Agentic AI for Proactive Cyber-Resilience in Multi-Cloud Environments: Autonomous Threat Detection, Response, and Adaptive Defense Posturing

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.