End-to-End AI-Driven DevSecOps: A Framework for Risk-Aware Testing, Monitoring, and Lifecycle Optimization
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I2P116Keywords:
Devsecops, Software Defect Prediction, AI-Driven Testing, Observability, Risk-Aware Quality Assurance, Continuous Security Testing, Lifecycle Optimization, Secure Software EngineeringAbstract
Modern software delivery must simultaneously accelerate release cadence, strengthen security assurance, improve test efficiency, and sustain dependable operations in highly distributed environments. Yet many organizations still manage testing, security, observability, and post-release optimization as loosely connected activities, creating fragmented feedback loops and delayed risk response. This paper proposes an end-to-end AI-driven DevSecOps framework that unifies defect prediction, threat-informed testing, continuous security gating, telemetry-driven monitoring, and lifecycle optimization within a single risk-aware control architecture. The framework is grounded in recent literature on DevSecOps adoption, machine learning for software testing, software defect prediction, secure microservices, and production observability [1][2]. Its central contribution is a layered decision model that fuses code-level signals, service dependency context, runtime anomalies, vulnerability intelligence, and business criticality to prioritize actions across planning, build, release, and operations. Rather than treating testing and monitoring as isolated checkpoints, the proposed approach closes the loop from operational evidence back to backlog refinement, test generation, policy tuning, and architectural remediation. The paper further defines governance requirements, quantitative scoring logic, and an evaluation blueprint for enterprise deployment scenarios. By integrating predictive analytics with secure delivery controls and observability-informed adaptation, the framework provides a practical foundation for improving release confidence, reducing mean time to detection, and allocating engineering effort where technical and business risk are highest.
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