A multi-agent GenAI architecture is introduced to support the automation of ethical cloud security, solving the problem of scalability or adaptability, and compliance in dynamic cloud environments. This framework combines dedicated generative agents such as policy analysts, threat detectors, remediation organizers, and auditor agents which interact via common knowledge graph and can be explained by a decision log. The agents utilize context-based prompt generation, generation constraints, provenance management, and generation to generate security policies, anomaly detection, provide automated mitigation, and maintain human-in-the-loop control. Some of the ethical protections are bias audits, privacy-sensitive learning, least-privilege enforcement, and policy verifiability, to warrant the correctness of the decisions taken in compliance with regulatory or organizational limits. Testing with representative cloud work lines shows that there is shorter energy on discerning and correcting occurrences, elevated coverage of controls and signs when the automated actions are traced. The framework enables adjustable levels of trust and escalation measures to accommodate the autonomy versus governance. The method fosters usage of GenAI to deal with cloud security by offering modular agents, verifiable ethics controls, metrics-based assessment, hence fostering responsible automation that is also transparent, auditable, and considers changing threats.
Laxminarayana Thirupathi, Sharanya Gattu, T. Wable et al.· 2026 International Conferenc...· 0 citations
Cloud computing has emerged as an important core to the contemporary digital services, facilitating scalable, on demand provisioning of resources across a variety of application fields. Nevertheless, this multi-tenant and dynamic environment of clouds and the amplified attack surface make the detection of intrusions through reliable methods a consistent issue that cloud security systems struggle with. The proposed work is a Generative Adversarial Network (GAN)-based hardening framework of cloud intrusion detection systems, targeting better resilience to changing and low-rate cyberattacks. The methodology combines a conditional generator which is used to generate realistic cloud-specific attack traffic, a discriminator used to refine the adversarial traffic, as well as a co-trained intrusion classifier trained on both clean and synthetic data in a closed-loop way. The feature-aware regularization is introduced to maintain the statistical consistency of network traffic, and optimize the attack diversity. The proposed approach is proved to yield better results in comparison with signature-based, machine learning, deep learning, and adversarial ML-based IDS models by experimental assessment. Significant gains in the accuracy of identifying, the ability to recall, stability, and minimizing errors are also noticed with quantifiable increases observed in all evaluation measures. These findings represent the usefulness of adversarial data-driven learning to develop robust, adaptive, and future-ready cloud intrusion detection systems.
T. Divya, Sheik Saidhbi, S. Umarani et al.· 2026 International Conferenc...· 0 citations