# AutoGuard: A Self-Healing Proactive Security Layer for DevSecOps Pipelines Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2512.04368 · HTML: https://mlsanity.com/paper/2512.04368/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 5 of 5)

- [Chaos Engineering](https://mlsanity.com/paper/1702.05843/) (2017)
- [UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models](https://mlsanity.com/paper/2505.12345/) (2025)
- [On Evaluating Self-Adaptive and Self-Healing Systems using Chaos Engineering](https://mlsanity.com/paper/2208.13227/) (2022)
- [CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering](https://mlsanity.com/paper/2303.07283/) (2023)
- [Self-Healing Software Systems: Lessons from Nature, Powered by AI](https://mlsanity.com/paper/2504.20093/) (2025)

## Built on by (most cited first, 1 of 1)

- [From Assistance to Agency: Rethinking Autonomy and Control in CI/CD Pipelines](https://mlsanity.com/paper/2605.07062/) (2026)

## Abstract (arXiv)

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