Contemporary DevSecOps pipelines have to deal with the evolution of security in an ever-continuously integrated and deployed environment. Existing methods,such as rule-based intrusion detection and static vulnerability scanning, are inadequate and unreceptive to changes in the system, causing longer response times and organization needs exposure to emerging attack vectors. In light of the previous constraints, we introduce AutoGuard to the DevSecOps ecosystem, a reinforcement learning (RL)-powered self-healing security framework built to pre-emptively protect DevSecOps environments. AutoGuard is a self-securing security environment that continuously observes pipeline activities for potential anomalies while preemptively remediating the environment. The model observes and reacts based on a policy that is continually learned dynamically over time. The RL agent improves each action over time through reward-based learning aimed at improving the agent's ability to prevent, detect and respond to a security incident in real-time. Testing using simulated ContinuousIntegration / Continuous Deployment (CI/CD) environments showed AutoGuard to successfully improve threat detection accuracy by 22%, reduce mean time torecovery (MTTR) for incidents by 38% and increase overall resilience to incidents as compared to traditional methods.
Keywords- DevSecOps, Reinforcement Learning, Self- Healing Security, Continuous Integration, Automated Threat Mitigation
The management of data in a cyber-secure way has emerged as one of the key challenges of an age of distributed computing, propelled by the continued emergence of Multi-Access Edge Computing (MEC) environments. MEC enables low-latency, high-bandwidth, and contextually aware services on account of the proximity of computation and storage to users. However, the decentralization of the MEC infrastructure creates a scenario that introduces more sever issues for established digital data backup and retrieval systems to ensure security issues related to unauthorized access, data breaches, latency in getting data back, and systems failure [1]. This introduces questions not just regarding the confidentiality of mission critical data, but also the availability of that data, and increased risk to scalability and reliability to MEC based services [2][3][4]. The aim is to balance both a solid security posture and system efficiency in the situations surrounding resource constraints and dynamic resource management for shifting work loads . Established cloud based protection of data are poorly effective in adapting to real-time demands of the MEC system environment, as they rely on centralized servers, will generally be less efficient in utilizing resources, and will be vulnerable to hacking threats [5][6][7]. Despite this promise, few researchers have endeavored to directly utilize CNNs for MEC-based backup and retrieval. By combining deep CNN models with optimization-based algorithms, it may be possible to provide dynamic task allocation, adaptive encryption, and latency-aware recovery capabilities [8][9].
The major research contributions of this paper are:
• A unified CFTO framework that combines CanGaroo Fetch Trianomy Optimization with deep CNNs to improve the efficiency and security of data backup/retrieval in MEC environments.
• A robust optimization mechanism that dynamically allocates storage resources, schedules backup tasks, and manages retrieval paths under adversarial and resourceconstrained conditions.
The remainder of this paper is organized as follows: Section 2 reviews related work on MEC security, backup systems, and optimization-based deep learning methods. Section 3 presents the proposed CFTO framework and its architectural design. Section 4 details the experimental setup and performance evaluation metrics. Section 5 Conclusion..
The integration of self-healing mechanisms and reinforcement learning (RL) into DevSecOps has recently emerged as a vital research direction, aiming to achieve proactive, autonomous, and adaptive security in continuous integration and deployment (CI/CD) pipelines. introduced the CHESS framework, a comprehensive evaluation approach for self-adaptive systems through chaos engineering principles. Their framework systematically injects controlled faults to test resilience and adaptive behavior under stress conditions. This research laid the groundwork for assessing the robustness of adaptive software systems, providing a methodological foundation that can be extended toward security self-healing. However, CHESS primarily focused on performance and system availability, not on security-specific threats, leaving room for frameworks like AutoGuard to integrate reinforcement learning for dynamic vulnerability mitigation. In a related study, explored self-adaptive and selfhealing systems using chaos engineering to measure their recovery capabilities. They demonstrated how fault injection could simulate attack-like conditions, helping systems autonomously restore stability. extended the notion of self-healing systems by drawing inspiration from biological processes. Their AI-driven model emulated natural healing mechanisms to detect and correct software anomalies autonomously. explored the use of AI in DevSecOps integration, emphasizing automation of security testing and compliance checks in use throughout the DevOps lifecycle. Their model utilized a machine learning algorithm to dynamically detect vulnerabilities, contributing to secure automation practices. However, their approach did not include self-healing or real-time reinforcement learning to adapt to new threat vectors.
AutoGuard addresses this gap by proposing and developing a proactive RL framework to independently detect, mitigate, and recover from vulnerabilities in evolving software eat ecosystems.
AutoGuard is designed as a layered, modular security layer that augments existing CI/CD workflows with continuous monitoring, decisionmaking, and autonomous remediation. The CI/CD Environment contains build servers (e.g., Jenkins, GitLab CI), container registries, and orchestration platforms (Kubernetes) where artifacts are built, tested, and deployed. The Security Monitor passively and actively collects telemetry-logs, metrics, SBOM (software bill of materials), vulnerability scanner outputs, container runtime data, and network flows-normalizing events into a feature vector for the RL engine. where V(t) = vulnerability signa
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