Multivariate AI Cloud Security and DevSecOps for Financial Processes and Privacy-Preserving Advertising Metrics

Abstract

The rapid adoption of cloud computing in financial business processes has significantly increased the complexity and scale of cybersecurity threats. Traditional rule-based security mechanisms are often insufficient to detect sophisticated fraud patterns and advanced persistent threats operating across distributed cloud environments. This paper proposes a multivariate AI-driven cloud security framework that integrates machine learning–based anomaly detection, behavioral analytics, and real-time threat intelligence within a DevSecOps pipeline. The framework leverages multivariate data streams—including transaction logs, network telemetry, user behavior, and application performance metrics—to proactively identify fraud, insider threats, and security breaches across financial workflows. By embedding automated security controls into continuous integration and continuous deployment (CI/CD) processes, the proposed approach enables continuous risk assessment, faster incident response, and adaptive security policy enforcement. Experimental analysis demonstrates improved detection accuracy, reduced false positives, and enhanced resilience of financial systems against evolving cyber threats in cloud-native environments.