provides sub-second enrichment for scoring while minimizing stale or over-exposed cached artifacts. DevSecOps integration shifts security “left” into build and deploy stages, embedding SAST/SCA/DAST, secrets management, model governance checks, and runtime policy enforcement into the CI/CD pipeline to ensure that model updates and configuration changes maintain compliance and security baselines.We prototype the architecture in a multi-bank testbed and evaluate (a) model convergence and utility under non-IID, heterogenous client data, (b) privacy budgets and accuracy trade-offs when applying client-level differential privacy and secure aggregation, (c) reduction in detection latency via adaptive caching compared to baseline LRU caches, and (d) the effectiveness of DevSecOps pipeline policy checks in preventing misconfiguration and insecure model deployments. Results show FL can achieve near-centralized accuracy with proper aggregation and DP tuning; adaptive caching yields significant latency and backend load reductions for common enrichment workloads; and CI/CD enforcement prevents a class of misconfigurations that could otherwise expose models or keys. We close with architectural recommendations, deployment considerations for regulated financial environments, and research directions to harden FL against poisoning and back-door attacks while preserving regulatory auditability and model explainability.