INTEGRATED ADAPTIVE DEVSECOPS FOR FINANCIAL AND CRITICAL INFRASTRUCTURE: LEVERAGING EXPLAINABLE AI, THREAT INTELLIGENCE, AND MODEL RISK MANAGEMENT FOR ENHANCED RISK AWARENESS

Abstract

This article proposes a unified, adaptive framework for integrating model risk management, explainable machine learning, and threat intelligence into DevSecOps pipelines for financial services and critical-infrastructure domains. The framework is motivated by the rapid adoption of AI/ML in financial services, the unique model governance challenges that arise from automated decisioning, and the operational pressures of continuous delivery (Financial Stability Board, 2017; Bennett, 2017). We synthesize multicriteria decision methods, model-risk governance principles, and contemporary DevSecOps practices to develop a practical, theoretically grounded process that prioritizes model interpretability, data privacy, and automated pre-deployment risk mitigation (Alimohammadlou & Bonyani, 2017; Holzinger et al., 2018; Díaz et al., 2019). The methodology emphasizes (1) continuous model validation and governance controls that sit within CI/CD and IaC workflows (Bennett, 2017; Crespo et al., 2017); (2) combining MCDM approaches with explainable AI to make trade-offs transparent for stakeholders (De Almeida et al., 2017; Alimohammadlou & Bonyani, 2017); and (3) enriched pipeline automation incorporating threat intelligence to stop high-risk artifacts prior to production deployment (Díaz et al., 2019; Malik, 2025). We present a descriptive results section that outlines expected outcomes, detection and mitigation pathways, and organizational enablers, and we discuss limitations, governance implications, and future research directions. The proposed approach is intended to be actionable for risk and engineering leads in banks, energy markets, and other AI-driven operation environments while also contributing to academic discourse on aligning model risk, cybersecurity, and rapid software delivery lifecycles.