Autonomous AI Agents for Cybersecurity Threat Detection and Response: A Multi-Agent Architecture Framework Using AWS Frontier Agents

Abstract

The rapid proliferation of sophisticated cyber threats has outpaced the capabilities of traditional, rule-based security systems, necessitating a paradigm shift toward autonomous, AI-driven defenses. This paper presents a comprehensive multi-agent architecture framework for autonomous cybersecurity threat detection and response, leveraging AWS Frontier Agents as the primary operational backbone. Specifically, we introduce a coordinated system comprising the AWS Security Agent, Amazon Bedrock Agents for orchestration, and Amazon GuardDuty for machine-learning-based threat intelligence. The proposed framework enables autonomous operation over extended time horizons (hours to days) without human intervention, performing complex reasoning chains, automated penetration testing, threat hunting, and dynamic incident response. We describe the system architecture, agent coordination protocols, and integration pathways with existing security operations centers (SOCs). Experimental evaluation across simulated enterprise threat scenarios demonstrates a 94.7% threat detection accuracy, a 78% reduction in mean time to respond (MTTR), and a 63% decrease in false positive alerts compared to conventional signature-based intrusion detection systems. Our results indicate that frontier agent architectures offer a transformative approach to modern cybersecurity operations, significantly augmenting human analyst capacity while maintaining robust governance and auditability. This work contributes a reference architecture, empirical benchmarks, and governance guidelines applicable to enterprise-scale deployments.