A Federated AI-Enabled Enterprise DevSecOps Framework for Autonomous Cloud Security Orchestration and Continuous Compliance
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806038Keywords:
Federated learning, DevSecOps, cloud security, autonomous orchestration, continuous compliance, AI security, policy-as-code, multi-cloud governance, reinforcement learning, cybersecurity automationAbstract
Modern enterprise cloud ecosystems are increasingly distributed, multi-cloud, and edge-integrated, creating significant challenges in maintaining consistent security posture and regulatory compliance. Traditional DevSecOps pipelines, while effective in integrating security into CI/CD workflows, remain largely centralized and reactive, lacking the intelligence and autonomy required for dynamic cloud-native environments. This paper proposes a Federated AI-Enabled Enterprise DevSecOps Framework designed to enable autonomous cloud security orchestration and continuous compliance across heterogeneous infrastructure
The framework integrates federated learning, reinforcement learning-based security automation, and policy-as-code mechanisms to ensure decentralized intelligence sharing without compromising data sovereignty. By embedding AI agents across development, security, and operations layers, the system continuously learns from distributed telemetry, detects anomalies in real time, and autonomously remediates threats. Furthermore, compliance is enforced continuously using machine-readable regulatory policies mapped to dynamic cloud workloads
The proposed model enhances scalability, reduces mean-time-to-detection (MTTD), and improves compliance accuracy in multi-tenant cloud environments. It also addresses critical challenges such as data privacy, cross-border governance, and adversarial threats in AI-driven security systems. This framework represents a shift from reactive DevSecOps pipelines to proactive, self-healing, and federated security ecosystems
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