## 7 Conclusions  
The rapid evolution of software development, coupled with the increasing sophistication of cyber threats, demands a fundamental shift in how we approach security. This paper introduced the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D), a comprehensive framework designed to embed security throughout the entire Secure Software and System Development Life Cycle (S-SDLC). AZTRM-D achieves this by unifying DevSecOps methodologies, the NIST Risk Management Framework (RMF),and the Zero Trust (ZT) model, all of which are orchestrated and significantly enhanced by Artificial Intelligence (AI). This framework moves security from a reactive, fragmented approach to a proactive, continuously adaptive, and intelligently automated posture. By integrating security from the very beginning and leveraging AI for real-time threat intelligence, automated vulnerability detection, dynamic policy enforcement, and continuous monitoring, AZTRM-D ensures that security is an intrinsic part of the software, not an afterthought. This holistic integration greatly improves an organization's security posture, reduces its attack surface, simplifies regulatory compliance, and ultimately accelerates the delivery of robust and trustworthy software systems. A key contribution of AZTRM-D is its synergistic combination of established security principles, elevated by AI's ability to drive automation and predictive insights. While this paper has provided a detailed architectural design and practical validation, we acknowledge the framework's limitations. The efficacy of AZTRM-D is fundamentally dependent on the robustness of its AI models against sophisticated attacks, and its generalizability across vastly different software domains requires further study. These considerations directly inform our future research directions. One promising area is rigorous adversarial testing to continuously harden the AI components against evasion techniques. Another involves integrating Post-Quantum Cryptography (PQC) to future-proof the methodology against the emerging threat of quantum computing [77]. We also plan to explore advanced human-AI co-supervision models, ensuring that human expertise remains central to validating the AI's most critical security decisions. Finally, investigating techniques like federated learning could improve the accuracy of AI-driven security measures while preserving data privacy. Exploring these advancements will ensure AZTRM-D remains at the forefront of the ever-changing cybersecurity landscape, including next-generation challenges like confidential computing [78]. Crucially, AZTRM-D also focuses on protecting its own AI features. Even though AI is vital for the framework's efficiency, it must be protected from the very risks it is designed to mitigate. This means applying ZT principles directly to the AI and automation components themselves by implementing strict identity verification, continuous monitoring, and access control measures for all AI processes. Furthermore, AI features in AZTRM-D do not operate with full autonomy. Human operators validate critical security decisions to prevent unintended consequences, a principle central to responsible AI frameworks [75]. This hybrid approach allows AI to optimize and automate routine tasks while human judgment remains central to decisions with significant security implications. Ultimately, AZTRM-D not only integrates AI into the core of cybersecurity practices but also ensures that this integration is continuously refined and safeguarded, as shown conceptually in Figure 12. The methodology's adaptive nature, driven by AI insights and automation, guarantees that the system remains resilient as it evolves. By harmonizing DevSecOps, RMF, and Zero Trust within this framework, AZTRM-D emerges as a critical solution for the multifaceted challenges of cybersecurity in the digital age, representing a significant step towards achieving truly resilient and secure software systems. **Author Contributions:** Conceptualization, I.C. ; methodology, I.C.