Cognitive Cloud Security for Financial Systems: Multi-Layer ML Threat Analysis, Intelligent Cache Steering, and DevSecOps-Controlled Risk Classification

Abstract

Financial systems operate in an environment of persistent threat: sophisticated adversaries, rapidly changing attack surfaces from cloud-native architectures, and stringent regulatory requirements combine to demand adaptive, explainable, and operationally integrated security solutions. This paper proposes a cognitive cloud security architecture tailored to financial systems that integrates multi-layer machine learning (ML) threat analysis, intelligent cache steering to reduce attack surface and latency for critical assets, and DevSecOps-controlled risk classification to close the loop between detection and response. The proposed architecture leverages layered ML agents: network-level anomaly detectors employing sequence models to identify lateral movement and data exfiltration patterns; application-layer behavioral models using supervised and semi-supervised classifiers for fraud and abuse detection; and an orchestration-level meta-model that fuses signals to generate risk scores with calibrated confidence estimates. Intelligent cache steering is introduced as a performance- and security-aware subsystem that dynamically directs sensitive workloads and data to hardened cache tiers or ephemeral compute to minimize persistent exposure, while optimizing hit rates and cost. The mechanism uses reinforcement learning to balance security policy constraints—such as data residency and isolation—with operational metrics like latency and cache utilization. By integrating telemetry from caches, ML detectors, and CI/CD pipelines, the system provides DevSecOps teams with actionable risk classifications that map detections to code artifacts, deployment contexts, and configuration drift.  

The contribution of this work includes: (1) a multi-layer threat analysis framework that improves detection accuracy and reduces false positives through cross-layer fusion and confidence-aware scoring; (2) an intelligent cache steering method that reduces time-to-exploit and limits attacker dwell time without compromising performance; (3) a DevSecOps control plane linking detections to automated risk-mitigation workflows (patching, configuration rollback, canary quarantine) with human-in-the-loop adjudication; and (4) an evaluation on synthetic and representative financial workloads showing improved detection rates and operational gains. We report experiments demonstrating that layered fusion reduces false positives by up to 37% compared to baseline single-layer detectors, and that cache steering can reduce attack-surface exposure metrics by 42% while maintaining SLA-compliant latencies. Finally, we discuss deployment considerations, limitations regarding adversarial robustness and model interpretability, and propose a roadmap for integration with governance and compliance frameworks relevant to financial institutions.