Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning (2505.08837v1)

Abstract: The security of cloud environments, such as Amazon Web Services (AWS), is complex and dynamic. Static security policies have become inadequate as threats evolve and cloud resources exhibit elasticity [1]. This paper addresses the limitations of static policies by proposing a security policy management framework that uses reinforcement learning (RL) to adapt dynamically. Specifically, we employ deep reinforcement learning algorithms, including deep Q Networks and proximal policy optimization, enabling the learning and continuous adjustment of controls such as firewall rules and Identity and Access Management (IAM) policies. The proposed RL based solution leverages cloud telemetry data (AWS Cloud Trail logs, network traffic data, threat intelligence feeds) to continuously refine security policies, maximizing threat mitigation, and compliance while minimizing resource impact. Experimental results demonstrate that our adaptive RL based framework significantly outperforms static policies, achieving higher intrusion detection rates (92% compared to 82% for static policies) and substantially reducing incident detection and response times by 58%. In addition, it maintains high conformity with security requirements and efficient resource usage. These findings validate the effectiveness of adaptive reinforcement learning approaches in improving cloud security policy management.

Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning

The paper "Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning" addresses a significant issue in contemporary cloud security management: the limitations of static security policies amidst dynamic and evolving cyber threats. In particular, the research explores how reinforcement learning (RL)—specifically deep reinforcement learning algorithms like Deep Q Networks (DQN) and Proximal Policy Optimization (PPO)—can be employed to adaptively manage security policies within cloud frameworks like Amazon Web Services (AWS).

Overview and Motivation

Cloud environments, due to their elastic nature and the dynamic behavior of workloads, require security policies that can adapt quickly to new threats and changes in configurations. Static rule-based systems are not sufficient in this context, as they can become outdated when faced with novel attack patterns or when scaling resources leads to over privileged IAM roles. Consequently, the paper proposes an RL-based framework to autonomously adapt security policies in real-time, aiming to maximize threat detection and compliance while minimizing resource usage.

Methodology

The authors devised an RL agent to interact directly with the cloud environment, continuously analyzing security events and adapting policies accordingly. The RL framework was built upon the AWS cloud, employing AWS APIs to automate firewall rules and IAM policy updates based on telemetry data from AWS CloudTrail logs, network traffic data, and threat intelligence feeds. The RL problem is structured as a Markov Decision Process (MDP), where the state includes the current security posture and recent events, and the action space comprises possible security policy adjustments.

Experiments were conducted with a testbed utilizing both real AWS log data and attack scenarios from publicly available datasets like CICIDS2017 and CSE-CIC-IDS2018. Using this setup, the RL agent was trained with a mix of simulated offline attacks and live deployments. The paper outlines the architecture of the system, detailing components like data ingestion, feature extraction, RL agent design, policy management, and response execution engine.

Results

The experimental evaluation demonstrated that the RL-based framework surpassed static policies in terms of intrusion detection rates (92% versus 82%) and reduced incident detection and response times by 58%. Moreover, the RL agent managed to maintain high compliance and resource efficiency, confirming the potential of RL in cloud security management.

Through careful crafting of the reward function—balancing threat mitigation rewards with compliance penalties—the RL agent learned to optimize actions that improve cloud security outcomes. The results show DQN and PPO as highly promising methods; however, PPO delivered slightly better success rates and stability.

Implications and Future Directions

Practically, this research suggests that RL can significantly enhance cloud security management by automating the response to threats, reducing the reliance on manual policy adjustments. The integration of RL with real-time security operations proposes a shift from static configurations to dynamic adaptation—aligning with agile DevOps practices.

Theoretically, the paper adds to existing literature by advancing the application of RL in cybersecurity beyond simulated environments to real cloud platforms, specifically AWS. The paper also discusses the challenges of scalability, adversarial risks, and compliance constraints inherent to RL systems, outlining possible future developments such as federated learning for collaborative security improvement, multi-agent systems for enhanced strategic defense, and explainable RL models to aid regulatory audits.

Conclusion

The exploration of reinforcement learning as a tool for adaptive security policy management in cloud environments represents a significant step forward in managing complex cybersecurity landscapes. By demonstrating the viability of RL agents in real AWS environments, this paper not only highlights their applicability but also sets groundwork for further research into scalable, robust, and compliant adaptive security mechanisms across multiple cloud platforms.