Abstract

Modern software-intensive critical infrastructure is increasingly governed by continuous delivery, distributed architectures, and AI-augmented decision-making. Yet reliability assurance, agile governance, and cyber resilience are still commonly treated as separate disciplines, leading to fragmented controls, slow incident learning cycles, and inconsistent risk posture across the lifecycle. This paper proposes a unified, scalable AI framework that (i) predicts software reliability degradation using multi-source engineering telemetry, (ii) optimizes agile governance using decision intelligence to balance value, risk, and technical debt, and (iii) operationalizes cyber resilience for smart infrastructure through threat-informed control alignment and continuous validation. The framework integrates a reliability prediction plane, a governance optimization plane, and a cyber-resilience plane over a shared data foundation and MLOps guardrails. We formalize key objective functions, describe system components and interfaces, and provide an evaluation blueprint using reliability, delivery, and security outcome metrics.