Designing Generative AI Driven Cloud Native Platforms for Autonomous Threat Detection and Intelligent DevSecOps
DOI:
https://doi.org/10.64235/edq1yk31Keywords:
Generative AI, Cloud-Native Security, DevSecOps, Autonomous Threat Detection, Self-Healing Infrastructure, Microservices Security, Continuous Compliance, Retrieval-Augmented GenerationAbstract
The rapid evolution of cloud-native architectures—characterized by microservices, containerization, and dynamic orchestration—has introduced unprecedented operational scale alongside complex security vulnerabilities. Traditional reactive security paradigms fail to keep pace with modern attack vectors that exploit ephemeral infrastructure and continuous deployment pipelines. This research proposes an integrated architecture for designing Generative AI (GenAI)-driven cloud-native platforms tailored for autonomous threat detection and intelligent DevSecOps workflows. By synthesizing generative foundational models, natural language interfaces, and retrieval-augmented generation (RAG) with cloud-native monitoring stacks, the proposed framework automates real-time anomaly detection, incident response, and continuous compliance. Generative AI models analyze multi-modal telemetry—including system logs, network traces, and infrastructure-as-code (IaC) templates—to synthesize context-aware threat intelligence, predict attack vectors, and autonomously generate self-healing remediation scripts. Furthermore, the platform embeds intelligent security guardrails directly into CI/CD pipelines, transforming DevSecOps into an adaptive, autonomous ecosystem. Empirical evaluations demonstrate that the GenAI-driven approach reduces Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) by up to 70% while drastically decreasing false-positive alerts compared to traditional signature-based systems. This study establishes a roadmap for building resilient, self-defending cloud infrastructure capable of mitigating emerging cyber threats autonomously.
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