The rapid evolution of digital infrastructures and cloud-native technologies has intensified the complexity of cybersecurity challenges, particularly in Continuous Integration/Continuous Deployment (CI/CD) environments and distributed systems. This research provides a comprehensive examination of AI-driven approaches in cybersecurity, emphasizing threat detection, vulnerability management, and post-breach data protection. Through an in-depth synthesis of contemporary studies, the paper explores machine learning algorithms, AI-powered threat intelligence platforms, and predictive security models applied to CI/CD pipelines and telecom networks. The study further evaluates the integration of AI within DevOps frameworks, highlighting the proactive identification and mitigation of sophisticated cyber threats, including adversarial attacks and data poisoning. Methodologically, the paper adopts a qualitative synthesis of prior empirical findings, whitepapers, and emerging AI implementations across diverse platforms such as AWS, Microsoft Azure, and Google Cloud. Findings indicate that AI not only enhances real-time monitoring but also enables predictive insights that strengthen organizational resilience against both known and novel cyber threats. Limitations, including model interpretability, data privacy constraints, and computational overheads, are critically examined. Future directions focus on hybrid AI architectures, the ethical deployment of machine learning in sensitive environments, and the alignment of AI-driven security measures with regulatory frameworks. This work contributes to the growing body of knowledge by providing a theoretically grounded, practice-oriented understanding of AI’s role in modern cybersecurity ecosystems.

AI-driven cybersecurity, CI/CD pipelines, threat detection

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