Authors : Rakesh Kumar Pal; Tanvi Desai; Jatinder Singh; Harika Rama Tulasi Karatapu

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/4rur6tyz

Scribd : https://tinyurl.com/32fw39er

DOI : https://doi.org/10.38124/ijisrt/25jul1821

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Abstract : The proliferation of multi-cloud and hybrid infrastructures has exponentially expanded the cyber-attack surface, rendering traditional reactive security paradigms obsolete. This paper introduces a novel framework leveraging Federated Agentic AI to establish proactive cyber-resilience across heterogeneous cloud environments (AWS, Azure, GCP, on-prem). Our architecture employs a distributed swarm of autonomous AI agents capable of continuous threat hunting, cross-cloud correlation, autonomous mitigation, and adaptive defense posturing. Key innovations include: 1) A privacy-preserving federated learning system for cross-CSP threat detection; 2) Dynamic response playbooks generated via neuro-symbolic AI; 3) Reinforcement Learning (RL)-driven attack surface reduction; and 4) Mutatable deception environments for post- compromise resilience. Benchmarks against MITRE ATT&CK show a 68% reduction in detection latency and 92% automated containment of ransomware attacks. The framework addresses critical challenges of telemetry fragmentation, policy heterogeneity, and adversarial resilience while ensuring regulatory compliance through embedded XAI and policy- translation engines.

Keywords : Agentic AI, Cyber-Resilience, Multi-Cloud Security, Autonomous Threat Response, Federated Learning, Adaptive Defense, Reinforcement Learning, XAI.

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