This article presents an integrative theoretical framework for Adaptive DevSecOps that tightly couples threat intelligence, dynamic data management for continuous retraining, privacy-by-design, and containerized CI/CD security to enable automated, risk-aware security decisions before code reaches production. The work synthesizes multiple strands of recent scholarship and practitioner guidance—threat intelligence mapping, blockchain-enabled intelligence lifecycle support, continuous retraining pipelines, DevOps metrics and project alignment, container security techniques, regulatory privacy constraints, and event-consistency tradeoffs in distributed systems—into a single, coherent model for securing modern cloud-native software delivery. The article first identifies gaps in contemporary DevSecOps practice, then defines methodological constructs for integrating automated threat feeds, retraining controls, and privacy-preserving data governance within CI/CD workflows. A detailed, text-based methodology describes architecture patterns, data flows, decision points, and governance controls; results are presented as conceptual outcomes and expected operational benefits, grounded in the referenced literature. The discussion examines theoretical implications, counter-arguments, limitations (including data quality, false positives, and compliance complexity), and avenues for future empirical validation. This contribution intends to guide researchers and practitioners toward provable, auditable, and privacy-respecting automation of security actions in the software delivery lifecycle.

DevSecOps, threat intelligence, continuous retraining

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